diff --git a/week8/community_contributions/Ensemble_with_xgboost/Build _UI.ipynb b/week8/community_contributions/Ensemble_with_xgboost/Build _UI.ipynb
new file mode 100644
index 0000000..9c3aba0
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/Build _UI.ipynb	
@@ -0,0 +1,181 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a71ed017-e1b0-4299-88b3-f0eb05adc4df",
+   "metadata": {},
+   "source": [
+    "# Build UI\n",
+    "\n",
+    "We will use more advanced aspects of Gradio - building piece by piece."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "614c6202-4575-448d-98ee-78b735775d2b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "from deal_agent_framework import DealAgentFramework\n",
+    "from agents.deals import Opportunity, Deal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0534e714-5a9c-45c6-998c-3472ac0bb8b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with gr.Blocks(title=\"Deal Intel\", fill_width=True) as ui:\n",
+    "\n",
+    "    with gr.Row():\n",
+    "        gr.Markdown('<div style=\"text-align: center;font-size:24px\">Deal Intel - Deal Hunting Agentic AI</div>')\n",
+    "    with gr.Row():\n",
+    "        gr.Markdown('<div style=\"text-align: center;font-size:14px\">Autonomous agent framework that finds online deals, collaborating with a proprietary fine-tuned LLM deployed on Modal, and a RAG pipeline with a frontier model and Chroma.</div>')\n",
+    "        \n",
+    "\n",
+    "ui.launch(inbrowser=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18c12c10-750c-4da3-8df5-f2bc3393f9e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Updated to change from height to max_height due to change in Gradio v5\n",
+    "# With much thanks to student Ed B. for raising this\n",
+    "\n",
+    "with gr.Blocks(title=\"Deal Intel\", fill_width=True) as ui:\n",
+    "\n",
+    "    initial_deal = Deal(product_description=\"Example description\", price=100.0, url=\"https://cnn.com\")\n",
+    "    initial_opportunity = Opportunity(deal=initial_deal, estimate=200.0, discount=100.0)\n",
+    "    opportunities = gr.State([initial_opportunity])\n",
+    "\n",
+    "    def get_table(opps):\n",
+    "        return [[opp.deal.product_description, opp.deal.price, opp.estimate, opp.discount, opp.deal.url] for opp in opps]\n",
+    "\n",
+    "    with gr.Row():\n",
+    "        gr.Markdown('<div style=\"text-align: center;font-size:24px\">\"Deal Intel\" - Deal Hunting Agentic AI</div>')\n",
+    "    with gr.Row():\n",
+    "        gr.Markdown('<div style=\"text-align: center;font-size:14px\">Deals surfaced so far:</div>')\n",
+    "    with gr.Row():\n",
+    "        opportunities_dataframe = gr.Dataframe(\n",
+    "            headers=[\"Description\", \"Price\", \"Estimate\", \"Discount\", \"URL\"],\n",
+    "            wrap=True,\n",
+    "            column_widths=[4, 1, 1, 1, 2],\n",
+    "            row_count=10,\n",
+    "            col_count=5,\n",
+    "            max_height=400,\n",
+    "        )\n",
+    "\n",
+    "    ui.load(get_table, inputs=[opportunities], outputs=[opportunities_dataframe])\n",
+    "\n",
+    "ui.launch(inbrowser=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87106328-a17a-447e-90b9-c547613468da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_framework = DealAgentFramework()\n",
+    "agent_framework.init_agents_as_needed()\n",
+    "\n",
+    "with gr.Blocks(title=\"Deal Intel\", fill_width=True) as ui:\n",
+    "\n",
+    "    initial_deal = Deal(product_description=\"Example description\", price=100.0, url=\"https://cnn.com\")\n",
+    "    initial_opportunity = Opportunity(deal=initial_deal, estimate=200.0, discount=100.0)\n",
+    "    opportunities = gr.State([initial_opportunity])\n",
+    "\n",
+    "    def get_table(opps):\n",
+    "        return [[opp.deal.product_description, opp.deal.price, opp.estimate, opp.discount, opp.deal.url] for opp in opps]\n",
+    "\n",
+    "    def do_select(opportunities, selected_index: gr.SelectData):\n",
+    "        row = selected_index.index[0]\n",
+    "        opportunity = opportunities[row]\n",
+    "        agent_framework.planner.messenger.alert(opportunity)\n",
+    "\n",
+    "    with gr.Row():\n",
+    "        gr.Markdown('<div style=\"text-align: center;font-size:24px\">\"Deal Intel\" - Deal Hunting Agentic AI</div>')\n",
+    "    with gr.Row():\n",
+    "        gr.Markdown('<div style=\"text-align: center;font-size:14px\">Deals surfaced so far:</div>')\n",
+    "    with gr.Row():\n",
+    "        opportunities_dataframe = gr.Dataframe(\n",
+    "            headers=[\"Description\", \"Price\", \"Estimate\", \"Discount\", \"URL\"],\n",
+    "            wrap=True,\n",
+    "            column_widths=[4, 1, 1, 1, 2],\n",
+    "            row_count=10,\n",
+    "            col_count=5,\n",
+    "            max_height=400,\n",
+    "        )\n",
+    "\n",
+    "    ui.load(get_table, inputs=[opportunities], outputs=[opportunities_dataframe])\n",
+    "    opportunities_dataframe.select(do_select, inputs=[opportunities], outputs=[])\n",
+    "\n",
+    "ui.launch(inbrowser=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48506465-1c7a-433f-a665-b277a8b4665c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python price_is_right_final.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9dd0a27-7d46-4c9e-bbe4-a61c9c899c99",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1504cb8-7bf7-4dc4-9b1a-eaba79404aac",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ed84afd-4a04-43d6-8a3b-5143deaf96b2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/week8/community_contributions/Ensemble_with_xgboost/Build_Messaging_Planning_Agent.ipynb b/week8/community_contributions/Ensemble_with_xgboost/Build_Messaging_Planning_Agent.ipynb
new file mode 100644
index 0000000..649a77d
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/Build_Messaging_Planning_Agent.ipynb
@@ -0,0 +1,119 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "23f53670-1a73-46ba-a754-4a497e8e0e64",
+   "metadata": {},
+   "source": [
+    "# Messaging Agent and Planning Agent\n",
+    "\n",
+    "Then we'll put it all together into an Agent Framework."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80d683d9-9e92-44ae-af87-a413ca84db21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dotenv import load_dotenv\n",
+    "from agents.messaging_agent import MessagingAgent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ba769cc-5301-4810-b01f-cab584cfb3b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load_dotenv(override=True)\n",
+    "DB = \"products_vectorstore\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e05cc427-3d2c-4792-ade1-d356f95a82a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = MessagingAgent()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ec518f5-dae4-44b1-a185-d7eaf853ec00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent.push(\"MASSIVE NEWS!!!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57b3a014-0b15-425a-a29b-6fefc5006dee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import chromadb\n",
+    "DB = \"products_vectorstore\"\n",
+    "client = chromadb.PersistentClient(path=DB)\n",
+    "collection = client.get_or_create_collection('products')\n",
+    "from agents.planning_agent import PlanningAgent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5c31c39-e357-446e-9cec-b4775c298941",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "planner = PlanningAgent(collection)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9ac771b-ea12-41c0-a7ce-05f12e27ad9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "planner.plan()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d91ac0bb-738e-4be5-9074-d583190b1e2a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/week8/community_contributions/Ensemble_with_xgboost/Build_RAG_Frontier_Agent.ipynb b/week8/community_contributions/Ensemble_with_xgboost/Build_RAG_Frontier_Agent.ipynb
new file mode 100644
index 0000000..69c6f58
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/Build_RAG_Frontier_Agent.ipynb
@@ -0,0 +1,342 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbcdfea8-7241-46d7-a771-c0381a3e7063",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import os\n",
+    "import re\n",
+    "import math\n",
+    "import json\n",
+    "from tqdm import tqdm\n",
+    "import random\n",
+    "from dotenv import load_dotenv\n",
+    "from huggingface_hub import login\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "from openai import OpenAI\n",
+    "from sentence_transformers import SentenceTransformer\n",
+    "from datasets import load_dataset\n",
+    "import chromadb\n",
+    "from items import Item\n",
+    "from testing import Tester"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98666e73-938e-469d-8987-e6e55ba5e034",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# environment\n",
+    "load_dotenv(override=True)\n",
+    "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
+    "os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN', 'your-key-if-not-using-env')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a25a5cf-8f6c-4b5d-ad98-fdd096f5adf8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "openai = OpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc696493-0b6f-48aa-9fa8-b1ae0ecaf3cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load in the test pickle file\n",
+    "with open('test.pkl', 'rb') as file:\n",
+    "    test = pickle.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33d38a06-0c0d-4e96-94d1-35ee183416ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_context(similars, prices):\n",
+    "    message = \"To provide some context, here are some other items that might be similar to the item you need to estimate.\\n\\n\"\n",
+    "    for similar, price in zip(similars, prices):\n",
+    "        message += f\"Potentially related product:\\n{similar}\\nPrice is ${price:.2f}\\n\\n\"\n",
+    "    return message"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61f203b7-63b6-48ed-869b-e393b5bfcad3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def messages_for(item, similars, prices):\n",
+    "    system_message = \"You estimate prices of items. Reply only with the price, no explanation. Price is always below $1000.\"\n",
+    "    user_prompt = make_context(similars, prices)\n",
+    "    user_prompt += \"And now the question for you:\\n\\n\"\n",
+    "    user_prompt += item.test_prompt().replace(\" to the nearest dollar\",\"\").replace(\"\\n\\nPrice is $\",\"\")\n",
+    "    return [\n",
+    "        {\"role\": \"system\", \"content\": system_message},\n",
+    "        {\"role\": \"user\", \"content\": user_prompt},\n",
+    "        {\"role\": \"assistant\", \"content\": \"Price is $\"}\n",
+    "    ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b26f405d-6e1f-4caa-b97f-1f62cd9d1ebc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DB = \"products_vectorstore\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d26a1104-cd11-4361-ab25-85fb576e0582",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = chromadb.PersistentClient(path=DB)\n",
+    "collection = client.get_or_create_collection('products')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e339760-96d8-4485-bec7-43fadcd30c4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def description(item):\n",
+    "    text = item.prompt.replace(\"How much does this cost to the nearest dollar?\\n\\n\", \"\")\n",
+    "    return text.split(\"\\n\\nPrice is $\")[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f759bd2-7a7e-4c1a-80a0-e12470feca89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e44dbd25-fb95-4b6b-bbbb-8da5fc817105",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def vector(item):\n",
+    "    return model.encode([description(item)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffd5ee47-db5d-4263-b0d9-80d568c91341",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_similars(item):\n",
+    "    results = collection.query(query_embeddings=vector(item).astype(float).tolist(), n_results=5)\n",
+    "    documents = results['documents'][0][:]\n",
+    "    prices = [m['price'] for m in results['metadatas'][0][:]]\n",
+    "    return documents, prices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f7b9ff9-fd90-4627-bb17-7c2f7bbd21f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(test[1].prompt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff1b2659-cc6b-47aa-a797-dd1cd3d1d6c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "documents, prices = find_similars(test[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24756d4d-edac-41ce-bb80-c3b6f1cea7ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(make_context(documents, prices))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b81eca2-0b58-4fe8-9dd6-47f13ba5f8ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(messages_for(test[1], documents, prices))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d11f1c8d-7480-4d64-a274-b030d701f1b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_price(s):\n",
+    "    s = s.replace('$','').replace(',','')\n",
+    "    match = re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", s)\n",
+    "    return float(match.group()) if match else 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06743833-c362-47f8-b02a-139be2cd52ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_price(\"The price for this is $99.99\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a919cf7d-b3d3-4968-8c96-54a0da0b0219",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The function for gpt-4o-mini\n",
+    "\n",
+    "def gpt_4o_mini_rag(item):\n",
+    "    documents, prices = find_similars(item)\n",
+    "    response = openai.chat.completions.create(\n",
+    "        model=\"gpt-4o-mini\", \n",
+    "        messages=messages_for(item, documents, prices),\n",
+    "        seed=42,\n",
+    "        max_tokens=5\n",
+    "    )\n",
+    "    reply = response.choices[0].message.content\n",
+    "    return get_price(reply)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b42e1b9-eaa0-4b45-a847-e8932367f596",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The function for gpt-4.1\n",
+    "\n",
+    "# def gpt_4_1_rag(item):\n",
+    "#     documents, prices = find_similars(item)\n",
+    "#     response = openai.chat.completions.create(\n",
+    "#         model=\"gpt-4.1\", \n",
+    "#         messages=messages_for(item, documents, prices),\n",
+    "#         seed=42,\n",
+    "#         max_tokens=5\n",
+    "#     )\n",
+    "#     reply = response.choices[0].message.content\n",
+    "#     return get_price(reply)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e519e26-ff15-4425-90bb-bfbf55deb39b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gpt_4o_mini_rag(test[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "082c6a5a-0f2a-4941-a465-ffb3137a2e8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# gpt_4_1_rag(test[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce78741b-2966-41d2-9831-cbf8f8d176be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test[1].price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16d90455-ff7d-4f5f-8b8c-8e061263d1c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Tester.test(gpt_4o_mini_rag, test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26d5ddc6-baa6-4760-a430-05671847ac47",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/week8/community_contributions/Ensemble_with_xgboost/Build_RF_XGB_Ensemble.ipynb b/week8/community_contributions/Ensemble_with_xgboost/Build_RF_XGB_Ensemble.ipynb
new file mode 100644
index 0000000..a582e92
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/Build_RF_XGB_Ensemble.ipynb
@@ -0,0 +1,2137 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "40d49349-faaa-420c-9b65-0bdc9edfabce",
+   "metadata": {},
+   "source": [
+    "## Random Forests, XGBoost & Ensemble"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fbcdfea8-7241-46d7-a771-c0381a3e7063",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import os\n",
+    "import re\n",
+    "import math\n",
+    "import json\n",
+    "from tqdm import tqdm\n",
+    "import random\n",
+    "from dotenv import load_dotenv\n",
+    "from huggingface_hub import login\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "from openai import OpenAI\n",
+    "from sentence_transformers import SentenceTransformer\n",
+    "from datasets import load_dataset\n",
+    "import chromadb\n",
+    "from items import Item\n",
+    "from testing import Tester\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.ensemble import GradientBoostingRegressor\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "import joblib\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e6e88bd1-f89c-4b98-92fa-aa4bc1575bca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CONSTANTS\n",
+    "QUESTION = \"How much does this cost to the nearest dollar?\\n\\n\"\n",
+    "DB = \"products_vectorstore\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "98666e73-938e-469d-8987-e6e55ba5e034",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# environment\n",
+    "load_dotenv(override=True)\n",
+    "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
+    "os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN', 'your-key-if-not-using-env')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "dc696493-0b6f-48aa-9fa8-b1ae0ecaf3cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load in the test pickle file:\n",
+    "with open('test.pkl', 'rb') as file:\n",
+    "    test = pickle.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d26a1104-cd11-4361-ab25-85fb576e0582",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = chromadb.PersistentClient(path=DB)\n",
+    "collection = client.get_or_create_collection('products')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e00b82a9-a8dc-46f1-8ea9-2f07cbc8e60d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n",
+    "vectors = np.array(result['embeddings'])\n",
+    "documents = result['documents']\n",
+    "prices = [metadata['price'] for metadata in result['metadatas']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf6492cb-b11a-4ad5-859b-a71a78ffb949",
+   "metadata": {},
+   "source": [
+    "# Random Forest\n",
+    "\n",
+    "We will now train a Random Forest model.\n",
+    "\n",
+    "Using the vectors we already have in Chroma, from the SentenceTransformer model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "48894777-101f-4fe5-998c-47079407f340",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_jobs=-1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(n_jobs=-1, random_state=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestRegressor(n_jobs=-1, random_state=42)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf_model = RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1)\n",
+    "rf_model.fit(vectors, prices)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1b1f080-0ab2-4b7f-8eb9-1d56d048a78d",
+   "metadata": {},
+   "source": [
+    "# Gradient Boosting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fb9fdec7-11f9-48ef-8a31-430b2fccc400",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor(random_state=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "GradientBoostingRegressor(random_state=42)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gb_model = GradientBoostingRegressor(n_estimators=100, random_state=42)\n",
+    "gb_model.fit(vectors, prices)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62eb7ddf-e1da-481e-84c6-1256547566bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the model to a file\n",
+    "\n",
+    "joblib.dump(rf_model, 'random_forest_model.pkl')\n",
+    "joblib.dump(gb_model, 'gradient_boosting_model.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d281dc5e-761e-4a5e-86b3-29d9c0a33d4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load it back in again\n",
+    "\n",
+    "rf_model = joblib.load('random_forest_model.pkl')\n",
+    "gb_model = joblib.load('gradient_boosting_model.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5d438dec-8e5b-4e60-bb6f-c3f82e522dd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from agents.specialist_agent import SpecialistAgent\n",
+    "from agents.frontier_agent import FrontierAgent\n",
+    "from agents.random_forest_agent import RandomForestAgent\n",
+    "from agents.gradient_boosting_agent import GradientBoostingAgent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "afc39369-b97b-4a90-b17e-b20ef501d3c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "specialist = SpecialistAgent()\n",
+    "frontier = FrontierAgent(collection)\n",
+    "random_forest = RandomForestAgent()\n",
+    "gradient_boosting = GradientBoostingAgent()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8e2d0d0a-8bb8-4b39-b046-322828c39244",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def description(item):\n",
+    "    return item.prompt.split(\"to the nearest dollar?\\n\\n\")[1].split(\"\\n\\nPrice is $\")[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "bfe0434f-b29e-4cc0-bad9-b07624665727",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rf(item):\n",
+    "    return random_forest.price(description(item))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "cdf233ec-264f-4b34-9f2b-27c39692137b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m1: Guess: $291.59 Truth: $374.41 Error: $82.82 SLE: 0.06 Item: OEM AC Compressor w/A/C Repair Kit For F...\u001b[0m\n",
+      "\u001b[92m2: Guess: $204.82 Truth: $225.11 Error: $20.29 SLE: 0.01 Item: Motorcraft YB3125 Fan Clutch\u001b[0m\n",
+      "\u001b[91m3: Guess: $206.03 Truth: $61.68 Error: $144.35 SLE: 1.43 Item: Dorman 603-159 Front Washer Fluid Reserv...\u001b[0m\n",
+      "\u001b[93m4: Guess: $364.85 Truth: $599.99 Error: $235.14 SLE: 0.25 Item: HP Premium 17.3-inch HD Plus Touchscreen...\u001b[0m\n",
+      "\u001b[91m5: Guess: $219.07 Truth: $16.99 Error: $202.08 SLE: 6.27 Item: 5-Position Super Switch Pickup Selector ...\u001b[0m\n",
+      "\u001b[92m6: Guess: $57.33 Truth: $31.99 Error: $25.34 SLE: 0.32 Item: Horror Bookmarks, Resin Horror Bookmarks...\u001b[0m\n",
+      "\u001b[91m7: Guess: $272.17 Truth: $101.79 Error: $170.38 SLE: 0.96 Item: SK6241 - Stinger 4 Gauge 6000 Series Pow...\u001b[0m\n",
+      "\u001b[92m8: Guess: $235.98 Truth: $289.00 Error: $53.02 SLE: 0.04 Item: Godox ML60Bi LED Light Kit, Handheld LED...\u001b[0m\n",
+      "\u001b[91m9: Guess: $316.78 Truth: $635.86 Error: $319.08 SLE: 0.48 Item: Randall RG75DG3PLUS G3 Plus 100-Watt Com...\u001b[0m\n",
+      "\u001b[91m10: Guess: $175.79 Truth: $65.99 Error: $109.80 SLE: 0.94 Item: HOLDWILL 6 Pack LED Shop Light, 4FT 24W ...\u001b[0m\n",
+      "\u001b[92m11: Guess: $262.45 Truth: $254.21 Error: $8.24 SLE: 0.00 Item: Viking Horns V103C/1005ATK 3 Gallon Air ...\u001b[0m\n",
+      "\u001b[93m12: Guess: $253.81 Truth: $412.99 Error: $159.18 SLE: 0.24 Item: CURT 70110 Custom Tow Bar Base Plate Bra...\u001b[0m\n",
+      "\u001b[92m13: Guess: $174.96 Truth: $205.50 Error: $30.54 SLE: 0.03 Item: 10-Pack Solar HAMMERED BRONZE Finish Pos...\u001b[0m\n",
+      "\u001b[92m14: Guess: $279.61 Truth: $248.23 Error: $31.38 SLE: 0.01 Item: COSTWAY Electric Tumble Dryer, Sliver\u001b[0m\n",
+      "\u001b[93m15: Guess: $305.92 Truth: $399.00 Error: $93.08 SLE: 0.07 Item: FREE SIGNAL TV Transit 32\" 12 Volt DC Po...\u001b[0m\n",
+      "\u001b[92m16: Guess: $339.52 Truth: $373.94 Error: $34.42 SLE: 0.01 Item: Bilstein 5100 Monotube Gas Shock Set com...\u001b[0m\n",
+      "\u001b[91m17: Guess: $237.41 Truth: $92.89 Error: $144.52 SLE: 0.87 Item: Sangean K-200 Multi-Function Upright AM/...\u001b[0m\n",
+      "\u001b[93m18: Guess: $112.32 Truth: $51.99 Error: $60.33 SLE: 0.58 Item: Charles Leonard Magnetic Lapboard Class ...\u001b[0m\n",
+      "\u001b[91m19: Guess: $425.61 Truth: $179.00 Error: $246.61 SLE: 0.74 Item: Gigabyte AMD Radeon HD 7870 2 GB GDDR5 D...\u001b[0m\n",
+      "\u001b[93m20: Guess: $75.46 Truth: $19.42 Error: $56.04 SLE: 1.74 Item: 3dRose LLC 8 x 8 x 0.25 Inches Bull Terr...\u001b[0m\n",
+      "\u001b[93m21: Guess: $332.77 Truth: $539.95 Error: $207.18 SLE: 0.23 Item: ROKINON 85mm F1.4 Auto Focus Full Frame ...\u001b[0m\n",
+      "\u001b[93m22: Guess: $214.54 Truth: $147.67 Error: $66.87 SLE: 0.14 Item: AUTOSAVER88 Headlight Assembly Compatibl...\u001b[0m\n",
+      "\u001b[91m23: Guess: $179.36 Truth: $24.99 Error: $154.37 SLE: 3.75 Item: ASI NAUTICAL 2.5 Inches Opera Glasses Bi...\u001b[0m\n",
+      "\u001b[91m24: Guess: $283.14 Truth: $149.00 Error: $134.14 SLE: 0.41 Item: Behringer TUBE OVERDRIVE TO100 Authentic...\u001b[0m\n",
+      "\u001b[92m25: Guess: $49.14 Truth: $16.99 Error: $32.15 SLE: 1.05 Item: Fun Express Insect Finger Puppets - 24 f...\u001b[0m\n",
+      "\u001b[93m26: Guess: $75.03 Truth: $7.99 Error: $67.04 SLE: 4.56 Item: WAFJAMF Roller Stamp Identity Theft Stam...\u001b[0m\n",
+      "\u001b[92m27: Guess: $186.19 Truth: $199.99 Error: $13.80 SLE: 0.01 Item: Capulina Tiffany Floor Lamp 2-Light 16\" ...\u001b[0m\n",
+      "\u001b[92m28: Guess: $273.60 Truth: $251.45 Error: $22.15 SLE: 0.01 Item: Apple Watch Series 6 (GPS, 44mm) - Space...\u001b[0m\n",
+      "\u001b[92m29: Guess: $232.49 Truth: $231.62 Error: $0.87 SLE: 0.00 Item: ICON 01725 Tandem Axle Fender Skirt FS17...\u001b[0m\n",
+      "\u001b[91m30: Guess: $226.25 Truth: $135.00 Error: $91.25 SLE: 0.26 Item: SanDisk 128GB Ultra (10 Pack) MicroSD Cl...\u001b[0m\n",
+      "\u001b[92m31: Guess: $301.87 Truth: $356.62 Error: $54.75 SLE: 0.03 Item: Velvac 2020,L,C/Hr,W,E2003,102\",Bk - 715...\u001b[0m\n",
+      "\u001b[92m32: Guess: $233.55 Truth: $257.99 Error: $24.44 SLE: 0.01 Item: TCMT Passenger Backrest Sissy Bar & Lugg...\u001b[0m\n",
+      "\u001b[91m33: Guess: $169.42 Truth: $27.99 Error: $141.43 SLE: 3.14 Item: Alnicov 63.5MM Brass Tremolo Block，Tremo...\u001b[0m\n",
+      "\u001b[91m34: Guess: $266.18 Truth: $171.20 Error: $94.98 SLE: 0.19 Item: Subaru Forester Outback Legacy OEM Engin...\u001b[0m\n",
+      "\u001b[92m35: Guess: $219.00 Truth: $225.00 Error: $6.00 SLE: 0.00 Item: Richmond Auto Upholstery - 2012 Dodge Ra...\u001b[0m\n",
+      "\u001b[91m36: Guess: $202.91 Truth: $105.00 Error: $97.91 SLE: 0.43 Item: AP-39 Automotive Paint Primer Grey 2K Ur...\u001b[0m\n",
+      "\u001b[93m37: Guess: $220.41 Truth: $299.99 Error: $79.58 SLE: 0.09 Item: Road Top Wireless Carplay Retrofit Kit D...\u001b[0m\n",
+      "\u001b[93m38: Guess: $658.90 Truth: $535.09 Error: $123.81 SLE: 0.04 Item: Gibson Performance Exhaust 5658 Aluminiz...\u001b[0m\n",
+      "\u001b[91m39: Guess: $129.36 Truth: $12.33 Error: $117.03 SLE: 5.20 Item: Bella Tunno Happy Links - Baby Montessor...\u001b[0m\n",
+      "\u001b[91m40: Guess: $183.61 Truth: $84.99 Error: $98.62 SLE: 0.58 Item: CANMORE H300 Handheld GPS Golf Device, S...\u001b[0m\n",
+      "\u001b[91m41: Guess: $110.24 Truth: $15.99 Error: $94.25 SLE: 3.53 Item: DCPOWER AC Adapter Compatible Replacemen...\u001b[0m\n",
+      "\u001b[91m42: Guess: $180.79 Truth: $62.44 Error: $118.35 SLE: 1.11 Item: Sharp, VX2128V, Commercial Desktop Calcu...\u001b[0m\n",
+      "\u001b[93m43: Guess: $132.13 Truth: $82.99 Error: $49.14 SLE: 0.21 Item: Melissa & Doug Lifelike Plush Stork Gian...\u001b[0m\n",
+      "\u001b[91m44: Guess: $311.71 Truth: $599.95 Error: $288.24 SLE: 0.43 Item: Sony SSCS8 2-Way 3-Driver Center Channel...\u001b[0m\n",
+      "\u001b[91m45: Guess: $277.69 Truth: $194.99 Error: $82.70 SLE: 0.12 Item: ASUS Chromebook CX1, 14\" Full HD NanoEdg...\u001b[0m\n",
+      "\u001b[92m46: Guess: $317.88 Truth: $344.95 Error: $27.07 SLE: 0.01 Item: FiiO X7 32GB Hi-Res Lossless Music Playe...\u001b[0m\n",
+      "\u001b[92m47: Guess: $46.17 Truth: $37.99 Error: $8.18 SLE: 0.04 Item: TORRO Leather Case Compatible with iPhon...\u001b[0m\n",
+      "\u001b[92m48: Guess: $228.40 Truth: $224.35 Error: $4.05 SLE: 0.00 Item: Universal Air Conditioner KT 1031 A/C Co...\u001b[0m\n",
+      "\u001b[92m49: Guess: $747.66 Truth: $814.00 Error: $66.34 SLE: 0.01 Item: Street Series Stainless Performance Cat-...\u001b[0m\n",
+      "\u001b[92m50: Guess: $511.14 Truth: $439.88 Error: $71.26 SLE: 0.02 Item: Lenovo IdeaPad 3 14-inch Laptop, 14.0-in...\u001b[0m\n",
+      "\u001b[92m51: Guess: $310.99 Truth: $341.43 Error: $30.44 SLE: 0.01 Item: Access Bed Covers TonnoSport 22050219 - ...\u001b[0m\n",
+      "\u001b[91m52: Guess: $200.34 Truth: $46.78 Error: $153.56 SLE: 2.07 Item: G.I. JOE Hasbro 3 3/4\" Wave 5 Action Fig...\u001b[0m\n",
+      "\u001b[92m53: Guess: $146.38 Truth: $171.44 Error: $25.06 SLE: 0.02 Item: T&S Brass B-0232-BST Double Pantry Fauce...\u001b[0m\n",
+      "\u001b[91m54: Guess: $261.18 Truth: $458.00 Error: $196.82 SLE: 0.31 Item: ZTUOAUMA Fuel Injection Pump 3090942 309...\u001b[0m\n",
+      "\u001b[91m55: Guess: $225.15 Truth: $130.75 Error: $94.40 SLE: 0.29 Item: 2AP18AA#ABA Hp Prime Graphing Calculator...\u001b[0m\n",
+      "\u001b[91m56: Guess: $186.04 Truth: $83.81 Error: $102.23 SLE: 0.63 Item: Lowrance 000-0119-83 Nmea 2000 25' Exten...\u001b[0m\n",
+      "\u001b[91m57: Guess: $169.97 Truth: $386.39 Error: $216.42 SLE: 0.67 Item: Jeep Genuine Accessories 82213051 Hood L...\u001b[0m\n",
+      "\u001b[93m58: Guess: $209.56 Truth: $169.00 Error: $40.56 SLE: 0.05 Item: GODOX CB-06 Hard Carrying Case with Whee...\u001b[0m\n",
+      "\u001b[91m59: Guess: $146.74 Truth: $17.95 Error: $128.79 SLE: 4.22 Item: Au-Tomotive Gold, INC. Ford Black Valet ...\u001b[0m\n",
+      "\u001b[92m60: Guess: $222.65 Truth: $269.00 Error: $46.35 SLE: 0.04 Item: Snailfly Black Roof Rack Rail + Cross Ba...\u001b[0m\n",
+      "\u001b[91m61: Guess: $202.16 Truth: $77.77 Error: $124.39 SLE: 0.90 Item: KING SHA Anti Glare LED Track Lighting H...\u001b[0m\n",
+      "\u001b[91m62: Guess: $181.11 Truth: $88.99 Error: $92.12 SLE: 0.50 Item: APS Compatible with Chevy Silverado 1500...\u001b[0m\n",
+      "\u001b[93m63: Guess: $233.73 Truth: $364.41 Error: $130.68 SLE: 0.20 Item: Wilwood Engineering 14011291R Brake Cali...\u001b[0m\n",
+      "\u001b[92m64: Guess: $134.65 Truth: $127.03 Error: $7.62 SLE: 0.00 Item: ACDelco Gold 336-1925A Starter, Remanufa...\u001b[0m\n",
+      "\u001b[92m65: Guess: $636.90 Truth: $778.95 Error: $142.05 SLE: 0.04 Item: UWS EC10783 69-Inch Matte Black Heavy-Wa...\u001b[0m\n",
+      "\u001b[91m66: Guess: $452.42 Truth: $206.66 Error: $245.76 SLE: 0.61 Item: Dell Latitude E5440 14in Business Laptop...\u001b[0m\n",
+      "\u001b[91m67: Guess: $187.75 Truth: $35.94 Error: $151.81 SLE: 2.66 Item: (Plug and Play) Spare Tire Brake Light W...\u001b[0m\n",
+      "\u001b[91m68: Guess: $249.13 Truth: $149.00 Error: $100.13 SLE: 0.26 Item: The Ultimate Roadside Rescue Assistant\u001b[0m\n",
+      "\u001b[92m69: Guess: $224.20 Truth: $251.98 Error: $27.78 SLE: 0.01 Item: Brand New 18\" x 8.5\" Replacement Wheel f...\u001b[0m\n",
+      "\u001b[93m70: Guess: $234.18 Truth: $160.00 Error: $74.18 SLE: 0.14 Item: Headlight Headlamp LH Left & RH Right Pa...\u001b[0m\n",
+      "\u001b[91m71: Guess: $122.29 Truth: $39.99 Error: $82.30 SLE: 1.21 Item: Lilo And Stitch Deluxe Oversize Print La...\u001b[0m\n",
+      "\u001b[93m72: Guess: $250.75 Truth: $362.41 Error: $111.66 SLE: 0.13 Item: AC Compressor & A/C Clutch For Hyundai A...\u001b[0m\n",
+      "\u001b[93m73: Guess: $238.01 Truth: $344.00 Error: $105.99 SLE: 0.13 Item: House Of Troy PIN475-AB Pinnacle Collect...\u001b[0m\n",
+      "\u001b[91m74: Guess: $209.07 Truth: $25.09 Error: $183.98 SLE: 4.35 Item: Juno T29 WH Floating Electrical Feed Sin...\u001b[0m\n",
+      "\u001b[92m75: Guess: $171.57 Truth: $175.95 Error: $4.38 SLE: 0.00 Item: Sherman GO-PARTS - for 2013-2016 Toyota ...\u001b[0m\n",
+      "\u001b[91m76: Guess: $253.67 Truth: $132.64 Error: $121.03 SLE: 0.42 Item: Roland RPU-3 Electronic Keyboard Pedal o...\u001b[0m\n",
+      "\u001b[93m77: Guess: $280.83 Truth: $422.99 Error: $142.16 SLE: 0.17 Item: Rockland VMI14 12,000 Pound 12 Volt DC E...\u001b[0m\n",
+      "\u001b[92m78: Guess: $167.75 Truth: $146.48 Error: $21.27 SLE: 0.02 Item: Max Advanced Brakes Elite XDS Front Cros...\u001b[0m\n",
+      "\u001b[93m79: Guess: $197.53 Truth: $156.83 Error: $40.70 SLE: 0.05 Item: Quality-Built 11030 Premium Quality Alte...\u001b[0m\n",
+      "\u001b[91m80: Guess: $357.41 Truth: $251.99 Error: $105.42 SLE: 0.12 Item: Lucida LG-510 Student Classical Guitar, ...\u001b[0m\n",
+      "\u001b[91m81: Guess: $222.52 Truth: $940.33 Error: $717.81 SLE: 2.07 Item: Longacre 52-79800 Aluminum Turn Plates\u001b[0m\n",
+      "\u001b[91m82: Guess: $177.82 Truth: $52.99 Error: $124.83 SLE: 1.43 Item: Motion Pro 08-0380 Adjustable Torque Wre...\u001b[0m\n",
+      "\u001b[93m83: Guess: $270.79 Truth: $219.95 Error: $50.84 SLE: 0.04 Item: Glyph Thunderbolt 3 NVMe Dock (0 GB)\u001b[0m\n",
+      "\u001b[93m84: Guess: $280.41 Truth: $441.03 Error: $160.62 SLE: 0.20 Item: TOYO Open Country MT Performance Radial ...\u001b[0m\n",
+      "\u001b[93m85: Guess: $239.35 Truth: $168.98 Error: $70.37 SLE: 0.12 Item: Razer Seiren X USB Streaming Microphone ...\u001b[0m\n",
+      "\u001b[92m86: Guess: $41.61 Truth: $2.49 Error: $39.12 SLE: 6.26 Item: Happy Birthday to Dad From Your Daughter...\u001b[0m\n",
+      "\u001b[91m87: Guess: $198.54 Truth: $98.62 Error: $99.92 SLE: 0.48 Item: Little Tikes My Real Jam First Concert S...\u001b[0m\n",
+      "\u001b[92m88: Guess: $206.61 Truth: $256.95 Error: $50.34 SLE: 0.05 Item: Studio M Peace and Harmony Art Pole Comm...\u001b[0m\n",
+      "\u001b[91m89: Guess: $175.48 Truth: $30.99 Error: $144.49 SLE: 2.92 Item: MyVolts 12V Power Supply Adaptor Compati...\u001b[0m\n",
+      "\u001b[93m90: Guess: $379.12 Truth: $569.84 Error: $190.72 SLE: 0.17 Item: Dell Latitude 7212 Rugged Extreme Tablet...\u001b[0m\n",
+      "\u001b[93m91: Guess: $228.43 Truth: $177.99 Error: $50.44 SLE: 0.06 Item: Covermates Contour Fit Car Cover - Light...\u001b[0m\n",
+      "\u001b[93m92: Guess: $611.60 Truth: $997.99 Error: $386.39 SLE: 0.24 Item: Westin 57-4025 Black HDX Grille Guard fi...\u001b[0m\n",
+      "\u001b[92m93: Guess: $243.08 Truth: $219.00 Error: $24.08 SLE: 0.01 Item: Fieldpiece JL2 Job Link Wireless App Tra...\u001b[0m\n",
+      "\u001b[93m94: Guess: $275.13 Truth: $225.55 Error: $49.58 SLE: 0.04 Item: hansgrohe Talis S Modern Premium Easy Cl...\u001b[0m\n",
+      "\u001b[93m95: Guess: $332.03 Truth: $495.95 Error: $163.92 SLE: 0.16 Item: G-Technology G-SPEED eS PRO High-Perform...\u001b[0m\n",
+      "\u001b[92m96: Guess: $764.31 Truth: $942.37 Error: $178.06 SLE: 0.04 Item: DreamLine SHDR-1960723L-01 Shower Door, ...\u001b[0m\n",
+      "\u001b[91m97: Guess: $157.49 Truth: $1.94 Error: $155.55 SLE: 15.90 Item: Sanctuary Square Backplate Finish: Oiled...\u001b[0m\n",
+      "\u001b[92m98: Guess: $283.05 Truth: $284.34 Error: $1.29 SLE: 0.00 Item: Pelican Protector 1750 Long Case - Multi...\u001b[0m\n",
+      "\u001b[93m99: Guess: $214.80 Truth: $171.90 Error: $42.90 SLE: 0.05 Item: Brock Replacement Driver and Passenger H...\u001b[0m\n",
+      "\u001b[91m100: Guess: $232.53 Truth: $144.99 Error: $87.54 SLE: 0.22 Item: Carlinkit Ai Box Mini, Android 11, Multi...\u001b[0m\n",
+      "\u001b[91m101: Guess: $251.67 Truth: $470.47 Error: $218.80 SLE: 0.39 Item: StarDot NetCamLIVE2 YouTube Live Stream ...\u001b[0m\n",
+      "\u001b[91m102: Guess: $220.74 Truth: $66.95 Error: $153.79 SLE: 1.40 Item: Atomic Compatible FILXXCAR0016 16x25x5 M...\u001b[0m\n",
+      "\u001b[92m103: Guess: $128.52 Truth: $117.00 Error: $11.52 SLE: 0.01 Item: Bandai Awakening of S. H. s.h.figuarts s...\u001b[0m\n",
+      "\u001b[93m104: Guess: $236.39 Truth: $172.14 Error: $64.25 SLE: 0.10 Item: Fit System 62135G Passenger Side Towing ...\u001b[0m\n",
+      "\u001b[93m105: Guess: $295.98 Truth: $392.74 Error: $96.76 SLE: 0.08 Item: Black Horse Black Aluminum Exceed Runnin...\u001b[0m\n",
+      "\u001b[92m106: Guess: $53.66 Truth: $16.99 Error: $36.67 SLE: 1.23 Item: Dearsun Twinkle Star Color Night Light P...\u001b[0m\n",
+      "\u001b[93m107: Guess: $62.47 Truth: $1.34 Error: $61.13 SLE: 10.89 Item: Pokemon - Gallade Spirit Link (83/108) -...\u001b[0m\n",
+      "\u001b[92m108: Guess: $348.03 Truth: $349.98 Error: $1.95 SLE: 0.00 Item: Ibanez GA34STCE-NT GIO Series Classical ...\u001b[0m\n",
+      "\u001b[93m109: Guess: $247.10 Truth: $370.71 Error: $123.61 SLE: 0.16 Item: Set 2 Heavy Duty 12-16.5 12x16.5 12 Ply ...\u001b[0m\n",
+      "\u001b[93m110: Guess: $134.89 Truth: $65.88 Error: $69.01 SLE: 0.50 Item: Hairpin Table Legs 28\" Heavy Duty Hairpi...\u001b[0m\n",
+      "\u001b[92m111: Guess: $219.90 Truth: $229.99 Error: $10.09 SLE: 0.00 Item: Marada Racing Seat with Adjustable Slide...\u001b[0m\n",
+      "\u001b[91m112: Guess: $155.73 Truth: $9.14 Error: $146.59 SLE: 7.50 Item: Remington Industries 24UL1007STRWHI25 24...\u001b[0m\n",
+      "\u001b[91m113: Guess: $487.76 Truth: $199.00 Error: $288.76 SLE: 0.80 Item: Acer S3-391-6046 13.3-inch Ultrabook, In...\u001b[0m\n",
+      "\u001b[91m114: Guess: $254.11 Truth: $109.99 Error: $144.12 SLE: 0.69 Item: ICBEAMER 7\" RGB LED Headlights Bulb Halo...\u001b[0m\n",
+      "\u001b[93m115: Guess: $374.14 Truth: $570.42 Error: $196.28 SLE: 0.18 Item: R1 Concepts Front Rear Brakes and Rotors...\u001b[0m\n",
+      "\u001b[92m116: Guess: $264.19 Truth: $279.99 Error: $15.80 SLE: 0.00 Item: Camplux 2.64 GPM Tankless , Outdoor Port...\u001b[0m\n",
+      "\u001b[91m117: Guess: $114.52 Truth: $30.99 Error: $83.53 SLE: 1.65 Item: KNOKLOCK 10 Pack 3.75 Inch(96mm) Kitchen...\u001b[0m\n",
+      "\u001b[91m118: Guess: $224.50 Truth: $31.99 Error: $192.51 SLE: 3.69 Item: Valley Enterprises Yaesu USB FTDI CT-62 ...\u001b[0m\n",
+      "\u001b[91m119: Guess: $162.25 Truth: $15.90 Error: $146.35 SLE: 5.14 Item: G9 LED Light Bulbs，8W，75W 100W replaceme...\u001b[0m\n",
+      "\u001b[91m120: Guess: $141.30 Truth: $45.99 Error: $95.31 SLE: 1.23 Item: ZCHAOZ 4 Lights Antique White Farmhouse ...\u001b[0m\n",
+      "\u001b[91m121: Guess: $227.91 Truth: $113.52 Error: $114.39 SLE: 0.48 Item: Honeywell TH8320R1003 Honeywell VisionPr...\u001b[0m\n",
+      "\u001b[91m122: Guess: $306.45 Truth: $516.99 Error: $210.54 SLE: 0.27 Item: Patriot Exhaust H8013-1 1-7/8\" Clippster...\u001b[0m\n",
+      "\u001b[92m123: Guess: $217.02 Truth: $196.99 Error: $20.03 SLE: 0.01 Item: Fitrite Autopart New Front Left Driver S...\u001b[0m\n",
+      "\u001b[91m124: Guess: $162.74 Truth: $46.55 Error: $116.19 SLE: 1.53 Item: Technical Precision Replacement for GE G...\u001b[0m\n",
+      "\u001b[92m125: Guess: $288.98 Truth: $356.99 Error: $68.01 SLE: 0.04 Item: Covercraft Carhartt SeatSaver Front Row ...\u001b[0m\n",
+      "\u001b[93m126: Guess: $221.58 Truth: $319.95 Error: $98.37 SLE: 0.13 Item: Sennheiser SD Pro 2 (506008) - Double-Si...\u001b[0m\n",
+      "\u001b[93m127: Guess: $157.53 Truth: $96.06 Error: $61.47 SLE: 0.24 Item: Hitachi MAF0110 Mass Air Flow Sensor\u001b[0m\n",
+      "\u001b[91m128: Guess: $271.09 Truth: $190.99 Error: $80.10 SLE: 0.12 Item: AmScope SE305R-P-LED-PS36A 10X-30X LED C...\u001b[0m\n",
+      "\u001b[93m129: Guess: $185.29 Truth: $257.95 Error: $72.66 SLE: 0.11 Item: Front Left Driver Side Window Regulator ...\u001b[0m\n",
+      "\u001b[91m130: Guess: $174.57 Truth: $62.95 Error: $111.62 SLE: 1.02 Item: Premium Replica Hubcap Set, Fits Nissan ...\u001b[0m\n",
+      "\u001b[92m131: Guess: $46.20 Truth: $47.66 Error: $1.46 SLE: 0.00 Item: Excellerations Phonics Spelling Game for...\u001b[0m\n",
+      "\u001b[93m132: Guess: $275.28 Truth: $226.99 Error: $48.29 SLE: 0.04 Item: RC4WD BigDog Dual Axle Scale Car/Truck T...\u001b[0m\n",
+      "\u001b[93m133: Guess: $276.66 Truth: $359.95 Error: $83.29 SLE: 0.07 Item: Unknown Stage 2 Clutch Kit - Low Altitud...\u001b[0m\n",
+      "\u001b[91m134: Guess: $257.09 Truth: $78.40 Error: $178.69 SLE: 1.39 Item: 2002-2008 Dodge Ram 1500 Mopar 4X4 Emble...\u001b[0m\n",
+      "\u001b[92m135: Guess: $186.20 Truth: $172.77 Error: $13.43 SLE: 0.01 Item: Pro Comp Alloys Series 89 Wheel with Pol...\u001b[0m\n",
+      "\u001b[93m136: Guess: $242.47 Truth: $316.45 Error: $73.98 SLE: 0.07 Item: Detroit Axle - Front Rear Strut & Coil S...\u001b[0m\n",
+      "\u001b[91m137: Guess: $169.15 Truth: $87.99 Error: $81.16 SLE: 0.42 Item: ECCPP Rear Wheel Axle Replacement fit fo...\u001b[0m\n",
+      "\u001b[92m138: Guess: $269.79 Truth: $226.63 Error: $43.16 SLE: 0.03 Item: Dell Latitude E6520 Intel i7-2720QM 2.20...\u001b[0m\n",
+      "\u001b[91m139: Guess: $193.22 Truth: $31.49 Error: $161.73 SLE: 3.20 Item: F FIERCE CYCLE 251pcs Black Universal Mo...\u001b[0m\n",
+      "\u001b[92m140: Guess: $174.37 Truth: $196.00 Error: $21.63 SLE: 0.01 Item: Flash Furniture 4 Pk. HERCULES Series 88...\u001b[0m\n",
+      "\u001b[91m141: Guess: $253.55 Truth: $78.40 Error: $175.15 SLE: 1.36 Item: B&M 30287 Throttle Valve/Kickdown Cable,...\u001b[0m\n",
+      "\u001b[92m142: Guess: $129.12 Truth: $116.25 Error: $12.87 SLE: 0.01 Item: Gates TCK226 PowerGrip Premium Timing Be...\u001b[0m\n",
+      "\u001b[92m143: Guess: $139.68 Truth: $112.78 Error: $26.90 SLE: 0.05 Item: Monroe Shocks & Struts Quick-Strut 17149...\u001b[0m\n",
+      "\u001b[91m144: Guess: $169.66 Truth: $27.32 Error: $142.34 SLE: 3.23 Item: Feit Electric BPMR16/GU10/930CA/6 35W EQ...\u001b[0m\n",
+      "\u001b[92m145: Guess: $113.93 Truth: $145.91 Error: $31.98 SLE: 0.06 Item: Yellow Jacket 2806 Contractor Extension ...\u001b[0m\n",
+      "\u001b[93m146: Guess: $211.22 Truth: $171.09 Error: $40.13 SLE: 0.04 Item: Garage-Pro Tailgate SET Compatible with ...\u001b[0m\n",
+      "\u001b[93m147: Guess: $223.57 Truth: $167.95 Error: $55.62 SLE: 0.08 Item: 3M Perfect It Buffing and Polishing Kit ...\u001b[0m\n",
+      "\u001b[93m148: Guess: $74.91 Truth: $28.49 Error: $46.42 SLE: 0.89 Item: Chinese Style Dollhouse Model DIY Miniat...\u001b[0m\n",
+      "\u001b[92m149: Guess: $161.23 Truth: $122.23 Error: $39.00 SLE: 0.08 Item: Generic NRG Innovations SRK-161H Steerin...\u001b[0m\n",
+      "\u001b[91m150: Guess: $120.85 Truth: $32.99 Error: $87.86 SLE: 1.63 Item: Learning Resources Coding Critters Range...\u001b[0m\n",
+      "\u001b[91m151: Guess: $182.82 Truth: $71.20 Error: $111.62 SLE: 0.87 Item: Bosch Automotive 15463 Oxygen Sensor, OE...\u001b[0m\n",
+      "\u001b[92m152: Guess: $78.46 Truth: $112.75 Error: $34.29 SLE: 0.13 Item: Case of 24-2 Inch Blue Painters Tape - 6...\u001b[0m\n",
+      "\u001b[93m153: Guess: $211.75 Truth: $142.43 Error: $69.32 SLE: 0.16 Item: MOCA Engine Water Pump & Fan Clutch fit ...\u001b[0m\n",
+      "\u001b[91m154: Guess: $230.96 Truth: $398.99 Error: $168.03 SLE: 0.30 Item: SAREMAS Foot Step Bars for Hyundai Palis...\u001b[0m\n",
+      "\u001b[92m155: Guess: $404.52 Truth: $449.00 Error: $44.48 SLE: 0.01 Item: Gretsch G9210 Square Neck Boxcar Mahogan...\u001b[0m\n",
+      "\u001b[91m156: Guess: $271.13 Truth: $189.00 Error: $82.13 SLE: 0.13 Item: NikoMaku Mirror Dash Cam Front and Rear ...\u001b[0m\n",
+      "\u001b[91m157: Guess: $245.68 Truth: $120.91 Error: $124.77 SLE: 0.50 Item: Fenix HP25R v2.0 USB-C Rechargeable Head...\u001b[0m\n",
+      "\u001b[93m158: Guess: $267.75 Truth: $203.53 Error: $64.22 SLE: 0.07 Item: R&L Racing Heavy Duty Roll-Up Soft Tonne...\u001b[0m\n",
+      "\u001b[92m159: Guess: $306.13 Truth: $349.99 Error: $43.86 SLE: 0.02 Item: Garmin 010-02258-10 GPSMAP 64sx, Handhel...\u001b[0m\n",
+      "\u001b[93m160: Guess: $91.29 Truth: $34.35 Error: $56.94 SLE: 0.92 Item: Brown 5-7/8\" X 8-1/2\" X 3/16\" Thick Heav...\u001b[0m\n",
+      "\u001b[93m161: Guess: $258.04 Truth: $384.99 Error: $126.95 SLE: 0.16 Item: GAOMON PD2200 Pen Display & 20 Pen Nibs ...\u001b[0m\n",
+      "\u001b[93m162: Guess: $269.75 Truth: $211.00 Error: $58.75 SLE: 0.06 Item: VXMOTOR for 97-03 Ford F150/F250 Lightdu...\u001b[0m\n",
+      "\u001b[91m163: Guess: $323.40 Truth: $129.00 Error: $194.40 SLE: 0.84 Item: HP EliteBook 2540p Intel Core i7-640LM X...\u001b[0m\n",
+      "\u001b[92m164: Guess: $147.41 Truth: $111.45 Error: $35.96 SLE: 0.08 Item: Green EPX Mixing Nozzles 100-Pack-fits 3...\u001b[0m\n",
+      "\u001b[91m165: Guess: $176.03 Truth: $81.12 Error: $94.91 SLE: 0.59 Item: Box Partners 6 1/4 x 3 1/8\" 13 Pt. Manil...\u001b[0m\n",
+      "\u001b[93m166: Guess: $331.45 Truth: $457.08 Error: $125.63 SLE: 0.10 Item: Vixen Air 1/2\" NPT Air Ride Suspension H...\u001b[0m\n",
+      "\u001b[91m167: Guess: $160.27 Truth: $49.49 Error: $110.78 SLE: 1.35 Item: Smart Floor Lamp, 2700-6500K+RGBPink Mul...\u001b[0m\n",
+      "\u001b[91m168: Guess: $196.85 Truth: $80.56 Error: $116.29 SLE: 0.79 Item: SOZG 324mm Wheelbase Body Shell RC Car B...\u001b[0m\n",
+      "\u001b[92m169: Guess: $329.67 Truth: $278.39 Error: $51.28 SLE: 0.03 Item: Mickey Thompson ET Street S/S Racing Rad...\u001b[0m\n",
+      "\u001b[91m170: Guess: $202.99 Truth: $364.50 Error: $161.51 SLE: 0.34 Item: Pirelli 275/40R20 106W XL RFT P0 PZ4-LUX...\u001b[0m\n",
+      "\u001b[93m171: Guess: $281.98 Truth: $378.99 Error: $97.01 SLE: 0.09 Item: Torklift C3212 Rear Tie Down\u001b[0m\n",
+      "\u001b[92m172: Guess: $170.63 Truth: $165.28 Error: $5.35 SLE: 0.00 Item: Cardone 78-4226 Remanufactured Ford Comp...\u001b[0m\n",
+      "\u001b[91m173: Guess: $186.41 Truth: $56.74 Error: $129.67 SLE: 1.39 Item: Kidde AccessPoint 001798 Supra TouchPoin...\u001b[0m\n",
+      "\u001b[93m174: Guess: $216.29 Truth: $307.95 Error: $91.66 SLE: 0.12 Item: 3M Protecta 3100414 Self Retracting Life...\u001b[0m\n",
+      "\u001b[91m175: Guess: $174.78 Truth: $38.00 Error: $136.78 SLE: 2.27 Item: Plantronics 89435-01 Wired Headset, Blac...\u001b[0m\n",
+      "\u001b[91m176: Guess: $153.62 Truth: $53.00 Error: $100.62 SLE: 1.11 Item: Logitech K750 Wireless Solar Keyboard fo...\u001b[0m\n",
+      "\u001b[93m177: Guess: $361.87 Truth: $498.00 Error: $136.13 SLE: 0.10 Item: Olympus PEN E-PL9 Body Only with 3-Inch ...\u001b[0m\n",
+      "\u001b[91m178: Guess: $193.87 Truth: $53.99 Error: $139.88 SLE: 1.60 Item: Beck/Arnley 051-6066 Hub & Bearing Assem...\u001b[0m\n",
+      "\u001b[93m179: Guess: $240.58 Truth: $350.00 Error: $109.42 SLE: 0.14 Item: Eibach Pro-Kit Performance Springs E10-6...\u001b[0m\n",
+      "\u001b[93m180: Guess: $208.80 Truth: $299.95 Error: $91.15 SLE: 0.13 Item: LEGO DC Batman 1989 Batwing 76161 Displa...\u001b[0m\n",
+      "\u001b[93m181: Guess: $171.06 Truth: $94.93 Error: $76.13 SLE: 0.34 Item: Kingston Brass KS3608PL Restoration 4-In...\u001b[0m\n",
+      "\u001b[92m182: Guess: $335.46 Truth: $379.00 Error: $43.54 SLE: 0.01 Item: Polk Vanishing Series 265-LS In-Wall 3-W...\u001b[0m\n",
+      "\u001b[93m183: Guess: $237.21 Truth: $299.95 Error: $62.74 SLE: 0.05 Item: Spec-D Tuning LED Projector Headlights G...\u001b[0m\n",
+      "\u001b[92m184: Guess: $44.95 Truth: $24.99 Error: $19.96 SLE: 0.32 Item: RICHMOND & FINCH Airpod Pro Case, Green ...\u001b[0m\n",
+      "\u001b[91m185: Guess: $169.37 Truth: $41.04 Error: $128.33 SLE: 1.96 Item: LFA Industries 43B-5A-33JT 1/16-1/2-1.5-...\u001b[0m\n",
+      "\u001b[91m186: Guess: $172.91 Truth: $327.90 Error: $154.99 SLE: 0.41 Item: SAUTVS LED Headlight Assembly for Slings...\u001b[0m\n",
+      "\u001b[91m187: Guess: $118.32 Truth: $10.99 Error: $107.33 SLE: 5.28 Item: 2 Pack Combo Womens Safety Glasses Impac...\u001b[0m\n",
+      "\u001b[91m188: Guess: $158.34 Truth: $14.99 Error: $143.35 SLE: 5.29 Item: Arepa - Venezuelan cuisine - Venezuela P...\u001b[0m\n",
+      "\u001b[92m189: Guess: $119.00 Truth: $84.95 Error: $34.05 SLE: 0.11 Item: Schlage Lock Company KS23D2300 Padlock, ...\u001b[0m\n",
+      "\u001b[91m190: Guess: $191.56 Truth: $111.00 Error: $80.56 SLE: 0.29 Item: Techni Mobili White Sit to Stand Mobile ...\u001b[0m\n",
+      "\u001b[92m191: Guess: $141.72 Truth: $123.73 Error: $17.99 SLE: 0.02 Item: Special Lite Products Contemporary Wall ...\u001b[0m\n",
+      "\u001b[91m192: Guess: $268.10 Truth: $557.38 Error: $289.28 SLE: 0.53 Item: Tascam DP-24SD 24-Track Digital Portastu...\u001b[0m\n",
+      "\u001b[91m193: Guess: $219.48 Truth: $95.55 Error: $123.93 SLE: 0.68 Item: Glow Lighting 636CC10SP Vista Crystal Fl...\u001b[0m\n",
+      "\u001b[93m194: Guess: $204.85 Truth: $154.00 Error: $50.85 SLE: 0.08 Item: Z3 Wind Deflector, Smoke Tint, Lexan, Wi...\u001b[0m\n",
+      "\u001b[91m195: Guess: $300.74 Truth: $198.99 Error: $101.75 SLE: 0.17 Item: Olympus E-20 5MP Digital Camera w/ 4x Op...\u001b[0m\n",
+      "\u001b[91m196: Guess: $247.62 Truth: $430.44 Error: $182.82 SLE: 0.30 Item: PHYNEDI 1:1000 World Trade Center (1973-...\u001b[0m\n",
+      "\u001b[92m197: Guess: $72.39 Truth: $45.67 Error: $26.72 SLE: 0.20 Item: YANGHUAN Unstable Unicorns Adventure Car...\u001b[0m\n",
+      "\u001b[92m198: Guess: $227.17 Truth: $249.00 Error: $21.83 SLE: 0.01 Item: Interlogix NX-1820E NetworX Touch Screen...\u001b[0m\n",
+      "\u001b[91m199: Guess: $189.59 Truth: $42.99 Error: $146.60 SLE: 2.15 Item: Steering Damper,Universal Motorcycle Han...\u001b[0m\n",
+      "\u001b[92m200: Guess: $203.70 Truth: $181.33 Error: $22.37 SLE: 0.01 Item: Amprobe TIC 410A Hot Stick Attachment\u001b[0m\n",
+      "\u001b[91m201: Guess: $124.43 Truth: $6.03 Error: $118.40 SLE: 8.30 Item: MyCableMart 3.5mm Plug/Jack, 4 Conductor...\u001b[0m\n",
+      "\u001b[93m202: Guess: $85.76 Truth: $29.99 Error: $55.77 SLE: 1.06 Item: OtterBox + Pop Symmetry Series Case for ...\u001b[0m\n",
+      "\u001b[91m203: Guess: $480.64 Truth: $899.00 Error: $418.36 SLE: 0.39 Item: Dell XPS X8700-1572BLK Desktop ( Intel C...\u001b[0m\n",
+      "\u001b[93m204: Guess: $252.46 Truth: $399.99 Error: $147.53 SLE: 0.21 Item: Franklin Iron Works Sperry Industrial Br...\u001b[0m\n",
+      "\u001b[91m205: Guess: $109.06 Truth: $4.66 Error: $104.40 SLE: 8.81 Item: Avery Legal Dividers, Standard Collated ...\u001b[0m\n",
+      "\u001b[92m206: Guess: $235.94 Truth: $261.41 Error: $25.47 SLE: 0.01 Item: Moen 8346 Commercial Posi-Temp Pressure ...\u001b[0m\n",
+      "\u001b[91m207: Guess: $218.78 Truth: $136.97 Error: $81.81 SLE: 0.22 Item: Carlisle Versa Trail ATR All Terrain Rad...\u001b[0m\n",
+      "\u001b[91m208: Guess: $170.94 Truth: $79.00 Error: $91.94 SLE: 0.59 Item: SUNWAYFOTO 44mm Tripod Ball Head Arca Co...\u001b[0m\n",
+      "\u001b[91m209: Guess: $239.13 Truth: $444.99 Error: $205.86 SLE: 0.38 Item: NanoBeam AC NBE-5AC-Gen2-US 4 Units 5GHz...\u001b[0m\n",
+      "\u001b[93m210: Guess: $277.91 Truth: $411.94 Error: $134.03 SLE: 0.15 Item: WULF 4\" Front 2\" Rear Leveling Lift Kit ...\u001b[0m\n",
+      "\u001b[93m211: Guess: $212.26 Truth: $148.40 Error: $63.86 SLE: 0.13 Item: Alera ALEVABFMC Valencia Series Mobile B...\u001b[0m\n",
+      "\u001b[91m212: Guess: $121.62 Truth: $244.99 Error: $123.37 SLE: 0.48 Item: YU-GI-OH! Ignition Assault Booster Box\u001b[0m\n",
+      "\u001b[92m213: Guess: $123.70 Truth: $86.50 Error: $37.20 SLE: 0.13 Item: 48\" x 36\" Extra-Large Framed Magnetic Bl...\u001b[0m\n",
+      "\u001b[92m214: Guess: $305.07 Truth: $297.95 Error: $7.12 SLE: 0.00 Item: Dell Latitude D620 Renewed Notebook PC\u001b[0m\n",
+      "\u001b[91m215: Guess: $564.31 Truth: $399.99 Error: $164.32 SLE: 0.12 Item: acer Aspire 5 Laptop, AMD Ryzen 3 5300U ...\u001b[0m\n",
+      "\u001b[91m216: Guess: $249.27 Truth: $599.00 Error: $349.73 SLE: 0.76 Item: Elk 31080/6RC-GRN 30 by 6-Inch Viva 6-Li...\u001b[0m\n",
+      "\u001b[91m217: Guess: $239.21 Truth: $105.99 Error: $133.22 SLE: 0.65 Item: Barbie Top Model Doll\u001b[0m\n",
+      "\u001b[91m218: Guess: $348.61 Truth: $689.00 Error: $340.39 SLE: 0.46 Item: Danby Designer 20-In. Electric Range wit...\u001b[0m\n",
+      "\u001b[92m219: Guess: $337.12 Truth: $404.99 Error: $67.87 SLE: 0.03 Item: FixtureDisplays® Metal Truss Podium Doub...\u001b[0m\n",
+      "\u001b[92m220: Guess: $234.84 Truth: $207.76 Error: $27.08 SLE: 0.01 Item: ACDelco 13597235 GM Original Equipment A...\u001b[0m\n",
+      "\u001b[92m221: Guess: $191.47 Truth: $171.82 Error: $19.65 SLE: 0.01 Item: EBC S1KF1135 Stage-1 Premium Street Brak...\u001b[0m\n",
+      "\u001b[92m222: Guess: $304.47 Truth: $293.24 Error: $11.23 SLE: 0.00 Item: FXR Men's Boost FX Jacket (Black/Orange/...\u001b[0m\n",
+      "\u001b[93m223: Guess: $277.57 Truth: $374.95 Error: $97.38 SLE: 0.09 Item: SuperATV Scratch Resistant 3-in-1 Flip W...\u001b[0m\n",
+      "\u001b[93m224: Guess: $180.27 Truth: $111.99 Error: $68.28 SLE: 0.22 Item: SBU 3 Layer All Weather Mini Van Car Cov...\u001b[0m\n",
+      "\u001b[93m225: Guess: $103.97 Truth: $42.99 Error: $60.98 SLE: 0.76 Item: 2 Pack Outdoor Brochure Holder Advertisi...\u001b[0m\n",
+      "\u001b[92m226: Guess: $126.29 Truth: $116.71 Error: $9.58 SLE: 0.01 Item: Monroe Shocks & Struts Quick-Strut 17158...\u001b[0m\n",
+      "\u001b[93m227: Guess: $191.43 Truth: $118.61 Error: $72.82 SLE: 0.23 Item: Elements of Design Magellan EB235AL Thre...\u001b[0m\n",
+      "\u001b[93m228: Guess: $201.35 Truth: $147.12 Error: $54.23 SLE: 0.10 Item: GM Genuine Parts 15-62961 Air Conditioni...\u001b[0m\n",
+      "\u001b[91m229: Guess: $227.66 Truth: $119.99 Error: $107.67 SLE: 0.41 Item: Baseus 17-in-1 USB C Docking Station to ...\u001b[0m\n",
+      "\u001b[91m230: Guess: $207.65 Truth: $369.98 Error: $162.33 SLE: 0.33 Item: Whitehall™ Personalized Whitehall Capito...\u001b[0m\n",
+      "\u001b[92m231: Guess: $270.47 Truth: $315.55 Error: $45.08 SLE: 0.02 Item: Pro Circuit Works Pipe PY05250 for 02-19...\u001b[0m\n",
+      "\u001b[93m232: Guess: $264.43 Truth: $190.99 Error: $73.44 SLE: 0.10 Item: HYANKA 15 \"1200W Professional DJ Speaker...\u001b[0m\n",
+      "\u001b[92m233: Guess: $181.43 Truth: $155.00 Error: $26.43 SLE: 0.02 Item: Bluetooth X6BT Card Reader Writer Encode...\u001b[0m\n",
+      "\u001b[92m234: Guess: $321.29 Truth: $349.99 Error: $28.70 SLE: 0.01 Item: AIRAID Cold Air Intake System by K&N: In...\u001b[0m\n",
+      "\u001b[93m235: Guess: $300.28 Truth: $249.99 Error: $50.29 SLE: 0.03 Item: Bostingner Shower Faucets Sets Complete,...\u001b[0m\n",
+      "\u001b[91m236: Guess: $274.69 Truth: $42.99 Error: $231.70 SLE: 3.37 Item: PIT66 Front Bumper Turn Signal Lights, C...\u001b[0m\n",
+      "\u001b[92m237: Guess: $31.56 Truth: $17.99 Error: $13.57 SLE: 0.29 Item: Caseology Bumpy Compatible with Google P...\u001b[0m\n",
+      "\u001b[91m238: Guess: $217.25 Truth: $425.00 Error: $207.75 SLE: 0.45 Item: Fleck 2510 Timer Mechanical Filter Contr...\u001b[0m\n",
+      "\u001b[92m239: Guess: $273.88 Truth: $249.99 Error: $23.89 SLE: 0.01 Item: Haloview MC7108 Wireless RV Backup Camer...\u001b[0m\n",
+      "\u001b[93m240: Guess: $179.25 Truth: $138.23 Error: $41.02 SLE: 0.07 Item: Schmidt Spiele - Manhattan\u001b[0m\n",
+      "\u001b[91m241: Guess: $191.36 Truth: $414.99 Error: $223.63 SLE: 0.59 Item: Corsa 14333 Tip Kit (Ford Mustang GT)\u001b[0m\n",
+      "\u001b[92m242: Guess: $195.79 Truth: $168.28 Error: $27.51 SLE: 0.02 Item: Hoshizaki FM116A Fan Motor Kit 1\u001b[0m\n",
+      "\u001b[92m243: Guess: $212.44 Truth: $199.99 Error: $12.45 SLE: 0.00 Item: BAINUO Antler Chandelier Lighting,6 Ligh...\u001b[0m\n",
+      "\u001b[91m244: Guess: $231.50 Truth: $126.70 Error: $104.80 SLE: 0.36 Item: DNA MOTORING HL-OH-FEXP06-SM-AM Smoke Le...\u001b[0m\n",
+      "\u001b[91m245: Guess: $167.98 Truth: $5.91 Error: $162.07 SLE: 10.22 Item: Wera Stainless 3840/1 TS 2.5mm Hex Inser...\u001b[0m\n",
+      "\u001b[92m246: Guess: $221.62 Truth: $193.06 Error: $28.56 SLE: 0.02 Item: Celestron - PowerSeeker 127EQ Telescope ...\u001b[0m\n",
+      "\u001b[92m247: Guess: $285.70 Truth: $249.99 Error: $35.71 SLE: 0.02 Item: NHOPEEW 10.1inch Android Car Radio Carpl...\u001b[0m\n",
+      "\u001b[91m248: Guess: $254.74 Truth: $64.12 Error: $190.62 SLE: 1.87 Item: Other Harmonica (Suzuki-2Timer24- A)\u001b[0m\n",
+      "\u001b[91m249: Guess: $243.62 Truth: $114.99 Error: $128.63 SLE: 0.56 Item: Harley Air Filter Venturi Intake Air Cle...\u001b[0m\n",
+      "\u001b[91m250: Guess: $321.61 Truth: $926.00 Error: $604.39 SLE: 1.11 Item: Elite Screens Edge Free Ambient Light Re...\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Tester.test(rf, test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "bd895ac9-2814-469c-9d3d-c94e3f42003d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gb(item):\n",
+    "    return gradient_boosting.price(description(item))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "00112510-9634-4164-99b1-9dc7c39f5232",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m1: Guess: $278.30 Truth: $374.41 Error: $96.11 SLE: 0.09 Item: OEM AC Compressor w/A/C Repair Kit For F...\u001b[0m\n",
+      "\u001b[92m2: Guess: $203.69 Truth: $225.11 Error: $21.42 SLE: 0.01 Item: Motorcraft YB3125 Fan Clutch\u001b[0m\n",
+      "\u001b[91m3: Guess: $282.90 Truth: $61.68 Error: $221.22 SLE: 2.28 Item: Dorman 603-159 Front Washer Fluid Reserv...\u001b[0m\n",
+      "\u001b[91m4: Guess: $273.28 Truth: $599.99 Error: $326.71 SLE: 0.62 Item: HP Premium 17.3-inch HD Plus Touchscreen...\u001b[0m\n",
+      "\u001b[91m5: Guess: $215.50 Truth: $16.99 Error: $198.51 SLE: 6.19 Item: 5-Position Super Switch Pickup Selector ...\u001b[0m\n",
+      "\u001b[91m6: Guess: $119.24 Truth: $31.99 Error: $87.25 SLE: 1.67 Item: Horror Bookmarks, Resin Horror Bookmarks...\u001b[0m\n",
+      "\u001b[91m7: Guess: $291.20 Truth: $101.79 Error: $189.41 SLE: 1.09 Item: SK6241 - Stinger 4 Gauge 6000 Series Pow...\u001b[0m\n",
+      "\u001b[92m8: Guess: $264.52 Truth: $289.00 Error: $24.48 SLE: 0.01 Item: Godox ML60Bi LED Light Kit, Handheld LED...\u001b[0m\n",
+      "\u001b[91m9: Guess: $324.30 Truth: $635.86 Error: $311.56 SLE: 0.45 Item: Randall RG75DG3PLUS G3 Plus 100-Watt Com...\u001b[0m\n",
+      "\u001b[91m10: Guess: $215.60 Truth: $65.99 Error: $149.61 SLE: 1.38 Item: HOLDWILL 6 Pack LED Shop Light, 4FT 24W ...\u001b[0m\n",
+      "\u001b[92m11: Guess: $277.66 Truth: $254.21 Error: $23.45 SLE: 0.01 Item: Viking Horns V103C/1005ATK 3 Gallon Air ...\u001b[0m\n",
+      "\u001b[91m12: Guess: $233.92 Truth: $412.99 Error: $179.07 SLE: 0.32 Item: CURT 70110 Custom Tow Bar Base Plate Bra...\u001b[0m\n",
+      "\u001b[93m13: Guess: $124.08 Truth: $205.50 Error: $81.42 SLE: 0.25 Item: 10-Pack Solar HAMMERED BRONZE Finish Pos...\u001b[0m\n",
+      "\u001b[92m14: Guess: $280.70 Truth: $248.23 Error: $32.47 SLE: 0.01 Item: COSTWAY Electric Tumble Dryer, Sliver\u001b[0m\n",
+      "\u001b[93m15: Guess: $261.35 Truth: $399.00 Error: $137.65 SLE: 0.18 Item: FREE SIGNAL TV Transit 32\" 12 Volt DC Po...\u001b[0m\n",
+      "\u001b[92m16: Guess: $339.87 Truth: $373.94 Error: $34.07 SLE: 0.01 Item: Bilstein 5100 Monotube Gas Shock Set com...\u001b[0m\n",
+      "\u001b[91m17: Guess: $235.63 Truth: $92.89 Error: $142.74 SLE: 0.85 Item: Sangean K-200 Multi-Function Upright AM/...\u001b[0m\n",
+      "\u001b[93m18: Guess: $116.94 Truth: $51.99 Error: $64.95 SLE: 0.64 Item: Charles Leonard Magnetic Lapboard Class ...\u001b[0m\n",
+      "\u001b[91m19: Guess: $361.21 Truth: $179.00 Error: $182.21 SLE: 0.49 Item: Gigabyte AMD Radeon HD 7870 2 GB GDDR5 D...\u001b[0m\n",
+      "\u001b[93m20: Guess: $82.58 Truth: $19.42 Error: $63.16 SLE: 1.99 Item: 3dRose LLC 8 x 8 x 0.25 Inches Bull Terr...\u001b[0m\n",
+      "\u001b[91m21: Guess: $242.43 Truth: $539.95 Error: $297.52 SLE: 0.64 Item: ROKINON 85mm F1.4 Auto Focus Full Frame ...\u001b[0m\n",
+      "\u001b[93m22: Guess: $203.82 Truth: $147.67 Error: $56.15 SLE: 0.10 Item: AUTOSAVER88 Headlight Assembly Compatibl...\u001b[0m\n",
+      "\u001b[91m23: Guess: $142.76 Truth: $24.99 Error: $117.77 SLE: 2.93 Item: ASI NAUTICAL 2.5 Inches Opera Glasses Bi...\u001b[0m\n",
+      "\u001b[91m24: Guess: $291.76 Truth: $149.00 Error: $142.76 SLE: 0.45 Item: Behringer TUBE OVERDRIVE TO100 Authentic...\u001b[0m\n",
+      "\u001b[92m25: Guess: $26.46 Truth: $16.99 Error: $9.47 SLE: 0.18 Item: Fun Express Insect Finger Puppets - 24 f...\u001b[0m\n",
+      "\u001b[93m26: Guess: $52.18 Truth: $7.99 Error: $44.19 SLE: 3.16 Item: WAFJAMF Roller Stamp Identity Theft Stam...\u001b[0m\n",
+      "\u001b[92m27: Guess: $185.96 Truth: $199.99 Error: $14.03 SLE: 0.01 Item: Capulina Tiffany Floor Lamp 2-Light 16\" ...\u001b[0m\n",
+      "\u001b[92m28: Guess: $245.77 Truth: $251.45 Error: $5.68 SLE: 0.00 Item: Apple Watch Series 6 (GPS, 44mm) - Space...\u001b[0m\n",
+      "\u001b[92m29: Guess: $242.42 Truth: $231.62 Error: $10.80 SLE: 0.00 Item: ICON 01725 Tandem Axle Fender Skirt FS17...\u001b[0m\n",
+      "\u001b[93m30: Guess: $192.60 Truth: $135.00 Error: $57.60 SLE: 0.12 Item: SanDisk 128GB Ultra (10 Pack) MicroSD Cl...\u001b[0m\n",
+      "\u001b[93m31: Guess: $230.39 Truth: $356.62 Error: $126.23 SLE: 0.19 Item: Velvac 2020,L,C/Hr,W,E2003,102\",Bk - 715...\u001b[0m\n",
+      "\u001b[92m32: Guess: $231.54 Truth: $257.99 Error: $26.45 SLE: 0.01 Item: TCMT Passenger Backrest Sissy Bar & Lugg...\u001b[0m\n",
+      "\u001b[91m33: Guess: $208.87 Truth: $27.99 Error: $180.88 SLE: 3.92 Item: Alnicov 63.5MM Brass Tremolo Block，Tremo...\u001b[0m\n",
+      "\u001b[91m34: Guess: $255.85 Truth: $171.20 Error: $84.65 SLE: 0.16 Item: Subaru Forester Outback Legacy OEM Engin...\u001b[0m\n",
+      "\u001b[93m35: Guess: $172.11 Truth: $225.00 Error: $52.89 SLE: 0.07 Item: Richmond Auto Upholstery - 2012 Dodge Ra...\u001b[0m\n",
+      "\u001b[91m36: Guess: $206.53 Truth: $105.00 Error: $101.53 SLE: 0.45 Item: AP-39 Automotive Paint Primer Grey 2K Ur...\u001b[0m\n",
+      "\u001b[92m37: Guess: $264.67 Truth: $299.99 Error: $35.32 SLE: 0.02 Item: Road Top Wireless Carplay Retrofit Kit D...\u001b[0m\n",
+      "\u001b[92m38: Guess: $438.38 Truth: $535.09 Error: $96.71 SLE: 0.04 Item: Gibson Performance Exhaust 5658 Aluminiz...\u001b[0m\n",
+      "\u001b[91m39: Guess: $137.75 Truth: $12.33 Error: $125.42 SLE: 5.49 Item: Bella Tunno Happy Links - Baby Montessor...\u001b[0m\n",
+      "\u001b[91m40: Guess: $201.92 Truth: $84.99 Error: $116.93 SLE: 0.74 Item: CANMORE H300 Handheld GPS Golf Device, S...\u001b[0m\n",
+      "\u001b[93m41: Guess: $88.92 Truth: $15.99 Error: $72.93 SLE: 2.78 Item: DCPOWER AC Adapter Compatible Replacemen...\u001b[0m\n",
+      "\u001b[91m42: Guess: $168.94 Truth: $62.44 Error: $106.50 SLE: 0.97 Item: Sharp, VX2128V, Commercial Desktop Calcu...\u001b[0m\n",
+      "\u001b[92m43: Guess: $63.30 Truth: $82.99 Error: $19.69 SLE: 0.07 Item: Melissa & Doug Lifelike Plush Stork Gian...\u001b[0m\n",
+      "\u001b[91m44: Guess: $315.57 Truth: $599.95 Error: $284.38 SLE: 0.41 Item: Sony SSCS8 2-Way 3-Driver Center Channel...\u001b[0m\n",
+      "\u001b[91m45: Guess: $350.86 Truth: $194.99 Error: $155.87 SLE: 0.34 Item: ASUS Chromebook CX1, 14\" Full HD NanoEdg...\u001b[0m\n",
+      "\u001b[93m46: Guess: $244.32 Truth: $344.95 Error: $100.63 SLE: 0.12 Item: FiiO X7 32GB Hi-Res Lossless Music Playe...\u001b[0m\n",
+      "\u001b[92m47: Guess: $42.76 Truth: $37.99 Error: $4.77 SLE: 0.01 Item: TORRO Leather Case Compatible with iPhon...\u001b[0m\n",
+      "\u001b[92m48: Guess: $220.75 Truth: $224.35 Error: $3.60 SLE: 0.00 Item: Universal Air Conditioner KT 1031 A/C Co...\u001b[0m\n",
+      "\u001b[91m49: Guess: $406.33 Truth: $814.00 Error: $407.67 SLE: 0.48 Item: Street Series Stainless Performance Cat-...\u001b[0m\n",
+      "\u001b[92m50: Guess: $444.27 Truth: $439.88 Error: $4.39 SLE: 0.00 Item: Lenovo IdeaPad 3 14-inch Laptop, 14.0-in...\u001b[0m\n",
+      "\u001b[93m51: Guess: $251.37 Truth: $341.43 Error: $90.06 SLE: 0.09 Item: Access Bed Covers TonnoSport 22050219 - ...\u001b[0m\n",
+      "\u001b[91m52: Guess: $172.66 Truth: $46.78 Error: $125.88 SLE: 1.67 Item: G.I. JOE Hasbro 3 3/4\" Wave 5 Action Fig...\u001b[0m\n",
+      "\u001b[92m53: Guess: $173.71 Truth: $171.44 Error: $2.27 SLE: 0.00 Item: T&S Brass B-0232-BST Double Pantry Fauce...\u001b[0m\n",
+      "\u001b[91m54: Guess: $214.37 Truth: $458.00 Error: $243.63 SLE: 0.57 Item: ZTUOAUMA Fuel Injection Pump 3090942 309...\u001b[0m\n",
+      "\u001b[91m55: Guess: $300.93 Truth: $130.75 Error: $170.18 SLE: 0.69 Item: 2AP18AA#ABA Hp Prime Graphing Calculator...\u001b[0m\n",
+      "\u001b[93m56: Guess: $163.77 Truth: $83.81 Error: $79.96 SLE: 0.44 Item: Lowrance 000-0119-83 Nmea 2000 25' Exten...\u001b[0m\n",
+      "\u001b[91m57: Guess: $158.84 Truth: $386.39 Error: $227.55 SLE: 0.78 Item: Jeep Genuine Accessories 82213051 Hood L...\u001b[0m\n",
+      "\u001b[93m58: Guess: $213.25 Truth: $169.00 Error: $44.25 SLE: 0.05 Item: GODOX CB-06 Hard Carrying Case with Whee...\u001b[0m\n",
+      "\u001b[91m59: Guess: $156.45 Truth: $17.95 Error: $138.50 SLE: 4.48 Item: Au-Tomotive Gold, INC. Ford Black Valet ...\u001b[0m\n",
+      "\u001b[92m60: Guess: $256.86 Truth: $269.00 Error: $12.14 SLE: 0.00 Item: Snailfly Black Roof Rack Rail + Cross Ba...\u001b[0m\n",
+      "\u001b[91m61: Guess: $248.44 Truth: $77.77 Error: $170.67 SLE: 1.33 Item: KING SHA Anti Glare LED Track Lighting H...\u001b[0m\n",
+      "\u001b[91m62: Guess: $258.29 Truth: $88.99 Error: $169.30 SLE: 1.12 Item: APS Compatible with Chevy Silverado 1500...\u001b[0m\n",
+      "\u001b[93m63: Guess: $290.76 Truth: $364.41 Error: $73.65 SLE: 0.05 Item: Wilwood Engineering 14011291R Brake Cali...\u001b[0m\n",
+      "\u001b[91m64: Guess: $228.11 Truth: $127.03 Error: $101.08 SLE: 0.34 Item: ACDelco Gold 336-1925A Starter, Remanufa...\u001b[0m\n",
+      "\u001b[91m65: Guess: $323.48 Truth: $778.95 Error: $455.47 SLE: 0.77 Item: UWS EC10783 69-Inch Matte Black Heavy-Wa...\u001b[0m\n",
+      "\u001b[91m66: Guess: $478.90 Truth: $206.66 Error: $272.24 SLE: 0.70 Item: Dell Latitude E5440 14in Business Laptop...\u001b[0m\n",
+      "\u001b[91m67: Guess: $158.53 Truth: $35.94 Error: $122.59 SLE: 2.14 Item: (Plug and Play) Spare Tire Brake Light W...\u001b[0m\n",
+      "\u001b[91m68: Guess: $253.81 Truth: $149.00 Error: $104.81 SLE: 0.28 Item: The Ultimate Roadside Rescue Assistant\u001b[0m\n",
+      "\u001b[93m69: Guess: $197.02 Truth: $251.98 Error: $54.96 SLE: 0.06 Item: Brand New 18\" x 8.5\" Replacement Wheel f...\u001b[0m\n",
+      "\u001b[92m70: Guess: $180.50 Truth: $160.00 Error: $20.50 SLE: 0.01 Item: Headlight Headlamp LH Left & RH Right Pa...\u001b[0m\n",
+      "\u001b[91m71: Guess: $137.45 Truth: $39.99 Error: $97.46 SLE: 1.48 Item: Lilo And Stitch Deluxe Oversize Print La...\u001b[0m\n",
+      "\u001b[91m72: Guess: $191.79 Truth: $362.41 Error: $170.62 SLE: 0.40 Item: AC Compressor & A/C Clutch For Hyundai A...\u001b[0m\n",
+      "\u001b[93m73: Guess: $208.88 Truth: $344.00 Error: $135.12 SLE: 0.25 Item: House Of Troy PIN475-AB Pinnacle Collect...\u001b[0m\n",
+      "\u001b[91m74: Guess: $233.11 Truth: $25.09 Error: $208.02 SLE: 4.81 Item: Juno T29 WH Floating Electrical Feed Sin...\u001b[0m\n",
+      "\u001b[93m75: Guess: $233.16 Truth: $175.95 Error: $57.21 SLE: 0.08 Item: Sherman GO-PARTS - for 2013-2016 Toyota ...\u001b[0m\n",
+      "\u001b[93m76: Guess: $182.09 Truth: $132.64 Error: $49.45 SLE: 0.10 Item: Roland RPU-3 Electronic Keyboard Pedal o...\u001b[0m\n",
+      "\u001b[91m77: Guess: $219.68 Truth: $422.99 Error: $203.31 SLE: 0.43 Item: Rockland VMI14 12,000 Pound 12 Volt DC E...\u001b[0m\n",
+      "\u001b[91m78: Guess: $232.99 Truth: $146.48 Error: $86.51 SLE: 0.21 Item: Max Advanced Brakes Elite XDS Front Cros...\u001b[0m\n",
+      "\u001b[93m79: Guess: $221.02 Truth: $156.83 Error: $64.19 SLE: 0.12 Item: Quality-Built 11030 Premium Quality Alte...\u001b[0m\n",
+      "\u001b[92m80: Guess: $222.74 Truth: $251.99 Error: $29.25 SLE: 0.02 Item: Lucida LG-510 Student Classical Guitar, ...\u001b[0m\n",
+      "\u001b[91m81: Guess: $194.18 Truth: $940.33 Error: $746.15 SLE: 2.48 Item: Longacre 52-79800 Aluminum Turn Plates\u001b[0m\n",
+      "\u001b[91m82: Guess: $258.16 Truth: $52.99 Error: $205.17 SLE: 2.46 Item: Motion Pro 08-0380 Adjustable Torque Wre...\u001b[0m\n",
+      "\u001b[92m83: Guess: $187.29 Truth: $219.95 Error: $32.66 SLE: 0.03 Item: Glyph Thunderbolt 3 NVMe Dock (0 GB)\u001b[0m\n",
+      "\u001b[91m84: Guess: $162.35 Truth: $441.03 Error: $278.68 SLE: 0.99 Item: TOYO Open Country MT Performance Radial ...\u001b[0m\n",
+      "\u001b[91m85: Guess: $335.47 Truth: $168.98 Error: $166.49 SLE: 0.47 Item: Razer Seiren X USB Streaming Microphone ...\u001b[0m\n",
+      "\u001b[91m86: Guess: $83.21 Truth: $2.49 Error: $80.72 SLE: 10.13 Item: Happy Birthday to Dad From Your Daughter...\u001b[0m\n",
+      "\u001b[93m87: Guess: $153.26 Truth: $98.62 Error: $54.64 SLE: 0.19 Item: Little Tikes My Real Jam First Concert S...\u001b[0m\n",
+      "\u001b[93m88: Guess: $184.29 Truth: $256.95 Error: $72.66 SLE: 0.11 Item: Studio M Peace and Harmony Art Pole Comm...\u001b[0m\n",
+      "\u001b[91m89: Guess: $153.86 Truth: $30.99 Error: $122.87 SLE: 2.49 Item: MyVolts 12V Power Supply Adaptor Compati...\u001b[0m\n",
+      "\u001b[91m90: Guess: $322.84 Truth: $569.84 Error: $247.00 SLE: 0.32 Item: Dell Latitude 7212 Rugged Extreme Tablet...\u001b[0m\n",
+      "\u001b[93m91: Guess: $222.82 Truth: $177.99 Error: $44.83 SLE: 0.05 Item: Covermates Contour Fit Car Cover - Light...\u001b[0m\n",
+      "\u001b[91m92: Guess: $254.07 Truth: $997.99 Error: $743.92 SLE: 1.86 Item: Westin 57-4025 Black HDX Grille Guard fi...\u001b[0m\n",
+      "\u001b[92m93: Guess: $208.15 Truth: $219.00 Error: $10.85 SLE: 0.00 Item: Fieldpiece JL2 Job Link Wireless App Tra...\u001b[0m\n",
+      "\u001b[91m94: Guess: $331.26 Truth: $225.55 Error: $105.71 SLE: 0.15 Item: hansgrohe Talis S Modern Premium Easy Cl...\u001b[0m\n",
+      "\u001b[93m95: Guess: $317.71 Truth: $495.95 Error: $178.24 SLE: 0.20 Item: G-Technology G-SPEED eS PRO High-Perform...\u001b[0m\n",
+      "\u001b[91m96: Guess: $349.17 Truth: $942.37 Error: $593.20 SLE: 0.98 Item: DreamLine SHDR-1960723L-01 Shower Door, ...\u001b[0m\n",
+      "\u001b[91m97: Guess: $206.35 Truth: $1.94 Error: $204.41 SLE: 18.11 Item: Sanctuary Square Backplate Finish: Oiled...\u001b[0m\n",
+      "\u001b[93m98: Guess: $179.87 Truth: $284.34 Error: $104.47 SLE: 0.21 Item: Pelican Protector 1750 Long Case - Multi...\u001b[0m\n",
+      "\u001b[92m99: Guess: $199.60 Truth: $171.90 Error: $27.70 SLE: 0.02 Item: Brock Replacement Driver and Passenger H...\u001b[0m\n",
+      "\u001b[93m100: Guess: $192.47 Truth: $144.99 Error: $47.48 SLE: 0.08 Item: Carlinkit Ai Box Mini, Android 11, Multi...\u001b[0m\n",
+      "\u001b[91m101: Guess: $248.34 Truth: $470.47 Error: $222.13 SLE: 0.41 Item: StarDot NetCamLIVE2 YouTube Live Stream ...\u001b[0m\n",
+      "\u001b[91m102: Guess: $200.84 Truth: $66.95 Error: $133.89 SLE: 1.19 Item: Atomic Compatible FILXXCAR0016 16x25x5 M...\u001b[0m\n",
+      "\u001b[93m103: Guess: $157.29 Truth: $117.00 Error: $40.29 SLE: 0.09 Item: Bandai Awakening of S. H. s.h.figuarts s...\u001b[0m\n",
+      "\u001b[91m104: Guess: $303.85 Truth: $172.14 Error: $131.71 SLE: 0.32 Item: Fit System 62135G Passenger Side Towing ...\u001b[0m\n",
+      "\u001b[93m105: Guess: $279.38 Truth: $392.74 Error: $113.36 SLE: 0.12 Item: Black Horse Black Aluminum Exceed Runnin...\u001b[0m\n",
+      "\u001b[92m106: Guess: $50.35 Truth: $16.99 Error: $33.36 SLE: 1.10 Item: Dearsun Twinkle Star Color Night Light P...\u001b[0m\n",
+      "\u001b[91m107: Guess: $116.19 Truth: $1.34 Error: $114.85 SLE: 15.32 Item: Pokemon - Gallade Spirit Link (83/108) -...\u001b[0m\n",
+      "\u001b[92m108: Guess: $283.54 Truth: $349.98 Error: $66.44 SLE: 0.04 Item: Ibanez GA34STCE-NT GIO Series Classical ...\u001b[0m\n",
+      "\u001b[91m109: Guess: $179.12 Truth: $370.71 Error: $191.59 SLE: 0.52 Item: Set 2 Heavy Duty 12-16.5 12x16.5 12 Ply ...\u001b[0m\n",
+      "\u001b[91m110: Guess: $168.17 Truth: $65.88 Error: $102.29 SLE: 0.86 Item: Hairpin Table Legs 28\" Heavy Duty Hairpi...\u001b[0m\n",
+      "\u001b[92m111: Guess: $214.22 Truth: $229.99 Error: $15.77 SLE: 0.01 Item: Marada Racing Seat with Adjustable Slide...\u001b[0m\n",
+      "\u001b[91m112: Guess: $184.86 Truth: $9.14 Error: $175.72 SLE: 8.46 Item: Remington Industries 24UL1007STRWHI25 24...\u001b[0m\n",
+      "\u001b[91m113: Guess: $446.01 Truth: $199.00 Error: $247.01 SLE: 0.65 Item: Acer S3-391-6046 13.3-inch Ultrabook, In...\u001b[0m\n",
+      "\u001b[91m114: Guess: $217.84 Truth: $109.99 Error: $107.85 SLE: 0.46 Item: ICBEAMER 7\" RGB LED Headlights Bulb Halo...\u001b[0m\n",
+      "\u001b[91m115: Guess: $319.16 Truth: $570.42 Error: $251.26 SLE: 0.34 Item: R1 Concepts Front Rear Brakes and Rotors...\u001b[0m\n",
+      "\u001b[92m116: Guess: $238.37 Truth: $279.99 Error: $41.62 SLE: 0.03 Item: Camplux 2.64 GPM Tankless , Outdoor Port...\u001b[0m\n",
+      "\u001b[91m117: Guess: $135.15 Truth: $30.99 Error: $104.16 SLE: 2.10 Item: KNOKLOCK 10 Pack 3.75 Inch(96mm) Kitchen...\u001b[0m\n",
+      "\u001b[91m118: Guess: $205.67 Truth: $31.99 Error: $173.68 SLE: 3.37 Item: Valley Enterprises Yaesu USB FTDI CT-62 ...\u001b[0m\n",
+      "\u001b[91m119: Guess: $180.09 Truth: $15.90 Error: $164.19 SLE: 5.62 Item: G9 LED Light Bulbs，8W，75W 100W replaceme...\u001b[0m\n",
+      "\u001b[91m120: Guess: $135.07 Truth: $45.99 Error: $89.08 SLE: 1.13 Item: ZCHAOZ 4 Lights Antique White Farmhouse ...\u001b[0m\n",
+      "\u001b[93m121: Guess: $190.33 Truth: $113.52 Error: $76.81 SLE: 0.26 Item: Honeywell TH8320R1003 Honeywell VisionPr...\u001b[0m\n",
+      "\u001b[91m122: Guess: $283.89 Truth: $516.99 Error: $233.10 SLE: 0.36 Item: Patriot Exhaust H8013-1 1-7/8\" Clippster...\u001b[0m\n",
+      "\u001b[93m123: Guess: $254.18 Truth: $196.99 Error: $57.19 SLE: 0.06 Item: Fitrite Autopart New Front Left Driver S...\u001b[0m\n",
+      "\u001b[91m124: Guess: $168.77 Truth: $46.55 Error: $122.22 SLE: 1.62 Item: Technical Precision Replacement for GE G...\u001b[0m\n",
+      "\u001b[91m125: Guess: $182.05 Truth: $356.99 Error: $174.94 SLE: 0.45 Item: Covercraft Carhartt SeatSaver Front Row ...\u001b[0m\n",
+      "\u001b[91m126: Guess: $170.57 Truth: $319.95 Error: $149.38 SLE: 0.39 Item: Sennheiser SD Pro 2 (506008) - Double-Si...\u001b[0m\n",
+      "\u001b[91m127: Guess: $242.11 Truth: $96.06 Error: $146.05 SLE: 0.84 Item: Hitachi MAF0110 Mass Air Flow Sensor\u001b[0m\n",
+      "\u001b[93m128: Guess: $234.22 Truth: $190.99 Error: $43.23 SLE: 0.04 Item: AmScope SE305R-P-LED-PS36A 10X-30X LED C...\u001b[0m\n",
+      "\u001b[92m129: Guess: $217.96 Truth: $257.95 Error: $39.99 SLE: 0.03 Item: Front Left Driver Side Window Regulator ...\u001b[0m\n",
+      "\u001b[91m130: Guess: $143.89 Truth: $62.95 Error: $80.94 SLE: 0.67 Item: Premium Replica Hubcap Set, Fits Nissan ...\u001b[0m\n",
+      "\u001b[92m131: Guess: $81.00 Truth: $47.66 Error: $33.34 SLE: 0.27 Item: Excellerations Phonics Spelling Game for...\u001b[0m\n",
+      "\u001b[93m132: Guess: $310.50 Truth: $226.99 Error: $83.51 SLE: 0.10 Item: RC4WD BigDog Dual Axle Scale Car/Truck T...\u001b[0m\n",
+      "\u001b[93m133: Guess: $276.49 Truth: $359.95 Error: $83.46 SLE: 0.07 Item: Unknown Stage 2 Clutch Kit - Low Altitud...\u001b[0m\n",
+      "\u001b[91m134: Guess: $206.00 Truth: $78.40 Error: $127.60 SLE: 0.92 Item: 2002-2008 Dodge Ram 1500 Mopar 4X4 Emble...\u001b[0m\n",
+      "\u001b[93m135: Guess: $241.53 Truth: $172.77 Error: $68.76 SLE: 0.11 Item: Pro Comp Alloys Series 89 Wheel with Pol...\u001b[0m\n",
+      "\u001b[93m136: Guess: $211.23 Truth: $316.45 Error: $105.22 SLE: 0.16 Item: Detroit Axle - Front Rear Strut & Coil S...\u001b[0m\n",
+      "\u001b[91m137: Guess: $204.57 Truth: $87.99 Error: $116.58 SLE: 0.70 Item: ECCPP Rear Wheel Axle Replacement fit fo...\u001b[0m\n",
+      "\u001b[93m138: Guess: $286.85 Truth: $226.63 Error: $60.22 SLE: 0.06 Item: Dell Latitude E6520 Intel i7-2720QM 2.20...\u001b[0m\n",
+      "\u001b[91m139: Guess: $224.73 Truth: $31.49 Error: $193.24 SLE: 3.76 Item: F FIERCE CYCLE 251pcs Black Universal Mo...\u001b[0m\n",
+      "\u001b[92m140: Guess: $201.31 Truth: $196.00 Error: $5.31 SLE: 0.00 Item: Flash Furniture 4 Pk. HERCULES Series 88...\u001b[0m\n",
+      "\u001b[91m141: Guess: $217.56 Truth: $78.40 Error: $139.16 SLE: 1.03 Item: B&M 30287 Throttle Valve/Kickdown Cable,...\u001b[0m\n",
+      "\u001b[91m142: Guess: $259.01 Truth: $116.25 Error: $142.76 SLE: 0.63 Item: Gates TCK226 PowerGrip Premium Timing Be...\u001b[0m\n",
+      "\u001b[91m143: Guess: $244.45 Truth: $112.78 Error: $131.67 SLE: 0.59 Item: Monroe Shocks & Struts Quick-Strut 17149...\u001b[0m\n",
+      "\u001b[91m144: Guess: $157.73 Truth: $27.32 Error: $130.41 SLE: 2.97 Item: Feit Electric BPMR16/GU10/930CA/6 35W EQ...\u001b[0m\n",
+      "\u001b[92m145: Guess: $183.45 Truth: $145.91 Error: $37.54 SLE: 0.05 Item: Yellow Jacket 2806 Contractor Extension ...\u001b[0m\n",
+      "\u001b[92m146: Guess: $207.20 Truth: $171.09 Error: $36.11 SLE: 0.04 Item: Garage-Pro Tailgate SET Compatible with ...\u001b[0m\n",
+      "\u001b[93m147: Guess: $215.04 Truth: $167.95 Error: $47.09 SLE: 0.06 Item: 3M Perfect It Buffing and Polishing Kit ...\u001b[0m\n",
+      "\u001b[93m148: Guess: $97.34 Truth: $28.49 Error: $68.85 SLE: 1.45 Item: Chinese Style Dollhouse Model DIY Miniat...\u001b[0m\n",
+      "\u001b[93m149: Guess: $180.19 Truth: $122.23 Error: $57.96 SLE: 0.15 Item: Generic NRG Innovations SRK-161H Steerin...\u001b[0m\n",
+      "\u001b[91m150: Guess: $118.84 Truth: $32.99 Error: $85.85 SLE: 1.59 Item: Learning Resources Coding Critters Range...\u001b[0m\n",
+      "\u001b[91m151: Guess: $241.42 Truth: $71.20 Error: $170.22 SLE: 1.47 Item: Bosch Automotive 15463 Oxygen Sensor, OE...\u001b[0m\n",
+      "\u001b[92m152: Guess: $79.83 Truth: $112.75 Error: $32.92 SLE: 0.12 Item: Case of 24-2 Inch Blue Painters Tape - 6...\u001b[0m\n",
+      "\u001b[93m153: Guess: $190.48 Truth: $142.43 Error: $48.05 SLE: 0.08 Item: MOCA Engine Water Pump & Fan Clutch fit ...\u001b[0m\n",
+      "\u001b[91m154: Guess: $226.52 Truth: $398.99 Error: $172.47 SLE: 0.32 Item: SAREMAS Foot Step Bars for Hyundai Palis...\u001b[0m\n",
+      "\u001b[93m155: Guess: $291.36 Truth: $449.00 Error: $157.64 SLE: 0.19 Item: Gretsch G9210 Square Neck Boxcar Mahogan...\u001b[0m\n",
+      "\u001b[91m156: Guess: $311.20 Truth: $189.00 Error: $122.20 SLE: 0.25 Item: NikoMaku Mirror Dash Cam Front and Rear ...\u001b[0m\n",
+      "\u001b[91m157: Guess: $276.11 Truth: $120.91 Error: $155.20 SLE: 0.67 Item: Fenix HP25R v2.0 USB-C Rechargeable Head...\u001b[0m\n",
+      "\u001b[91m158: Guess: $315.32 Truth: $203.53 Error: $111.79 SLE: 0.19 Item: R&L Racing Heavy Duty Roll-Up Soft Tonne...\u001b[0m\n",
+      "\u001b[93m159: Guess: $250.39 Truth: $349.99 Error: $99.60 SLE: 0.11 Item: Garmin 010-02258-10 GPSMAP 64sx, Handhel...\u001b[0m\n",
+      "\u001b[92m160: Guess: $66.07 Truth: $34.35 Error: $31.72 SLE: 0.41 Item: Brown 5-7/8\" X 8-1/2\" X 3/16\" Thick Heav...\u001b[0m\n",
+      "\u001b[91m161: Guess: $166.61 Truth: $384.99 Error: $218.38 SLE: 0.70 Item: GAOMON PD2200 Pen Display & 20 Pen Nibs ...\u001b[0m\n",
+      "\u001b[91m162: Guess: $318.54 Truth: $211.00 Error: $107.54 SLE: 0.17 Item: VXMOTOR for 97-03 Ford F150/F250 Lightdu...\u001b[0m\n",
+      "\u001b[91m163: Guess: $429.09 Truth: $129.00 Error: $300.09 SLE: 1.43 Item: HP EliteBook 2540p Intel Core i7-640LM X...\u001b[0m\n",
+      "\u001b[92m164: Guess: $138.66 Truth: $111.45 Error: $27.21 SLE: 0.05 Item: Green EPX Mixing Nozzles 100-Pack-fits 3...\u001b[0m\n",
+      "\u001b[93m165: Guess: $146.67 Truth: $81.12 Error: $65.55 SLE: 0.34 Item: Box Partners 6 1/4 x 3 1/8\" 13 Pt. Manil...\u001b[0m\n",
+      "\u001b[93m166: Guess: $285.83 Truth: $457.08 Error: $171.25 SLE: 0.22 Item: Vixen Air 1/2\" NPT Air Ride Suspension H...\u001b[0m\n",
+      "\u001b[91m167: Guess: $186.51 Truth: $49.49 Error: $137.02 SLE: 1.72 Item: Smart Floor Lamp, 2700-6500K+RGBPink Mul...\u001b[0m\n",
+      "\u001b[91m168: Guess: $185.89 Truth: $80.56 Error: $105.33 SLE: 0.69 Item: SOZG 324mm Wheelbase Body Shell RC Car B...\u001b[0m\n",
+      "\u001b[93m169: Guess: $200.21 Truth: $278.39 Error: $78.18 SLE: 0.11 Item: Mickey Thompson ET Street S/S Racing Rad...\u001b[0m\n",
+      "\u001b[91m170: Guess: $198.17 Truth: $364.50 Error: $166.33 SLE: 0.37 Item: Pirelli 275/40R20 106W XL RFT P0 PZ4-LUX...\u001b[0m\n",
+      "\u001b[93m171: Guess: $272.21 Truth: $378.99 Error: $106.78 SLE: 0.11 Item: Torklift C3212 Rear Tie Down\u001b[0m\n",
+      "\u001b[91m172: Guess: $250.48 Truth: $165.28 Error: $85.20 SLE: 0.17 Item: Cardone 78-4226 Remanufactured Ford Comp...\u001b[0m\n",
+      "\u001b[91m173: Guess: $176.93 Truth: $56.74 Error: $120.19 SLE: 1.27 Item: Kidde AccessPoint 001798 Supra TouchPoin...\u001b[0m\n",
+      "\u001b[91m174: Guess: $177.01 Truth: $307.95 Error: $130.94 SLE: 0.30 Item: 3M Protecta 3100414 Self Retracting Life...\u001b[0m\n",
+      "\u001b[91m175: Guess: $196.60 Truth: $38.00 Error: $158.60 SLE: 2.63 Item: Plantronics 89435-01 Wired Headset, Blac...\u001b[0m\n",
+      "\u001b[91m176: Guess: $137.09 Truth: $53.00 Error: $84.09 SLE: 0.88 Item: Logitech K750 Wireless Solar Keyboard fo...\u001b[0m\n",
+      "\u001b[93m177: Guess: $356.04 Truth: $498.00 Error: $141.96 SLE: 0.11 Item: Olympus PEN E-PL9 Body Only with 3-Inch ...\u001b[0m\n",
+      "\u001b[91m178: Guess: $196.95 Truth: $53.99 Error: $142.96 SLE: 1.64 Item: Beck/Arnley 051-6066 Hub & Bearing Assem...\u001b[0m\n",
+      "\u001b[93m179: Guess: $220.11 Truth: $350.00 Error: $129.89 SLE: 0.21 Item: Eibach Pro-Kit Performance Springs E10-6...\u001b[0m\n",
+      "\u001b[93m180: Guess: $207.31 Truth: $299.95 Error: $92.64 SLE: 0.14 Item: LEGO DC Batman 1989 Batwing 76161 Displa...\u001b[0m\n",
+      "\u001b[91m181: Guess: $198.22 Truth: $94.93 Error: $103.29 SLE: 0.53 Item: Kingston Brass KS3608PL Restoration 4-In...\u001b[0m\n",
+      "\u001b[92m182: Guess: $322.76 Truth: $379.00 Error: $56.24 SLE: 0.03 Item: Polk Vanishing Series 265-LS In-Wall 3-W...\u001b[0m\n",
+      "\u001b[93m183: Guess: $218.40 Truth: $299.95 Error: $81.55 SLE: 0.10 Item: Spec-D Tuning LED Projector Headlights G...\u001b[0m\n",
+      "\u001b[92m184: Guess: $47.27 Truth: $24.99 Error: $22.28 SLE: 0.38 Item: RICHMOND & FINCH Airpod Pro Case, Green ...\u001b[0m\n",
+      "\u001b[91m185: Guess: $187.08 Truth: $41.04 Error: $146.04 SLE: 2.24 Item: LFA Industries 43B-5A-33JT 1/16-1/2-1.5-...\u001b[0m\n",
+      "\u001b[93m186: Guess: $201.16 Truth: $327.90 Error: $126.74 SLE: 0.24 Item: SAUTVS LED Headlight Assembly for Slings...\u001b[0m\n",
+      "\u001b[93m187: Guess: $73.01 Truth: $10.99 Error: $62.02 SLE: 3.31 Item: 2 Pack Combo Womens Safety Glasses Impac...\u001b[0m\n",
+      "\u001b[91m188: Guess: $143.08 Truth: $14.99 Error: $128.09 SLE: 4.83 Item: Arepa - Venezuelan cuisine - Venezuela P...\u001b[0m\n",
+      "\u001b[93m189: Guess: $148.89 Truth: $84.95 Error: $63.94 SLE: 0.31 Item: Schlage Lock Company KS23D2300 Padlock, ...\u001b[0m\n",
+      "\u001b[91m190: Guess: $221.71 Truth: $111.00 Error: $110.71 SLE: 0.47 Item: Techni Mobili White Sit to Stand Mobile ...\u001b[0m\n",
+      "\u001b[92m191: Guess: $89.23 Truth: $123.73 Error: $34.50 SLE: 0.10 Item: Special Lite Products Contemporary Wall ...\u001b[0m\n",
+      "\u001b[91m192: Guess: $231.98 Truth: $557.38 Error: $325.40 SLE: 0.76 Item: Tascam DP-24SD 24-Track Digital Portastu...\u001b[0m\n",
+      "\u001b[91m193: Guess: $223.09 Truth: $95.55 Error: $127.54 SLE: 0.71 Item: Glow Lighting 636CC10SP Vista Crystal Fl...\u001b[0m\n",
+      "\u001b[91m194: Guess: $286.77 Truth: $154.00 Error: $132.77 SLE: 0.38 Item: Z3 Wind Deflector, Smoke Tint, Lexan, Wi...\u001b[0m\n",
+      "\u001b[93m195: Guess: $263.81 Truth: $198.99 Error: $64.82 SLE: 0.08 Item: Olympus E-20 5MP Digital Camera w/ 4x Op...\u001b[0m\n",
+      "\u001b[91m196: Guess: $214.16 Truth: $430.44 Error: $216.28 SLE: 0.48 Item: PHYNEDI 1:1000 World Trade Center (1973-...\u001b[0m\n",
+      "\u001b[93m197: Guess: $103.21 Truth: $45.67 Error: $57.54 SLE: 0.65 Item: YANGHUAN Unstable Unicorns Adventure Car...\u001b[0m\n",
+      "\u001b[92m198: Guess: $205.72 Truth: $249.00 Error: $43.28 SLE: 0.04 Item: Interlogix NX-1820E NetworX Touch Screen...\u001b[0m\n",
+      "\u001b[91m199: Guess: $189.09 Truth: $42.99 Error: $146.10 SLE: 2.14 Item: Steering Damper,Universal Motorcycle Han...\u001b[0m\n",
+      "\u001b[92m200: Guess: $193.65 Truth: $181.33 Error: $12.32 SLE: 0.00 Item: Amprobe TIC 410A Hot Stick Attachment\u001b[0m\n",
+      "\u001b[91m201: Guess: $143.67 Truth: $6.03 Error: $137.64 SLE: 9.15 Item: MyCableMart 3.5mm Plug/Jack, 4 Conductor...\u001b[0m\n",
+      "\u001b[91m202: Guess: $111.76 Truth: $29.99 Error: $81.77 SLE: 1.67 Item: OtterBox + Pop Symmetry Series Case for ...\u001b[0m\n",
+      "\u001b[91m203: Guess: $531.68 Truth: $899.00 Error: $367.32 SLE: 0.28 Item: Dell XPS X8700-1572BLK Desktop ( Intel C...\u001b[0m\n",
+      "\u001b[93m204: Guess: $274.56 Truth: $399.99 Error: $125.43 SLE: 0.14 Item: Franklin Iron Works Sperry Industrial Br...\u001b[0m\n",
+      "\u001b[91m205: Guess: $133.58 Truth: $4.66 Error: $128.92 SLE: 10.04 Item: Avery Legal Dividers, Standard Collated ...\u001b[0m\n",
+      "\u001b[92m206: Guess: $258.49 Truth: $261.41 Error: $2.92 SLE: 0.00 Item: Moen 8346 Commercial Posi-Temp Pressure ...\u001b[0m\n",
+      "\u001b[91m207: Guess: $247.15 Truth: $136.97 Error: $110.18 SLE: 0.34 Item: Carlisle Versa Trail ATR All Terrain Rad...\u001b[0m\n",
+      "\u001b[91m208: Guess: $203.62 Truth: $79.00 Error: $124.62 SLE: 0.88 Item: SUNWAYFOTO 44mm Tripod Ball Head Arca Co...\u001b[0m\n",
+      "\u001b[93m209: Guess: $301.04 Truth: $444.99 Error: $143.95 SLE: 0.15 Item: NanoBeam AC NBE-5AC-Gen2-US 4 Units 5GHz...\u001b[0m\n",
+      "\u001b[93m210: Guess: $272.39 Truth: $411.94 Error: $139.55 SLE: 0.17 Item: WULF 4\" Front 2\" Rear Leveling Lift Kit ...\u001b[0m\n",
+      "\u001b[92m211: Guess: $168.37 Truth: $148.40 Error: $19.97 SLE: 0.02 Item: Alera ALEVABFMC Valencia Series Mobile B...\u001b[0m\n",
+      "\u001b[91m212: Guess: $145.44 Truth: $244.99 Error: $99.55 SLE: 0.27 Item: YU-GI-OH! Ignition Assault Booster Box\u001b[0m\n",
+      "\u001b[91m213: Guess: $173.32 Truth: $86.50 Error: $86.82 SLE: 0.48 Item: 48\" x 36\" Extra-Large Framed Magnetic Bl...\u001b[0m\n",
+      "\u001b[92m214: Guess: $316.82 Truth: $297.95 Error: $18.87 SLE: 0.00 Item: Dell Latitude D620 Renewed Notebook PC\u001b[0m\n",
+      "\u001b[93m215: Guess: $534.76 Truth: $399.99 Error: $134.77 SLE: 0.08 Item: acer Aspire 5 Laptop, AMD Ryzen 3 5300U ...\u001b[0m\n",
+      "\u001b[91m216: Guess: $203.98 Truth: $599.00 Error: $395.02 SLE: 1.15 Item: Elk 31080/6RC-GRN 30 by 6-Inch Viva 6-Li...\u001b[0m\n",
+      "\u001b[91m217: Guess: $258.75 Truth: $105.99 Error: $152.76 SLE: 0.79 Item: Barbie Top Model Doll\u001b[0m\n",
+      "\u001b[91m218: Guess: $300.72 Truth: $689.00 Error: $388.28 SLE: 0.68 Item: Danby Designer 20-In. Electric Range wit...\u001b[0m\n",
+      "\u001b[93m219: Guess: $270.29 Truth: $404.99 Error: $134.70 SLE: 0.16 Item: FixtureDisplays® Metal Truss Podium Doub...\u001b[0m\n",
+      "\u001b[92m220: Guess: $225.78 Truth: $207.76 Error: $18.02 SLE: 0.01 Item: ACDelco 13597235 GM Original Equipment A...\u001b[0m\n",
+      "\u001b[93m221: Guess: $220.83 Truth: $171.82 Error: $49.01 SLE: 0.06 Item: EBC S1KF1135 Stage-1 Premium Street Brak...\u001b[0m\n",
+      "\u001b[92m222: Guess: $274.91 Truth: $293.24 Error: $18.33 SLE: 0.00 Item: FXR Men's Boost FX Jacket (Black/Orange/...\u001b[0m\n",
+      "\u001b[93m223: Guess: $252.93 Truth: $374.95 Error: $122.02 SLE: 0.15 Item: SuperATV Scratch Resistant 3-in-1 Flip W...\u001b[0m\n",
+      "\u001b[91m224: Guess: $202.49 Truth: $111.99 Error: $90.50 SLE: 0.35 Item: SBU 3 Layer All Weather Mini Van Car Cov...\u001b[0m\n",
+      "\u001b[92m225: Guess: $69.11 Truth: $42.99 Error: $26.12 SLE: 0.22 Item: 2 Pack Outdoor Brochure Holder Advertisi...\u001b[0m\n",
+      "\u001b[91m226: Guess: $205.10 Truth: $116.71 Error: $88.39 SLE: 0.31 Item: Monroe Shocks & Struts Quick-Strut 17158...\u001b[0m\n",
+      "\u001b[91m227: Guess: $260.83 Truth: $118.61 Error: $142.22 SLE: 0.61 Item: Elements of Design Magellan EB235AL Thre...\u001b[0m\n",
+      "\u001b[93m228: Guess: $225.50 Truth: $147.12 Error: $78.38 SLE: 0.18 Item: GM Genuine Parts 15-62961 Air Conditioni...\u001b[0m\n",
+      "\u001b[91m229: Guess: $257.43 Truth: $119.99 Error: $137.44 SLE: 0.58 Item: Baseus 17-in-1 USB C Docking Station to ...\u001b[0m\n",
+      "\u001b[91m230: Guess: $86.38 Truth: $369.98 Error: $283.60 SLE: 2.09 Item: Whitehall™ Personalized Whitehall Capito...\u001b[0m\n",
+      "\u001b[92m231: Guess: $301.46 Truth: $315.55 Error: $14.09 SLE: 0.00 Item: Pro Circuit Works Pipe PY05250 for 02-19...\u001b[0m\n",
+      "\u001b[91m232: Guess: $302.66 Truth: $190.99 Error: $111.67 SLE: 0.21 Item: HYANKA 15 \"1200W Professional DJ Speaker...\u001b[0m\n",
+      "\u001b[92m233: Guess: $190.35 Truth: $155.00 Error: $35.35 SLE: 0.04 Item: Bluetooth X6BT Card Reader Writer Encode...\u001b[0m\n",
+      "\u001b[92m234: Guess: $328.06 Truth: $349.99 Error: $21.93 SLE: 0.00 Item: AIRAID Cold Air Intake System by K&N: In...\u001b[0m\n",
+      "\u001b[92m235: Guess: $250.34 Truth: $249.99 Error: $0.35 SLE: 0.00 Item: Bostingner Shower Faucets Sets Complete,...\u001b[0m\n",
+      "\u001b[91m236: Guess: $198.44 Truth: $42.99 Error: $155.45 SLE: 2.28 Item: PIT66 Front Bumper Turn Signal Lights, C...\u001b[0m\n",
+      "\u001b[93m237: Guess: $64.90 Truth: $17.99 Error: $46.91 SLE: 1.55 Item: Caseology Bumpy Compatible with Google P...\u001b[0m\n",
+      "\u001b[93m238: Guess: $265.44 Truth: $425.00 Error: $159.56 SLE: 0.22 Item: Fleck 2510 Timer Mechanical Filter Contr...\u001b[0m\n",
+      "\u001b[93m239: Guess: $318.67 Truth: $249.99 Error: $68.68 SLE: 0.06 Item: Haloview MC7108 Wireless RV Backup Camer...\u001b[0m\n",
+      "\u001b[93m240: Guess: $200.08 Truth: $138.23 Error: $61.85 SLE: 0.14 Item: Schmidt Spiele - Manhattan\u001b[0m\n",
+      "\u001b[93m241: Guess: $261.93 Truth: $414.99 Error: $153.06 SLE: 0.21 Item: Corsa 14333 Tip Kit (Ford Mustang GT)\u001b[0m\n",
+      "\u001b[93m242: Guess: $209.66 Truth: $168.28 Error: $41.38 SLE: 0.05 Item: Hoshizaki FM116A Fan Motor Kit 1\u001b[0m\n",
+      "\u001b[92m243: Guess: $171.20 Truth: $199.99 Error: $28.79 SLE: 0.02 Item: BAINUO Antler Chandelier Lighting,6 Ligh...\u001b[0m\n",
+      "\u001b[91m244: Guess: $226.91 Truth: $126.70 Error: $100.21 SLE: 0.34 Item: DNA MOTORING HL-OH-FEXP06-SM-AM Smoke Le...\u001b[0m\n",
+      "\u001b[91m245: Guess: $250.44 Truth: $5.91 Error: $244.53 SLE: 12.92 Item: Wera Stainless 3840/1 TS 2.5mm Hex Inser...\u001b[0m\n",
+      "\u001b[92m246: Guess: $213.52 Truth: $193.06 Error: $20.46 SLE: 0.01 Item: Celestron - PowerSeeker 127EQ Telescope ...\u001b[0m\n",
+      "\u001b[92m247: Guess: $261.82 Truth: $249.99 Error: $11.83 SLE: 0.00 Item: NHOPEEW 10.1inch Android Car Radio Carpl...\u001b[0m\n",
+      "\u001b[91m248: Guess: $223.29 Truth: $64.12 Error: $159.17 SLE: 1.53 Item: Other Harmonica (Suzuki-2Timer24- A)\u001b[0m\n",
+      "\u001b[91m249: Guess: $267.11 Truth: $114.99 Error: $152.12 SLE: 0.70 Item: Harley Air Filter Venturi Intake Air Cle...\u001b[0m\n",
+      "\u001b[91m250: Guess: $329.56 Truth: $926.00 Error: $596.44 SLE: 1.06 Item: Elite Screens Edge Free Ambient Light Re...\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Tester.test(gb, test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9f759bd2-7a7e-4c1a-80a0-e12470feca89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product = \"Quadcast HyperX condenser mic for high quality audio for podcasting\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e44dbd25-fb95-4b6b-bbbb-8da5fc817105",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "189.0\n",
+      "154.59\n",
+      "296.79030000000023\n",
+      "227.5062013552033\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(specialist.price(product))\n",
+    "print(frontier.price(product))\n",
+    "print(random_forest.price(product))\n",
+    "print(gradient_boosting.price(product))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1779b353-e2bb-4fc7-be7c-93057e4d688a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|▎                                                                                 | 1/250 [00:04<17:04,  4.11s/it]INFO:backoff:Backing off send_request(...) for 0.1s (requests.exceptions.ReadTimeout: HTTPSConnectionPool(host='us.i.posthog.com', port=443): Read timed out. (read timeout=15))\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████| 250/250 [15:38<00:00,  3.75s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "specialists = []\n",
+    "frontiers = []\n",
+    "random_forests = []\n",
+    "gradient_boostings = []\n",
+    "prices = []\n",
+    "for item in tqdm(test[1000:1250]):\n",
+    "    text = description(item)\n",
+    "    specialists.append(specialist.price(text))\n",
+    "    frontiers.append(frontier.price(text))\n",
+    "    random_forests.append(random_forest.price(text))\n",
+    "    gradient_boostings.append(gradient_boosting.price(text))\n",
+    "    prices.append(item.price)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f0bca725-4e34-405b-8d90-41d67086a25d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mins = [min(s,f,r,g) for s,f,r,g in zip(specialists, frontiers, random_forests, gradient_boostings)]\n",
+    "maxes = [max(s,f,r,g) for s,f,r,g in zip(specialists, frontiers, random_forests, gradient_boostings)]\n",
+    "\n",
+    "X = pd.DataFrame({\n",
+    "    'Specialist': specialists,\n",
+    "    'Frontier': frontiers,\n",
+    "    'RandomForest': random_forests,\n",
+    "    'GradientBoosting' : gradient_boostings,\n",
+    "    'Min': mins,\n",
+    "    'Max': maxes,\n",
+    "})\n",
+    "\n",
+    "# Convert y to a Series\n",
+    "y = pd.Series(prices)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "1be5be8a-3e7f-42a2-be54-0c7e380f7cc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Specialist: 0.75\n",
+      "Frontier: 0.45\n",
+      "RandomForest: -0.02\n",
+      "GradientBoosting: 0.09\n",
+      "Min: -0.15\n",
+      "Max: -0.16\n",
+      "Intercept=24.08\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train a Linear Regression\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "lr = LinearRegression()\n",
+    "lr.fit(X, y)\n",
+    "\n",
+    "feature_columns = X.columns.tolist()\n",
+    "\n",
+    "for feature, coef in zip(feature_columns, lr.coef_):\n",
+    "    print(f\"{feature}: {coef:.2f}\")\n",
+    "print(f\"Intercept={lr.intercept_:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "0bdf6e68-28a3-4ed2-b17e-de0ede923d34",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ensemble_model.pkl']"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "joblib.dump(lr, 'ensemble_model.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e762441a-9470-4dd7-8a8f-ec0430e908c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from agents.ensemble_agent import EnsembleAgent\n",
+    "ensemble = EnsembleAgent(collection)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "1a29f03c-8010-43b7-ae7d-1bc85ca6e8e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "177.20276709193624"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ensemble.price(product)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e6a5e226-a508-43d5-aa42-cefbde72ffdf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ensemble_pricer(item):\n",
+    "    return max(0,ensemble.price(description(item)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "8397b1ef-2ea3-4af8-bb34-36594e0600cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[92m1: Guess: $414.77 Truth: $374.41 Error: $40.36 SLE: 0.01 Item: OEM AC Compressor w/A/C Repair Kit For F...\u001b[0m\n",
+      "\u001b[92m2: Guess: $210.12 Truth: $225.11 Error: $14.99 SLE: 0.00 Item: Motorcraft YB3125 Fan Clutch\u001b[0m\n",
+      "\u001b[92m3: Guess: $50.48 Truth: $61.68 Error: $11.20 SLE: 0.04 Item: Dorman 603-159 Front Washer Fluid Reserv...\u001b[0m\n",
+      "\u001b[93m4: Guess: $380.50 Truth: $599.99 Error: $219.49 SLE: 0.21 Item: HP Premium 17.3-inch HD Plus Touchscreen...\u001b[0m\n",
+      "\u001b[92m5: Guess: $25.65 Truth: $16.99 Error: $8.66 SLE: 0.15 Item: 5-Position Super Switch Pickup Selector ...\u001b[0m\n",
+      "\u001b[92m6: Guess: $48.76 Truth: $31.99 Error: $16.77 SLE: 0.17 Item: Horror Bookmarks, Resin Horror Bookmarks...\u001b[0m\n",
+      "\u001b[92m7: Guess: $120.04 Truth: $101.79 Error: $18.25 SLE: 0.03 Item: SK6241 - Stinger 4 Gauge 6000 Series Pow...\u001b[0m\n",
+      "\u001b[92m8: Guess: $343.88 Truth: $289.00 Error: $54.88 SLE: 0.03 Item: Godox ML60Bi LED Light Kit, Handheld LED...\u001b[0m\n",
+      "\u001b[93m9: Guess: $867.46 Truth: $635.86 Error: $231.60 SLE: 0.10 Item: Randall RG75DG3PLUS G3 Plus 100-Watt Com...\u001b[0m\n",
+      "\u001b[92m10: Guess: $69.98 Truth: $65.99 Error: $3.99 SLE: 0.00 Item: HOLDWILL 6 Pack LED Shop Light, 4FT 24W ...\u001b[0m\n",
+      "\u001b[92m11: Guess: $246.95 Truth: $254.21 Error: $7.26 SLE: 0.00 Item: Viking Horns V103C/1005ATK 3 Gallon Air ...\u001b[0m\n",
+      "\u001b[92m12: Guess: $420.74 Truth: $412.99 Error: $7.75 SLE: 0.00 Item: CURT 70110 Custom Tow Bar Base Plate Bra...\u001b[0m\n",
+      "\u001b[92m13: Guess: $239.59 Truth: $205.50 Error: $34.09 SLE: 0.02 Item: 10-Pack Solar HAMMERED BRONZE Finish Pos...\u001b[0m\n",
+      "\u001b[92m14: Guess: $283.99 Truth: $248.23 Error: $35.76 SLE: 0.02 Item: COSTWAY Electric Tumble Dryer, Sliver\u001b[0m\n",
+      "\u001b[92m15: Guess: $329.95 Truth: $399.00 Error: $69.05 SLE: 0.04 Item: FREE SIGNAL TV Transit 32\" 12 Volt DC Po...\u001b[0m\n",
+      "\u001b[92m16: Guess: $370.01 Truth: $373.94 Error: $3.93 SLE: 0.00 Item: Bilstein 5100 Monotube Gas Shock Set com...\u001b[0m\n",
+      "\u001b[92m17: Guess: $124.73 Truth: $92.89 Error: $31.84 SLE: 0.09 Item: Sangean K-200 Multi-Function Upright AM/...\u001b[0m\n",
+      "\u001b[93m18: Guess: $105.55 Truth: $51.99 Error: $53.56 SLE: 0.49 Item: Charles Leonard Magnetic Lapboard Class ...\u001b[0m\n",
+      "\u001b[91m19: Guess: $287.70 Truth: $179.00 Error: $108.70 SLE: 0.22 Item: Gigabyte AMD Radeon HD 7870 2 GB GDDR5 D...\u001b[0m\n",
+      "\u001b[92m20: Guess: $37.60 Truth: $19.42 Error: $18.18 SLE: 0.41 Item: 3dRose LLC 8 x 8 x 0.25 Inches Bull Terr...\u001b[0m\n",
+      "\u001b[92m21: Guess: $488.72 Truth: $539.95 Error: $51.23 SLE: 0.01 Item: ROKINON 85mm F1.4 Auto Focus Full Frame ...\u001b[0m\n",
+      "\u001b[92m22: Guess: $153.75 Truth: $147.67 Error: $6.08 SLE: 0.00 Item: AUTOSAVER88 Headlight Assembly Compatibl...\u001b[0m\n",
+      "\u001b[92m23: Guess: $28.77 Truth: $24.99 Error: $3.78 SLE: 0.02 Item: ASI NAUTICAL 2.5 Inches Opera Glasses Bi...\u001b[0m\n",
+      "\u001b[93m24: Guess: $79.27 Truth: $149.00 Error: $69.73 SLE: 0.39 Item: Behringer TUBE OVERDRIVE TO100 Authentic...\u001b[0m\n",
+      "\u001b[92m25: Guess: $31.67 Truth: $16.99 Error: $14.68 SLE: 0.36 Item: Fun Express Insect Finger Puppets - 24 f...\u001b[0m\n",
+      "\u001b[92m26: Guess: $25.92 Truth: $7.99 Error: $17.93 SLE: 1.20 Item: WAFJAMF Roller Stamp Identity Theft Stam...\u001b[0m\n",
+      "\u001b[92m27: Guess: $204.56 Truth: $199.99 Error: $4.57 SLE: 0.00 Item: Capulina Tiffany Floor Lamp 2-Light 16\" ...\u001b[0m\n",
+      "\u001b[92m28: Guess: $296.82 Truth: $251.45 Error: $45.37 SLE: 0.03 Item: Apple Watch Series 6 (GPS, 44mm) - Space...\u001b[0m\n",
+      "\u001b[92m29: Guess: $253.13 Truth: $231.62 Error: $21.51 SLE: 0.01 Item: ICON 01725 Tandem Axle Fender Skirt FS17...\u001b[0m\n",
+      "\u001b[92m30: Guess: $155.98 Truth: $135.00 Error: $20.98 SLE: 0.02 Item: SanDisk 128GB Ultra (10 Pack) MicroSD Cl...\u001b[0m\n",
+      "\u001b[92m31: Guess: $407.45 Truth: $356.62 Error: $50.83 SLE: 0.02 Item: Velvac 2020,L,C/Hr,W,E2003,102\",Bk - 715...\u001b[0m\n",
+      "\u001b[92m32: Guess: $271.71 Truth: $257.99 Error: $13.72 SLE: 0.00 Item: TCMT Passenger Backrest Sissy Bar & Lugg...\u001b[0m\n",
+      "\u001b[92m33: Guess: $48.35 Truth: $27.99 Error: $20.36 SLE: 0.28 Item: Alnicov 63.5MM Brass Tremolo Block，Tremo...\u001b[0m\n",
+      "\u001b[93m34: Guess: $125.03 Truth: $171.20 Error: $46.17 SLE: 0.10 Item: Subaru Forester Outback Legacy OEM Engin...\u001b[0m\n",
+      "\u001b[91m35: Guess: $392.21 Truth: $225.00 Error: $167.21 SLE: 0.31 Item: Richmond Auto Upholstery - 2012 Dodge Ra...\u001b[0m\n",
+      "\u001b[91m36: Guess: $189.81 Truth: $105.00 Error: $84.81 SLE: 0.35 Item: AP-39 Automotive Paint Primer Grey 2K Ur...\u001b[0m\n",
+      "\u001b[92m37: Guess: $319.24 Truth: $299.99 Error: $19.25 SLE: 0.00 Item: Road Top Wireless Carplay Retrofit Kit D...\u001b[0m\n",
+      "\u001b[92m38: Guess: $632.83 Truth: $535.09 Error: $97.74 SLE: 0.03 Item: Gibson Performance Exhaust 5658 Aluminiz...\u001b[0m\n",
+      "\u001b[92m39: Guess: $32.90 Truth: $12.33 Error: $20.57 SLE: 0.87 Item: Bella Tunno Happy Links - Baby Montessor...\u001b[0m\n",
+      "\u001b[92m40: Guess: $120.19 Truth: $84.99 Error: $35.20 SLE: 0.12 Item: CANMORE H300 Handheld GPS Golf Device, S...\u001b[0m\n",
+      "\u001b[92m41: Guess: $28.26 Truth: $15.99 Error: $12.27 SLE: 0.30 Item: DCPOWER AC Adapter Compatible Replacemen...\u001b[0m\n",
+      "\u001b[92m42: Guess: $58.26 Truth: $62.44 Error: $4.18 SLE: 0.00 Item: Sharp, VX2128V, Commercial Desktop Calcu...\u001b[0m\n",
+      "\u001b[92m43: Guess: $94.21 Truth: $82.99 Error: $11.22 SLE: 0.02 Item: Melissa & Doug Lifelike Plush Stork Gian...\u001b[0m\n",
+      "\u001b[93m44: Guess: $365.69 Truth: $599.95 Error: $234.26 SLE: 0.24 Item: Sony SSCS8 2-Way 3-Driver Center Channel...\u001b[0m\n",
+      "\u001b[93m45: Guess: $251.62 Truth: $194.99 Error: $56.63 SLE: 0.06 Item: ASUS Chromebook CX1, 14\" Full HD NanoEdg...\u001b[0m\n",
+      "\u001b[92m46: Guess: $315.80 Truth: $344.95 Error: $29.15 SLE: 0.01 Item: FiiO X7 32GB Hi-Res Lossless Music Playe...\u001b[0m\n",
+      "\u001b[92m47: Guess: $61.31 Truth: $37.99 Error: $23.32 SLE: 0.22 Item: TORRO Leather Case Compatible with iPhon...\u001b[0m\n",
+      "\u001b[92m48: Guess: $230.81 Truth: $224.35 Error: $6.46 SLE: 0.00 Item: Universal Air Conditioner KT 1031 A/C Co...\u001b[0m\n",
+      "\u001b[92m49: Guess: $799.29 Truth: $814.00 Error: $14.71 SLE: 0.00 Item: Street Series Stainless Performance Cat-...\u001b[0m\n",
+      "\u001b[93m50: Guess: $317.66 Truth: $439.88 Error: $122.22 SLE: 0.11 Item: Lenovo IdeaPad 3 14-inch Laptop, 14.0-in...\u001b[0m\n",
+      "\u001b[92m51: Guess: $345.02 Truth: $341.43 Error: $3.59 SLE: 0.00 Item: Access Bed Covers TonnoSport 22050219 - ...\u001b[0m\n",
+      "\u001b[92m52: Guess: $52.11 Truth: $46.78 Error: $5.33 SLE: 0.01 Item: G.I. JOE Hasbro 3 3/4\" Wave 5 Action Fig...\u001b[0m\n",
+      "\u001b[93m53: Guess: $234.35 Truth: $171.44 Error: $62.91 SLE: 0.10 Item: T&S Brass B-0232-BST Double Pantry Fauce...\u001b[0m\n",
+      "\u001b[92m54: Guess: $480.97 Truth: $458.00 Error: $22.97 SLE: 0.00 Item: ZTUOAUMA Fuel Injection Pump 3090942 309...\u001b[0m\n",
+      "\u001b[93m55: Guess: $178.10 Truth: $130.75 Error: $47.35 SLE: 0.09 Item: 2AP18AA#ABA Hp Prime Graphing Calculator...\u001b[0m\n",
+      "\u001b[92m56: Guess: $56.86 Truth: $83.81 Error: $26.95 SLE: 0.15 Item: Lowrance 000-0119-83 Nmea 2000 25' Exten...\u001b[0m\n",
+      "\u001b[91m57: Guess: $167.67 Truth: $386.39 Error: $218.72 SLE: 0.69 Item: Jeep Genuine Accessories 82213051 Hood L...\u001b[0m\n",
+      "\u001b[92m58: Guess: $176.92 Truth: $169.00 Error: $7.92 SLE: 0.00 Item: GODOX CB-06 Hard Carrying Case with Whee...\u001b[0m\n",
+      "\u001b[92m59: Guess: $29.57 Truth: $17.95 Error: $11.62 SLE: 0.23 Item: Au-Tomotive Gold, INC. Ford Black Valet ...\u001b[0m\n",
+      "\u001b[92m60: Guess: $285.89 Truth: $269.00 Error: $16.89 SLE: 0.00 Item: Snailfly Black Roof Rack Rail + Cross Ba...\u001b[0m\n",
+      "\u001b[92m61: Guess: $105.87 Truth: $77.77 Error: $28.10 SLE: 0.09 Item: KING SHA Anti Glare LED Track Lighting H...\u001b[0m\n",
+      "\u001b[92m62: Guess: $102.21 Truth: $88.99 Error: $13.22 SLE: 0.02 Item: APS Compatible with Chevy Silverado 1500...\u001b[0m\n",
+      "\u001b[92m63: Guess: $333.99 Truth: $364.41 Error: $30.42 SLE: 0.01 Item: Wilwood Engineering 14011291R Brake Cali...\u001b[0m\n",
+      "\u001b[92m64: Guess: $160.69 Truth: $127.03 Error: $33.66 SLE: 0.05 Item: ACDelco Gold 336-1925A Starter, Remanufa...\u001b[0m\n",
+      "\u001b[92m65: Guess: $660.87 Truth: $778.95 Error: $118.08 SLE: 0.03 Item: UWS EC10783 69-Inch Matte Black Heavy-Wa...\u001b[0m\n",
+      "\u001b[92m66: Guess: $193.16 Truth: $206.66 Error: $13.50 SLE: 0.00 Item: Dell Latitude E5440 14in Business Laptop...\u001b[0m\n",
+      "\u001b[92m67: Guess: $48.29 Truth: $35.94 Error: $12.35 SLE: 0.08 Item: (Plug and Play) Spare Tire Brake Light W...\u001b[0m\n",
+      "\u001b[92m68: Guess: $169.10 Truth: $149.00 Error: $20.10 SLE: 0.02 Item: The Ultimate Roadside Rescue Assistant\u001b[0m\n",
+      "\u001b[92m69: Guess: $236.94 Truth: $251.98 Error: $15.04 SLE: 0.00 Item: Brand New 18\" x 8.5\" Replacement Wheel f...\u001b[0m\n",
+      "\u001b[93m70: Guess: $225.17 Truth: $160.00 Error: $65.17 SLE: 0.12 Item: Headlight Headlamp LH Left & RH Right Pa...\u001b[0m\n",
+      "\u001b[92m71: Guess: $71.69 Truth: $39.99 Error: $31.70 SLE: 0.33 Item: Lilo And Stitch Deluxe Oversize Print La...\u001b[0m\n",
+      "\u001b[93m72: Guess: $267.58 Truth: $362.41 Error: $94.83 SLE: 0.09 Item: AC Compressor & A/C Clutch For Hyundai A...\u001b[0m\n",
+      "\u001b[92m73: Guess: $409.01 Truth: $344.00 Error: $65.01 SLE: 0.03 Item: House Of Troy PIN475-AB Pinnacle Collect...\u001b[0m\n",
+      "\u001b[92m74: Guess: $36.40 Truth: $25.09 Error: $11.31 SLE: 0.13 Item: Juno T29 WH Floating Electrical Feed Sin...\u001b[0m\n",
+      "\u001b[92m75: Guess: $140.88 Truth: $175.95 Error: $35.07 SLE: 0.05 Item: Sherman GO-PARTS - for 2013-2016 Toyota ...\u001b[0m\n",
+      "\u001b[91m76: Guess: $335.75 Truth: $132.64 Error: $203.11 SLE: 0.85 Item: Roland RPU-3 Electronic Keyboard Pedal o...\u001b[0m\n",
+      "\u001b[93m77: Guess: $276.96 Truth: $422.99 Error: $146.03 SLE: 0.18 Item: Rockland VMI14 12,000 Pound 12 Volt DC E...\u001b[0m\n",
+      "\u001b[92m78: Guess: $165.91 Truth: $146.48 Error: $19.43 SLE: 0.02 Item: Max Advanced Brakes Elite XDS Front Cros...\u001b[0m\n",
+      "\u001b[92m79: Guess: $186.72 Truth: $156.83 Error: $29.89 SLE: 0.03 Item: Quality-Built 11030 Premium Quality Alte...\u001b[0m\n",
+      "\u001b[93m80: Guess: $167.44 Truth: $251.99 Error: $84.55 SLE: 0.17 Item: Lucida LG-510 Student Classical Guitar, ...\u001b[0m\n",
+      "\u001b[91m81: Guess: $342.92 Truth: $940.33 Error: $597.41 SLE: 1.01 Item: Longacre 52-79800 Aluminum Turn Plates\u001b[0m\n",
+      "\u001b[92m82: Guess: $70.32 Truth: $52.99 Error: $17.33 SLE: 0.08 Item: Motion Pro 08-0380 Adjustable Torque Wre...\u001b[0m\n",
+      "\u001b[93m83: Guess: $302.97 Truth: $219.95 Error: $83.02 SLE: 0.10 Item: Glyph Thunderbolt 3 NVMe Dock (0 GB)\u001b[0m\n",
+      "\u001b[92m84: Guess: $451.60 Truth: $441.03 Error: $10.57 SLE: 0.00 Item: TOYO Open Country MT Performance Radial ...\u001b[0m\n",
+      "\u001b[92m85: Guess: $181.95 Truth: $168.98 Error: $12.97 SLE: 0.01 Item: Razer Seiren X USB Streaming Microphone ...\u001b[0m\n",
+      "\u001b[92m86: Guess: $27.18 Truth: $2.49 Error: $24.69 SLE: 4.36 Item: Happy Birthday to Dad From Your Daughter...\u001b[0m\n",
+      "\u001b[92m87: Guess: $94.46 Truth: $98.62 Error: $4.16 SLE: 0.00 Item: Little Tikes My Real Jam First Concert S...\u001b[0m\n",
+      "\u001b[92m88: Guess: $216.10 Truth: $256.95 Error: $40.85 SLE: 0.03 Item: Studio M Peace and Harmony Art Pole Comm...\u001b[0m\n",
+      "\u001b[92m89: Guess: $34.01 Truth: $30.99 Error: $3.02 SLE: 0.01 Item: MyVolts 12V Power Supply Adaptor Compati...\u001b[0m\n",
+      "\u001b[93m90: Guess: $767.40 Truth: $569.84 Error: $197.56 SLE: 0.09 Item: Dell Latitude 7212 Rugged Extreme Tablet...\u001b[0m\n",
+      "\u001b[92m91: Guess: $201.17 Truth: $177.99 Error: $23.18 SLE: 0.01 Item: Covermates Contour Fit Car Cover - Light...\u001b[0m\n",
+      "\u001b[92m92: Guess: $972.55 Truth: $997.99 Error: $25.44 SLE: 0.00 Item: Westin 57-4025 Black HDX Grille Guard fi...\u001b[0m\n",
+      "\u001b[92m93: Guess: $201.93 Truth: $219.00 Error: $17.07 SLE: 0.01 Item: Fieldpiece JL2 Job Link Wireless App Tra...\u001b[0m\n",
+      "\u001b[92m94: Guess: $250.76 Truth: $225.55 Error: $25.21 SLE: 0.01 Item: hansgrohe Talis S Modern Premium Easy Cl...\u001b[0m\n",
+      "\u001b[91m95: Guess: $775.41 Truth: $495.95 Error: $279.46 SLE: 0.20 Item: G-Technology G-SPEED eS PRO High-Perform...\u001b[0m\n",
+      "\u001b[92m96: Guess: $924.45 Truth: $942.37 Error: $17.92 SLE: 0.00 Item: DreamLine SHDR-1960723L-01 Shower Door, ...\u001b[0m\n",
+      "\u001b[92m97: Guess: $27.71 Truth: $1.94 Error: $25.77 SLE: 5.19 Item: Sanctuary Square Backplate Finish: Oiled...\u001b[0m\n",
+      "\u001b[92m98: Guess: $273.73 Truth: $284.34 Error: $10.61 SLE: 0.00 Item: Pelican Protector 1750 Long Case - Multi...\u001b[0m\n",
+      "\u001b[92m99: Guess: $194.77 Truth: $171.90 Error: $22.87 SLE: 0.02 Item: Brock Replacement Driver and Passenger H...\u001b[0m\n",
+      "\u001b[93m100: Guess: $222.34 Truth: $144.99 Error: $77.35 SLE: 0.18 Item: Carlinkit Ai Box Mini, Android 11, Multi...\u001b[0m\n",
+      "\u001b[92m101: Guess: $406.35 Truth: $470.47 Error: $64.12 SLE: 0.02 Item: StarDot NetCamLIVE2 YouTube Live Stream ...\u001b[0m\n",
+      "\u001b[92m102: Guess: $79.62 Truth: $66.95 Error: $12.67 SLE: 0.03 Item: Atomic Compatible FILXXCAR0016 16x25x5 M...\u001b[0m\n",
+      "\u001b[92m103: Guess: $126.27 Truth: $117.00 Error: $9.27 SLE: 0.01 Item: Bandai Awakening of S. H. s.h.figuarts s...\u001b[0m\n",
+      "\u001b[91m104: Guess: $271.20 Truth: $172.14 Error: $99.06 SLE: 0.20 Item: Fit System 62135G Passenger Side Towing ...\u001b[0m\n",
+      "\u001b[92m105: Guess: $359.25 Truth: $392.74 Error: $33.49 SLE: 0.01 Item: Black Horse Black Aluminum Exceed Runnin...\u001b[0m\n",
+      "\u001b[92m106: Guess: $50.62 Truth: $16.99 Error: $33.63 SLE: 1.11 Item: Dearsun Twinkle Star Color Night Light P...\u001b[0m\n",
+      "\u001b[92m107: Guess: $23.97 Truth: $1.34 Error: $22.63 SLE: 5.61 Item: Pokemon - Gallade Spirit Link (83/108) -...\u001b[0m\n",
+      "\u001b[93m108: Guess: $254.32 Truth: $349.98 Error: $95.66 SLE: 0.10 Item: Ibanez GA34STCE-NT GIO Series Classical ...\u001b[0m\n",
+      "\u001b[92m109: Guess: $414.40 Truth: $370.71 Error: $43.69 SLE: 0.01 Item: Set 2 Heavy Duty 12-16.5 12x16.5 12 Ply ...\u001b[0m\n",
+      "\u001b[92m110: Guess: $73.27 Truth: $65.88 Error: $7.39 SLE: 0.01 Item: Hairpin Table Legs 28\" Heavy Duty Hairpi...\u001b[0m\n",
+      "\u001b[93m111: Guess: $280.92 Truth: $229.99 Error: $50.93 SLE: 0.04 Item: Marada Racing Seat with Adjustable Slide...\u001b[0m\n",
+      "\u001b[92m112: Guess: $25.05 Truth: $9.14 Error: $15.91 SLE: 0.89 Item: Remington Industries 24UL1007STRWHI25 24...\u001b[0m\n",
+      "\u001b[91m113: Guess: $377.12 Truth: $199.00 Error: $178.12 SLE: 0.41 Item: Acer S3-391-6046 13.3-inch Ultrabook, In...\u001b[0m\n",
+      "\u001b[91m114: Guess: $195.37 Truth: $109.99 Error: $85.38 SLE: 0.33 Item: ICBEAMER 7\" RGB LED Headlights Bulb Halo...\u001b[0m\n",
+      "\u001b[93m115: Guess: $395.30 Truth: $570.42 Error: $175.12 SLE: 0.13 Item: R1 Concepts Front Rear Brakes and Rotors...\u001b[0m\n",
+      "\u001b[92m116: Guess: $253.52 Truth: $279.99 Error: $26.47 SLE: 0.01 Item: Camplux 2.64 GPM Tankless , Outdoor Port...\u001b[0m\n",
+      "\u001b[92m117: Guess: $46.52 Truth: $30.99 Error: $15.53 SLE: 0.16 Item: KNOKLOCK 10 Pack 3.75 Inch(96mm) Kitchen...\u001b[0m\n",
+      "\u001b[92m118: Guess: $40.11 Truth: $31.99 Error: $8.12 SLE: 0.05 Item: Valley Enterprises Yaesu USB FTDI CT-62 ...\u001b[0m\n",
+      "\u001b[93m119: Guess: $62.65 Truth: $15.90 Error: $46.75 SLE: 1.76 Item: G9 LED Light Bulbs，8W，75W 100W replaceme...\u001b[0m\n",
+      "\u001b[93m120: Guess: $106.37 Truth: $45.99 Error: $60.38 SLE: 0.68 Item: ZCHAOZ 4 Lights Antique White Farmhouse ...\u001b[0m\n",
+      "\u001b[91m121: Guess: $234.32 Truth: $113.52 Error: $120.80 SLE: 0.52 Item: Honeywell TH8320R1003 Honeywell VisionPr...\u001b[0m\n",
+      "\u001b[92m122: Guess: $501.78 Truth: $516.99 Error: $15.21 SLE: 0.00 Item: Patriot Exhaust H8013-1 1-7/8\" Clippster...\u001b[0m\n",
+      "\u001b[93m123: Guess: $140.05 Truth: $196.99 Error: $56.94 SLE: 0.11 Item: Fitrite Autopart New Front Left Driver S...\u001b[0m\n",
+      "\u001b[93m124: Guess: $92.57 Truth: $46.55 Error: $46.02 SLE: 0.46 Item: Technical Precision Replacement for GE G...\u001b[0m\n",
+      "\u001b[93m125: Guess: $278.54 Truth: $356.99 Error: $78.45 SLE: 0.06 Item: Covercraft Carhartt SeatSaver Front Row ...\u001b[0m\n",
+      "\u001b[92m126: Guess: $279.54 Truth: $319.95 Error: $40.41 SLE: 0.02 Item: Sennheiser SD Pro 2 (506008) - Double-Si...\u001b[0m\n",
+      "\u001b[93m127: Guess: $143.18 Truth: $96.06 Error: $47.12 SLE: 0.16 Item: Hitachi MAF0110 Mass Air Flow Sensor\u001b[0m\n",
+      "\u001b[93m128: Guess: $239.09 Truth: $190.99 Error: $48.10 SLE: 0.05 Item: AmScope SE305R-P-LED-PS36A 10X-30X LED C...\u001b[0m\n",
+      "\u001b[93m129: Guess: $161.55 Truth: $257.95 Error: $96.40 SLE: 0.22 Item: Front Left Driver Side Window Regulator ...\u001b[0m\n",
+      "\u001b[92m130: Guess: $84.35 Truth: $62.95 Error: $21.40 SLE: 0.08 Item: Premium Replica Hubcap Set, Fits Nissan ...\u001b[0m\n",
+      "\u001b[93m131: Guess: $92.57 Truth: $47.66 Error: $44.91 SLE: 0.43 Item: Excellerations Phonics Spelling Game for...\u001b[0m\n",
+      "\u001b[92m132: Guess: $218.86 Truth: $226.99 Error: $8.13 SLE: 0.00 Item: RC4WD BigDog Dual Axle Scale Car/Truck T...\u001b[0m\n",
+      "\u001b[92m133: Guess: $291.86 Truth: $359.95 Error: $68.09 SLE: 0.04 Item: Unknown Stage 2 Clutch Kit - Low Altitud...\u001b[0m\n",
+      "\u001b[92m134: Guess: $78.20 Truth: $78.40 Error: $0.20 SLE: 0.00 Item: 2002-2008 Dodge Ram 1500 Mopar 4X4 Emble...\u001b[0m\n",
+      "\u001b[92m135: Guess: $179.42 Truth: $172.77 Error: $6.65 SLE: 0.00 Item: Pro Comp Alloys Series 89 Wheel with Pol...\u001b[0m\n",
+      "\u001b[92m136: Guess: $305.83 Truth: $316.45 Error: $10.62 SLE: 0.00 Item: Detroit Axle - Front Rear Strut & Coil S...\u001b[0m\n",
+      "\u001b[92m137: Guess: $95.42 Truth: $87.99 Error: $7.43 SLE: 0.01 Item: ECCPP Rear Wheel Axle Replacement fit fo...\u001b[0m\n",
+      "\u001b[92m138: Guess: $221.32 Truth: $226.63 Error: $5.31 SLE: 0.00 Item: Dell Latitude E6520 Intel i7-2720QM 2.20...\u001b[0m\n",
+      "\u001b[92m139: Guess: $44.53 Truth: $31.49 Error: $13.04 SLE: 0.11 Item: F FIERCE CYCLE 251pcs Black Universal Mo...\u001b[0m\n",
+      "\u001b[93m140: Guess: $239.87 Truth: $196.00 Error: $43.87 SLE: 0.04 Item: Flash Furniture 4 Pk. HERCULES Series 88...\u001b[0m\n",
+      "\u001b[92m141: Guess: $51.09 Truth: $78.40 Error: $27.31 SLE: 0.18 Item: B&M 30287 Throttle Valve/Kickdown Cable,...\u001b[0m\n",
+      "\u001b[92m142: Guess: $125.85 Truth: $116.25 Error: $9.60 SLE: 0.01 Item: Gates TCK226 PowerGrip Premium Timing Be...\u001b[0m\n",
+      "\u001b[92m143: Guess: $146.88 Truth: $112.78 Error: $34.10 SLE: 0.07 Item: Monroe Shocks & Struts Quick-Strut 17149...\u001b[0m\n",
+      "\u001b[93m144: Guess: $82.47 Truth: $27.32 Error: $55.15 SLE: 1.17 Item: Feit Electric BPMR16/GU10/930CA/6 35W EQ...\u001b[0m\n",
+      "\u001b[92m145: Guess: $139.40 Truth: $145.91 Error: $6.51 SLE: 0.00 Item: Yellow Jacket 2806 Contractor Extension ...\u001b[0m\n",
+      "\u001b[92m146: Guess: $185.59 Truth: $171.09 Error: $14.50 SLE: 0.01 Item: Garage-Pro Tailgate SET Compatible with ...\u001b[0m\n",
+      "\u001b[93m147: Guess: $116.05 Truth: $167.95 Error: $51.90 SLE: 0.13 Item: 3M Perfect It Buffing and Polishing Kit ...\u001b[0m\n",
+      "\u001b[92m148: Guess: $56.66 Truth: $28.49 Error: $28.17 SLE: 0.45 Item: Chinese Style Dollhouse Model DIY Miniat...\u001b[0m\n",
+      "\u001b[92m149: Guess: $148.08 Truth: $122.23 Error: $25.85 SLE: 0.04 Item: Generic NRG Innovations SRK-161H Steerin...\u001b[0m\n",
+      "\u001b[92m150: Guess: $58.72 Truth: $32.99 Error: $25.73 SLE: 0.32 Item: Learning Resources Coding Critters Range...\u001b[0m\n",
+      "\u001b[93m151: Guess: $114.50 Truth: $71.20 Error: $43.30 SLE: 0.22 Item: Bosch Automotive 15463 Oxygen Sensor, OE...\u001b[0m\n",
+      "\u001b[92m152: Guess: $90.17 Truth: $112.75 Error: $22.58 SLE: 0.05 Item: Case of 24-2 Inch Blue Painters Tape - 6...\u001b[0m\n",
+      "\u001b[92m153: Guess: $131.93 Truth: $142.43 Error: $10.50 SLE: 0.01 Item: MOCA Engine Water Pump & Fan Clutch fit ...\u001b[0m\n",
+      "\u001b[93m154: Guess: $304.84 Truth: $398.99 Error: $94.15 SLE: 0.07 Item: SAREMAS Foot Step Bars for Hyundai Palis...\u001b[0m\n",
+      "\u001b[93m155: Guess: $589.85 Truth: $449.00 Error: $140.85 SLE: 0.07 Item: Gretsch G9210 Square Neck Boxcar Mahogan...\u001b[0m\n",
+      "\u001b[92m156: Guess: $198.86 Truth: $189.00 Error: $9.86 SLE: 0.00 Item: NikoMaku Mirror Dash Cam Front and Rear ...\u001b[0m\n",
+      "\u001b[92m157: Guess: $112.44 Truth: $120.91 Error: $8.47 SLE: 0.01 Item: Fenix HP25R v2.0 USB-C Rechargeable Head...\u001b[0m\n",
+      "\u001b[92m158: Guess: $182.50 Truth: $203.53 Error: $21.03 SLE: 0.01 Item: R&L Racing Heavy Duty Roll-Up Soft Tonne...\u001b[0m\n",
+      "\u001b[92m159: Guess: $341.67 Truth: $349.99 Error: $8.32 SLE: 0.00 Item: Garmin 010-02258-10 GPSMAP 64sx, Handhel...\u001b[0m\n",
+      "\u001b[92m160: Guess: $29.90 Truth: $34.35 Error: $4.45 SLE: 0.02 Item: Brown 5-7/8\" X 8-1/2\" X 3/16\" Thick Heav...\u001b[0m\n",
+      "\u001b[92m161: Guess: $331.67 Truth: $384.99 Error: $53.32 SLE: 0.02 Item: GAOMON PD2200 Pen Display & 20 Pen Nibs ...\u001b[0m\n",
+      "\u001b[93m162: Guess: $262.90 Truth: $211.00 Error: $51.90 SLE: 0.05 Item: VXMOTOR for 97-03 Ford F150/F250 Lightdu...\u001b[0m\n",
+      "\u001b[91m163: Guess: $226.38 Truth: $129.00 Error: $97.38 SLE: 0.31 Item: HP EliteBook 2540p Intel Core i7-640LM X...\u001b[0m\n",
+      "\u001b[93m164: Guess: $38.95 Truth: $111.45 Error: $72.50 SLE: 1.07 Item: Green EPX Mixing Nozzles 100-Pack-fits 3...\u001b[0m\n",
+      "\u001b[92m165: Guess: $45.80 Truth: $81.12 Error: $35.32 SLE: 0.32 Item: Box Partners 6 1/4 x 3 1/8\" 13 Pt. Manil...\u001b[0m\n",
+      "\u001b[92m166: Guess: $437.99 Truth: $457.08 Error: $19.09 SLE: 0.00 Item: Vixen Air 1/2\" NPT Air Ride Suspension H...\u001b[0m\n",
+      "\u001b[92m167: Guess: $82.78 Truth: $49.49 Error: $33.29 SLE: 0.26 Item: Smart Floor Lamp, 2700-6500K+RGBPink Mul...\u001b[0m\n",
+      "\u001b[93m168: Guess: $122.46 Truth: $80.56 Error: $41.90 SLE: 0.17 Item: SOZG 324mm Wheelbase Body Shell RC Car B...\u001b[0m\n",
+      "\u001b[92m169: Guess: $301.87 Truth: $278.39 Error: $23.48 SLE: 0.01 Item: Mickey Thompson ET Street S/S Racing Rad...\u001b[0m\n",
+      "\u001b[92m170: Guess: $425.78 Truth: $364.50 Error: $61.28 SLE: 0.02 Item: Pirelli 275/40R20 106W XL RFT P0 PZ4-LUX...\u001b[0m\n",
+      "\u001b[93m171: Guess: $520.14 Truth: $378.99 Error: $141.15 SLE: 0.10 Item: Torklift C3212 Rear Tie Down\u001b[0m\n",
+      "\u001b[93m172: Guess: $220.74 Truth: $165.28 Error: $55.46 SLE: 0.08 Item: Cardone 78-4226 Remanufactured Ford Comp...\u001b[0m\n",
+      "\u001b[93m173: Guess: $98.70 Truth: $56.74 Error: $41.96 SLE: 0.30 Item: Kidde AccessPoint 001798 Supra TouchPoin...\u001b[0m\n",
+      "\u001b[93m174: Guess: $214.16 Truth: $307.95 Error: $93.79 SLE: 0.13 Item: 3M Protecta 3100414 Self Retracting Life...\u001b[0m\n",
+      "\u001b[93m175: Guess: $102.49 Truth: $38.00 Error: $64.49 SLE: 0.95 Item: Plantronics 89435-01 Wired Headset, Blac...\u001b[0m\n",
+      "\u001b[92m176: Guess: $91.64 Truth: $53.00 Error: $38.64 SLE: 0.29 Item: Logitech K750 Wireless Solar Keyboard fo...\u001b[0m\n",
+      "\u001b[92m177: Guess: $537.72 Truth: $498.00 Error: $39.72 SLE: 0.01 Item: Olympus PEN E-PL9 Body Only with 3-Inch ...\u001b[0m\n",
+      "\u001b[91m178: Guess: $134.82 Truth: $53.99 Error: $80.83 SLE: 0.82 Item: Beck/Arnley 051-6066 Hub & Bearing Assem...\u001b[0m\n",
+      "\u001b[92m179: Guess: $363.60 Truth: $350.00 Error: $13.60 SLE: 0.00 Item: Eibach Pro-Kit Performance Springs E10-6...\u001b[0m\n",
+      "\u001b[92m180: Guess: $311.21 Truth: $299.95 Error: $11.26 SLE: 0.00 Item: LEGO DC Batman 1989 Batwing 76161 Displa...\u001b[0m\n",
+      "\u001b[92m181: Guess: $97.05 Truth: $94.93 Error: $2.12 SLE: 0.00 Item: Kingston Brass KS3608PL Restoration 4-In...\u001b[0m\n",
+      "\u001b[92m182: Guess: $321.44 Truth: $379.00 Error: $57.56 SLE: 0.03 Item: Polk Vanishing Series 265-LS In-Wall 3-W...\u001b[0m\n",
+      "\u001b[92m183: Guess: $252.24 Truth: $299.95 Error: $47.71 SLE: 0.03 Item: Spec-D Tuning LED Projector Headlights G...\u001b[0m\n",
+      "\u001b[92m184: Guess: $39.72 Truth: $24.99 Error: $14.73 SLE: 0.20 Item: RICHMOND & FINCH Airpod Pro Case, Green ...\u001b[0m\n",
+      "\u001b[91m185: Guess: $135.40 Truth: $41.04 Error: $94.36 SLE: 1.39 Item: LFA Industries 43B-5A-33JT 1/16-1/2-1.5-...\u001b[0m\n",
+      "\u001b[92m186: Guess: $298.91 Truth: $327.90 Error: $28.99 SLE: 0.01 Item: SAUTVS LED Headlight Assembly for Slings...\u001b[0m\n",
+      "\u001b[92m187: Guess: $36.90 Truth: $10.99 Error: $25.91 SLE: 1.32 Item: 2 Pack Combo Womens Safety Glasses Impac...\u001b[0m\n",
+      "\u001b[92m188: Guess: $23.51 Truth: $14.99 Error: $8.52 SLE: 0.18 Item: Arepa - Venezuelan cuisine - Venezuela P...\u001b[0m\n",
+      "\u001b[93m189: Guess: $43.32 Truth: $84.95 Error: $41.63 SLE: 0.44 Item: Schlage Lock Company KS23D2300 Padlock, ...\u001b[0m\n",
+      "\u001b[92m190: Guess: $144.93 Truth: $111.00 Error: $33.93 SLE: 0.07 Item: Techni Mobili White Sit to Stand Mobile ...\u001b[0m\n",
+      "\u001b[92m191: Guess: $159.17 Truth: $123.73 Error: $35.44 SLE: 0.06 Item: Special Lite Products Contemporary Wall ...\u001b[0m\n",
+      "\u001b[92m192: Guess: $519.71 Truth: $557.38 Error: $37.67 SLE: 0.00 Item: Tascam DP-24SD 24-Track Digital Portastu...\u001b[0m\n",
+      "\u001b[92m193: Guess: $117.26 Truth: $95.55 Error: $21.71 SLE: 0.04 Item: Glow Lighting 636CC10SP Vista Crystal Fl...\u001b[0m\n",
+      "\u001b[92m194: Guess: $164.33 Truth: $154.00 Error: $10.33 SLE: 0.00 Item: Z3 Wind Deflector, Smoke Tint, Lexan, Wi...\u001b[0m\n",
+      "\u001b[91m195: Guess: $333.36 Truth: $198.99 Error: $134.37 SLE: 0.26 Item: Olympus E-20 5MP Digital Camera w/ 4x Op...\u001b[0m\n",
+      "\u001b[91m196: Guess: $215.62 Truth: $430.44 Error: $214.82 SLE: 0.47 Item: PHYNEDI 1:1000 World Trade Center (1973-...\u001b[0m\n",
+      "\u001b[92m197: Guess: $46.01 Truth: $45.67 Error: $0.34 SLE: 0.00 Item: YANGHUAN Unstable Unicorns Adventure Car...\u001b[0m\n",
+      "\u001b[92m198: Guess: $268.04 Truth: $249.00 Error: $19.04 SLE: 0.01 Item: Interlogix NX-1820E NetworX Touch Screen...\u001b[0m\n",
+      "\u001b[92m199: Guess: $57.29 Truth: $42.99 Error: $14.30 SLE: 0.08 Item: Steering Damper,Universal Motorcycle Han...\u001b[0m\n",
+      "\u001b[93m200: Guess: $224.93 Truth: $181.33 Error: $43.60 SLE: 0.05 Item: Amprobe TIC 410A Hot Stick Attachment\u001b[0m\n",
+      "\u001b[92m201: Guess: $21.04 Truth: $6.03 Error: $15.01 SLE: 1.31 Item: MyCableMart 3.5mm Plug/Jack, 4 Conductor...\u001b[0m\n",
+      "\u001b[93m202: Guess: $73.50 Truth: $29.99 Error: $43.51 SLE: 0.77 Item: OtterBox + Pop Symmetry Series Case for ...\u001b[0m\n",
+      "\u001b[92m203: Guess: $768.64 Truth: $899.00 Error: $130.36 SLE: 0.02 Item: Dell XPS X8700-1572BLK Desktop ( Intel C...\u001b[0m\n",
+      "\u001b[93m204: Guess: $521.15 Truth: $399.99 Error: $121.16 SLE: 0.07 Item: Franklin Iron Works Sperry Industrial Br...\u001b[0m\n",
+      "\u001b[92m205: Guess: $22.80 Truth: $4.66 Error: $18.14 SLE: 2.06 Item: Avery Legal Dividers, Standard Collated ...\u001b[0m\n",
+      "\u001b[92m206: Guess: $250.76 Truth: $261.41 Error: $10.65 SLE: 0.00 Item: Moen 8346 Commercial Posi-Temp Pressure ...\u001b[0m\n",
+      "\u001b[92m207: Guess: $152.89 Truth: $136.97 Error: $15.92 SLE: 0.01 Item: Carlisle Versa Trail ATR All Terrain Rad...\u001b[0m\n",
+      "\u001b[92m208: Guess: $106.34 Truth: $79.00 Error: $27.34 SLE: 0.09 Item: SUNWAYFOTO 44mm Tripod Ball Head Arca Co...\u001b[0m\n",
+      "\u001b[92m209: Guess: $426.61 Truth: $444.99 Error: $18.38 SLE: 0.00 Item: NanoBeam AC NBE-5AC-Gen2-US 4 Units 5GHz...\u001b[0m\n",
+      "\u001b[92m210: Guess: $470.28 Truth: $411.94 Error: $58.34 SLE: 0.02 Item: WULF 4\" Front 2\" Rear Leveling Lift Kit ...\u001b[0m\n",
+      "\u001b[93m211: Guess: $204.70 Truth: $148.40 Error: $56.30 SLE: 0.10 Item: Alera ALEVABFMC Valencia Series Mobile B...\u001b[0m\n",
+      "\u001b[93m212: Guess: $167.36 Truth: $244.99 Error: $77.63 SLE: 0.14 Item: YU-GI-OH! Ignition Assault Booster Box\u001b[0m\n",
+      "\u001b[92m213: Guess: $114.17 Truth: $86.50 Error: $27.67 SLE: 0.08 Item: 48\" x 36\" Extra-Large Framed Magnetic Bl...\u001b[0m\n",
+      "\u001b[91m214: Guess: $131.30 Truth: $297.95 Error: $166.65 SLE: 0.66 Item: Dell Latitude D620 Renewed Notebook PC\u001b[0m\n",
+      "\u001b[92m215: Guess: $474.25 Truth: $399.99 Error: $74.26 SLE: 0.03 Item: acer Aspire 5 Laptop, AMD Ryzen 3 5300U ...\u001b[0m\n",
+      "\u001b[92m216: Guess: $616.57 Truth: $599.00 Error: $17.57 SLE: 0.00 Item: Elk 31080/6RC-GRN 30 by 6-Inch Viva 6-Li...\u001b[0m\n",
+      "\u001b[92m217: Guess: $71.30 Truth: $105.99 Error: $34.69 SLE: 0.15 Item: Barbie Top Model Doll\u001b[0m\n",
+      "\u001b[92m218: Guess: $568.89 Truth: $689.00 Error: $120.11 SLE: 0.04 Item: Danby Designer 20-In. Electric Range wit...\u001b[0m\n",
+      "\u001b[92m219: Guess: $340.73 Truth: $404.99 Error: $64.26 SLE: 0.03 Item: FixtureDisplays® Metal Truss Podium Doub...\u001b[0m\n",
+      "\u001b[92m220: Guess: $230.27 Truth: $207.76 Error: $22.51 SLE: 0.01 Item: ACDelco 13597235 GM Original Equipment A...\u001b[0m\n",
+      "\u001b[92m221: Guess: $190.44 Truth: $171.82 Error: $18.62 SLE: 0.01 Item: EBC S1KF1135 Stage-1 Premium Street Brak...\u001b[0m\n",
+      "\u001b[92m222: Guess: $293.87 Truth: $293.24 Error: $0.63 SLE: 0.00 Item: FXR Men's Boost FX Jacket (Black/Orange/...\u001b[0m\n",
+      "\u001b[92m223: Guess: $385.94 Truth: $374.95 Error: $10.99 SLE: 0.00 Item: SuperATV Scratch Resistant 3-in-1 Flip W...\u001b[0m\n",
+      "\u001b[92m224: Guess: $125.86 Truth: $111.99 Error: $13.87 SLE: 0.01 Item: SBU 3 Layer All Weather Mini Van Car Cov...\u001b[0m\n",
+      "\u001b[92m225: Guess: $66.44 Truth: $42.99 Error: $23.45 SLE: 0.18 Item: 2 Pack Outdoor Brochure Holder Advertisi...\u001b[0m\n",
+      "\u001b[92m226: Guess: $145.97 Truth: $116.71 Error: $29.26 SLE: 0.05 Item: Monroe Shocks & Struts Quick-Strut 17158...\u001b[0m\n",
+      "\u001b[91m227: Guess: $201.35 Truth: $118.61 Error: $82.74 SLE: 0.28 Item: Elements of Design Magellan EB235AL Thre...\u001b[0m\n",
+      "\u001b[92m228: Guess: $142.35 Truth: $147.12 Error: $4.77 SLE: 0.00 Item: GM Genuine Parts 15-62961 Air Conditioni...\u001b[0m\n",
+      "\u001b[93m229: Guess: $173.74 Truth: $119.99 Error: $53.75 SLE: 0.14 Item: Baseus 17-in-1 USB C Docking Station to ...\u001b[0m\n",
+      "\u001b[93m230: Guess: $490.03 Truth: $369.98 Error: $120.05 SLE: 0.08 Item: Whitehall™ Personalized Whitehall Capito...\u001b[0m\n",
+      "\u001b[92m231: Guess: $291.51 Truth: $315.55 Error: $24.04 SLE: 0.01 Item: Pro Circuit Works Pipe PY05250 for 02-19...\u001b[0m\n",
+      "\u001b[93m232: Guess: $261.90 Truth: $190.99 Error: $70.91 SLE: 0.10 Item: HYANKA 15 \"1200W Professional DJ Speaker...\u001b[0m\n",
+      "\u001b[92m233: Guess: $193.70 Truth: $155.00 Error: $38.70 SLE: 0.05 Item: Bluetooth X6BT Card Reader Writer Encode...\u001b[0m\n",
+      "\u001b[92m234: Guess: $373.37 Truth: $349.99 Error: $23.38 SLE: 0.00 Item: AIRAID Cold Air Intake System by K&N: In...\u001b[0m\n",
+      "\u001b[92m235: Guess: $256.72 Truth: $249.99 Error: $6.73 SLE: 0.00 Item: Bostingner Shower Faucets Sets Complete,...\u001b[0m\n",
+      "\u001b[92m236: Guess: $37.83 Truth: $42.99 Error: $5.16 SLE: 0.02 Item: PIT66 Front Bumper Turn Signal Lights, C...\u001b[0m\n",
+      "\u001b[92m237: Guess: $39.64 Truth: $17.99 Error: $21.65 SLE: 0.58 Item: Caseology Bumpy Compatible with Google P...\u001b[0m\n",
+      "\u001b[92m238: Guess: $355.64 Truth: $425.00 Error: $69.36 SLE: 0.03 Item: Fleck 2510 Timer Mechanical Filter Contr...\u001b[0m\n",
+      "\u001b[93m239: Guess: $303.37 Truth: $249.99 Error: $53.38 SLE: 0.04 Item: Haloview MC7108 Wireless RV Backup Camer...\u001b[0m\n",
+      "\u001b[93m240: Guess: $61.21 Truth: $138.23 Error: $77.02 SLE: 0.65 Item: Schmidt Spiele - Manhattan\u001b[0m\n",
+      "\u001b[93m241: Guess: $520.03 Truth: $414.99 Error: $105.04 SLE: 0.05 Item: Corsa 14333 Tip Kit (Ford Mustang GT)\u001b[0m\n",
+      "\u001b[93m242: Guess: $223.31 Truth: $168.28 Error: $55.03 SLE: 0.08 Item: Hoshizaki FM116A Fan Motor Kit 1\u001b[0m\n",
+      "\u001b[92m243: Guess: $222.89 Truth: $199.99 Error: $22.90 SLE: 0.01 Item: BAINUO Antler Chandelier Lighting,6 Ligh...\u001b[0m\n",
+      "\u001b[92m244: Guess: $166.09 Truth: $126.70 Error: $39.39 SLE: 0.07 Item: DNA MOTORING HL-OH-FEXP06-SM-AM Smoke Le...\u001b[0m\n",
+      "\u001b[92m245: Guess: $23.59 Truth: $5.91 Error: $17.68 SLE: 1.61 Item: Wera Stainless 3840/1 TS 2.5mm Hex Inser...\u001b[0m\n",
+      "\u001b[92m246: Guess: $200.99 Truth: $193.06 Error: $7.93 SLE: 0.00 Item: Celestron - PowerSeeker 127EQ Telescope ...\u001b[0m\n",
+      "\u001b[93m247: Guess: $185.86 Truth: $249.99 Error: $64.13 SLE: 0.09 Item: NHOPEEW 10.1inch Android Car Radio Carpl...\u001b[0m\n",
+      "\u001b[92m248: Guess: $75.71 Truth: $64.12 Error: $11.59 SLE: 0.03 Item: Other Harmonica (Suzuki-2Timer24- A)\u001b[0m\n",
+      "\u001b[93m249: Guess: $194.92 Truth: $114.99 Error: $79.93 SLE: 0.27 Item: Harley Air Filter Venturi Intake Air Cle...\u001b[0m\n",
+      "\u001b[93m250: Guess: $646.25 Truth: $926.00 Error: $279.75 SLE: 0.13 Item: Elite Screens Edge Free Ambient Light Re...\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Tester.test(ensemble_pricer, test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cff07ba7-4557-4519-acba-15475118065d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/week8/community_contributions/Ensemble_with_xgboost/Build_Scanning_Agent.ipynb b/week8/community_contributions/Ensemble_with_xgboost/Build_Scanning_Agent.ipynb
new file mode 100644
index 0000000..846a5e3
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/Build_Scanning_Agent.ipynb
@@ -0,0 +1,235 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0df0d850-49eb-4a0b-a27a-146969db710d",
+   "metadata": {},
+   "source": [
+    "# ScanningAgent\n",
+    "\n",
+    "Looks for promising deals by subscribing to RSS feeds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3763a79-8a5a-4300-8de4-93e85475af10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import os\n",
+    "import json\n",
+    "from dotenv import load_dotenv\n",
+    "from openai import OpenAI\n",
+    "from agents.deals import ScrapedDeal, DealSelection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6469e32-16c3-4443-9475-ade710ef6933",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize and constants\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
+    "MODEL = 'gpt-4o-mini'\n",
+    "openai = OpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afece9db-8cd4-46be-ac57-0b472e84da7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "deals = ScrapedDeal.fetch(show_progress=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8cd15c4d-eb44-4601-bf0c-f945c1d8e3ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(deals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4259f30a-6455-49ed-8863-2f9ddd4776cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "deals[44].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8100e5ac-38f5-40c1-a712-08ae12c85038",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system_prompt = \"\"\"You identify and summarize the 5 most detailed deals from a list, by selecting deals that have the most detailed, high quality description and the most clear price.\n",
+    "Respond strictly in JSON with no explanation, using this format. You should provide the price as a number derived from the description. If the price of a deal isn't clear, do not include that deal in your response.\n",
+    "Most important is that you respond with the 5 deals that have the most detailed product description with price. It's not important to mention the terms of the deal; most important is a thorough description of the product.\n",
+    "Be careful with products that are described as \"$XXX off\" or \"reduced by $XXX\" - this isn't the actual price of the product. Only respond with products when you are highly confident about the price. \n",
+    "\n",
+    "{\"deals\": [\n",
+    "    {\n",
+    "        \"product_description\": \"Your clearly expressed summary of the product in 4-5 sentences. Details of the item are much more important than why it's a good deal. Avoid mentioning discounts and coupons; focus on the item itself. There should be a paragpraph of text for each item you choose.\",\n",
+    "        \"price\": 99.99,\n",
+    "        \"url\": \"the url as provided\"\n",
+    "    },\n",
+    "    ...\n",
+    "]}\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4bca170-af71-40c9-9597-1d72980c74d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "user_prompt = \"\"\"Respond with the most promising 5 deals from this list, selecting those which have the most detailed, high quality product description and a clear price.\n",
+    "Respond strictly in JSON, and only JSON. You should rephrase the description to be a summary of the product itself, not the terms of the deal.\n",
+    "Remember to respond with a paragraph of text in the product_description field for each of the 5 items that you select.\n",
+    "Be careful with products that are described as \"$XXX off\" or \"reduced by $XXX\" - this isn't the actual price of the product. Only respond with products when you are highly confident about the price. \n",
+    "\n",
+    "Deals:\n",
+    "\n",
+    "\"\"\"\n",
+    "user_prompt += '\\n\\n'.join([deal.describe() for deal in deals])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "020947a6-561b-417b-98a0-a085e31d2ce3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(user_prompt[:2000])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7de46f74-868c-4127-8a68-cf2da7d600bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_recommendations():\n",
+    "    completion = openai.beta.chat.completions.parse(\n",
+    "        model=\"gpt-4o-mini\",\n",
+    "        messages=[\n",
+    "            {\"role\": \"system\", \"content\": system_prompt},\n",
+    "            {\"role\": \"user\", \"content\": user_prompt}\n",
+    "      ],\n",
+    "        response_format=DealSelection\n",
+    "    )\n",
+    "    result = completion.choices[0].message.parsed\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c06270d-8c17-4d5a-9cfe-b6cefe788d5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = get_recommendations()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84e62845-3338-441a-8161-c70097af4773",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(result.deals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5554a0a-ae40-4684-ad3e-faa3d22e030c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result.deals[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bdc57fb-7497-47af-a643-6ba5a21cc17e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from agents.scanner_agent import ScannerAgent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "132278bc-217a-43a6-b6c4-724140c6a225",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = ScannerAgent()\n",
+    "result = agent.scan()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e1d013a-c930-4dad-901b-41433379e14b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ee2e837-1f1d-42d4-8bc4-51cccc343006",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/week8/community_contributions/Ensemble_with_xgboost/Create_Vector_Database.ipynb b/week8/community_contributions/Ensemble_with_xgboost/Create_Vector_Database.ipynb
new file mode 100644
index 0000000..2dcc68e
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/Create_Vector_Database.ipynb
@@ -0,0 +1,208 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "993a2a24-1a58-42be-8034-6d116fb8d786",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import os\n",
+    "import re\n",
+    "import math\n",
+    "import json\n",
+    "from tqdm import tqdm\n",
+    "import random\n",
+    "from dotenv import load_dotenv\n",
+    "from huggingface_hub import login\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "from sentence_transformers import SentenceTransformer\n",
+    "from datasets import load_dataset\n",
+    "import chromadb\n",
+    "from items import Item\n",
+    "from sklearn.manifold import TSNE\n",
+    "import plotly.graph_objects as go"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2359ccc0-dbf2-4b1e-9473-e472b32f548b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# environment\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
+    "os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN', 'your-key-if-not-using-env')\n",
+    "DB = \"products_vectorstore\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "645167e6-cf0d-42d2-949f-1089a25a2841",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Log in to HuggingFace\n",
+    "\n",
+    "hf_token = os.environ['HF_TOKEN']\n",
+    "login(hf_token, add_to_git_credential=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "688bd995-ec3e-43cd-8179-7fe14b275877",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# With train.pkl in this folder\n",
+    "with open('train.pkl', 'rb') as file:\n",
+    "    train = pickle.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4aab95e-d719-4476-b6e7-e248120df25a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = chromadb.PersistentClient(path=DB)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f95dafd-ab80-464e-ba8a-dec7a2424780",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check if the collection exists and delete it if it does\n",
+    "collection_name = \"products\"\n",
+    "existing_collection_names = [collection.name for collection in client.list_collections()]\n",
+    "if collection_name in existing_collection_names:\n",
+    "    client.delete_collection(collection_name)\n",
+    "    print(f\"Deleted existing collection: {collection_name}\")\n",
+    "\n",
+    "collection = client.create_collection(collection_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a87db200-d19d-44bf-acbd-15c45c70f5c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b23a025-4c35-4d3a-96ad-b956cad37b0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Pass in a list of texts, get back a numpy array of vectors\n",
+    "vector = model.encode([\"Well hi there\"])[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8adde63f-e732-4f7c-bba9-f8b2a469f14e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38de1bf8-c9b5-45b4-9f4b-86af93b3f80d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def description(item):\n",
+    "    text = item.prompt.replace(\"How much does this cost to the nearest dollar?\\n\\n\", \"\")\n",
+    "    return text.split(\"\\n\\nPrice is $\")[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c1205bd-4692-44ef-8ea4-69f255354537",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "description(train[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c79e2fe-1f50-4ebf-9a93-34f3088f2996",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in tqdm(range(0, len(train), 1000)):\n",
+    "    documents = [description(item) for item in train[i: i+1000]]\n",
+    "    vectors = model.encode(documents).astype(float).tolist()\n",
+    "    metadatas = [{\"category\": item.category, \"price\": item.price} for item in train[i: i+1000]]\n",
+    "    ids = [f\"doc_{j}\" for j in range(i, i+1000)]\n",
+    "    collection.add(\n",
+    "        ids=ids,\n",
+    "        documents=documents,\n",
+    "        embeddings=vectors,\n",
+    "        metadatas=metadatas\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a9395db-7bc9-47f9-902f-af8d380c9c09",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "745f73d9-f1a6-4e9f-96d9-1c38a1dd7559",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/week8/community_contributions/Ensemble_with_xgboost/Deploy_Specialist_Agent_to_Modal.ipynb b/week8/community_contributions/Ensemble_with_xgboost/Deploy_Specialist_Agent_to_Modal.ipynb
new file mode 100644
index 0000000..8e51070
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/Deploy_Specialist_Agent_to_Modal.ipynb
@@ -0,0 +1,104 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc0e1c1c-be6a-4395-bbbd-eeafc9330d7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import modal\n",
+    "import modal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d240622-8422-4c99-8464-c04d063e4cb6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !modal setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0050c070-146f-4c26-8045-5ff284761199",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ebf35de4-ef8f-4e5b-8d4e-9a1771bfbe25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ['PYTHONIOENCODING'] = 'utf-8'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f90d857-2f12-4521-bb90-28efd917f7d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!modal deploy pricer_service"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1dec70ff-1986-4405-8624-9bbbe0ce1f4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pricer = modal.Cls.from_name(\"pricer-service\", \"Pricer\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17776139-0d9e-4ad0-bcd0-82d3a92ca61f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pricer().price.remote(\"Quadcast HyperX condenser mic, connects via usb-c to your computer for crystal clear audio\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "deb6cdf6-bcb0-49fb-8671-bb5eb22f02e3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/week8/community_contributions/Ensemble_with_xgboost/Visualize_vectors.ipynb b/week8/community_contributions/Ensemble_with_xgboost/Visualize_vectors.ipynb
new file mode 100644
index 0000000..7721480
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/Visualize_vectors.ipynb
@@ -0,0 +1,195 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "993a2a24-1a58-42be-8034-6d116fb8d786",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import os\n",
+    "import re\n",
+    "import math\n",
+    "import json\n",
+    "from tqdm import tqdm\n",
+    "import random\n",
+    "from dotenv import load_dotenv\n",
+    "from huggingface_hub import login\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "from sentence_transformers import SentenceTransformer\n",
+    "from datasets import load_dataset\n",
+    "import chromadb\n",
+    "from items import Item\n",
+    "from sklearn.manifold import TSNE\n",
+    "import plotly.graph_objects as go"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cc1fe53-612f-4228-aa02-8758f4c2098f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MAXIMUM_DATAPOINTS = 30_000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4aab95e-d719-4476-b6e7-e248120df25a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DB = \"products_vectorstore\"\n",
+    "client = chromadb.PersistentClient(path=DB)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f95dafd-ab80-464e-ba8a-dec7a2424780",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "collection = client.get_or_create_collection('products')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "525fc313-8a16-4ac0-8c42-6a6d1ba1c9b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "CATEGORIES = ['Appliances', 'Automotive', 'Cell_Phones_and_Accessories', 'Electronics','Musical_Instruments', 'Office_Products', 'Tools_and_Home_Improvement', 'Toys_and_Games']\n",
+    "COLORS = ['red', 'blue', 'brown', 'orange', 'yellow', 'green' , 'purple', 'cyan']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4cf1c9a-1ced-48d4-974c-3c850905034e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prework\n",
+    "result = collection.get(include=['embeddings', 'documents', 'metadatas'], limit=MAXIMUM_DATAPOINTS)\n",
+    "vectors = np.array(result['embeddings'])\n",
+    "documents = result['documents']\n",
+    "categories = [metadata['category'] for metadata in result['metadatas']]\n",
+    "colors = [COLORS[CATEGORIES.index(c)] for c in categories]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c54df150-c8d8-4bc3-8877-6759691eeb42",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's try a 2D chart\n",
+    "tsne_2d = TSNE(n_components=2, random_state=42, n_jobs=-1)\n",
+    "reduced_vectors_2d = tsne_2d.fit_transform(vectors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c93457ab-d895-4d9c-8e5c-1173e2089cfd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's try 3D!\n",
+    "tsne_3d = TSNE(n_components=3, random_state=42, n_jobs=-1)\n",
+    "reduced_vectors_3d = tsne_3d.fit_transform(vectors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8fb2a63-24c5-4dce-9e63-aa208272f82d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the 2D scatter plot\n",
+    "fig = go.Figure(data=[go.Scatter(\n",
+    "    x=reduced_vectors_2d[:, 0],\n",
+    "    y=reduced_vectors_2d[:, 1],\n",
+    "    mode='markers',\n",
+    "    marker=dict(size=3, color=colors, opacity=0.7),\n",
+    ")])\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    title='2D Chroma Vectorstore Visualization',\n",
+    "    scene=dict(xaxis_title='x', yaxis_title='y'),\n",
+    "    width=1200,\n",
+    "    height=800,\n",
+    "    margin=dict(r=20, b=10, l=10, t=40)\n",
+    ")\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e4ae088-3d29-45d3-87a2-fea805fe2c65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Create the 3D scatter plot\n",
+    "fig = go.Figure(data=[go.Scatter3d(\n",
+    "    x=reduced_vectors_3d[:, 0],\n",
+    "    y=reduced_vectors_3d[:, 1],\n",
+    "    z=reduced_vectors_3d[:, 2],\n",
+    "    mode='markers',\n",
+    "    marker=dict(size=3, color=colors, opacity=0.7),\n",
+    ")])\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    title='3D Chroma Vector Store Visualization',\n",
+    "    scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n",
+    "    width=1200,\n",
+    "    height=800,\n",
+    "    margin=dict(r=20, b=10, l=10, t=40)\n",
+    ")\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a12d1e8-7da8-401d-8c8d-ba0098096ded",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/week8/community_contributions/Ensemble_with_xgboost/agents/agent.py b/week8/community_contributions/Ensemble_with_xgboost/agents/agent.py
new file mode 100644
index 0000000..fe09e18
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/agents/agent.py
@@ -0,0 +1,33 @@
+import logging
+
+class Agent:
+    """
+    An abstract superclass for Agents
+    Used to log messages in a way that can identify each Agent
+    """
+
+    # Foreground colors
+    RED = '\033[31m'
+    GREEN = '\033[32m'
+    YELLOW = '\033[33m'
+    BLUE = '\033[34m'
+    MAGENTA = '\033[35m'
+    CYAN = '\033[36m'
+    WHITE = '\033[37m'
+    
+    # Background color
+    BG_BLACK = '\033[40m'
+    
+    # Reset code to return to default color
+    RESET = '\033[0m'
+
+    name: str = ""
+    color: str = '\033[37m'
+
+    def log(self, message):
+        """
+        Log this as an info message, identifying the agent
+        """
+        color_code = self.BG_BLACK + self.color
+        message = f"[{self.name}] {message}"
+        logging.info(color_code + message + self.RESET)
\ No newline at end of file
diff --git a/week8/community_contributions/Ensemble_with_xgboost/agents/deals.py b/week8/community_contributions/Ensemble_with_xgboost/agents/deals.py
new file mode 100644
index 0000000..5fb8039
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/agents/deals.py
@@ -0,0 +1,109 @@
+from pydantic import BaseModel
+from typing import List, Dict, Self
+from bs4 import BeautifulSoup
+import re
+import feedparser
+from tqdm import tqdm
+import requests
+import time
+
+feeds = [
+    "https://www.dealnews.com/c142/Electronics/?rss=1",
+        "https://www.dealnews.com/c39/Computers/?rss=1",
+        "https://www.dealnews.com/c238/Automotive/?rss=1",
+        "https://www.dealnews.com/f1912/Smart-Home/?rss=1",
+        "https://www.dealnews.com/c196/Home-Garden/?rss=1",
+       ]
+
+def extract(html_snippet: str) -> str:
+    """
+    Use Beautiful Soup to clean up this HTML snippet and extract useful text
+    """
+    soup = BeautifulSoup(html_snippet, 'html.parser')
+    snippet_div = soup.find('div', class_='snippet summary')
+    
+    if snippet_div:
+        description = snippet_div.get_text(strip=True)
+        description = BeautifulSoup(description, 'html.parser').get_text()
+        description = re.sub('<[^<]+?>', '', description)
+        result = description.strip()
+    else:
+        result = html_snippet
+    return result.replace('\n', ' ')
+
+class ScrapedDeal:
+    """
+    A class to represent a Deal retrieved from an RSS feed
+    """
+    category: str
+    title: str
+    summary: str
+    url: str
+    details: str
+    features: str
+
+    def __init__(self, entry: Dict[str, str]):
+        """
+        Populate this instance based on the provided dict
+        """
+        self.title = entry['title']
+        self.summary = extract(entry['summary'])
+        self.url = entry['links'][0]['href']
+        stuff = requests.get(self.url).content
+        soup = BeautifulSoup(stuff, 'html.parser')
+        content = soup.find('div', class_='content-section').get_text()
+        content = content.replace('\nmore', '').replace('\n', ' ')
+        if "Features" in content:
+            self.details, self.features = content.split("Features")
+        else:
+            self.details = content
+            self.features = ""
+
+    def __repr__(self):
+        """
+        Return a string to describe this deal
+        """
+        return f"<{self.title}>"
+
+    def describe(self):
+        """
+        Return a longer string to describe this deal for use in calling a model
+        """
+        return f"Title: {self.title}\nDetails: {self.details.strip()}\nFeatures: {self.features.strip()}\nURL: {self.url}"
+
+    @classmethod
+    def fetch(cls, show_progress : bool = False) -> List[Self]:
+        """
+        Retrieve all deals from the selected RSS feeds
+        """
+        deals = []
+        feed_iter = tqdm(feeds) if show_progress else feeds
+        for feed_url in feed_iter:
+            feed = feedparser.parse(feed_url)
+            for entry in feed.entries[:10]:
+                deals.append(cls(entry))
+                time.sleep(0.5)
+        return deals
+
+class Deal(BaseModel):
+    """
+    A class to Represent a Deal with a summary description
+    """
+    product_description: str
+    price: float
+    url: str
+
+class DealSelection(BaseModel):
+    """
+    A class to Represent a list of Deals
+    """
+    deals: List[Deal]
+
+class Opportunity(BaseModel):
+    """
+    A class to represent a possible opportunity: a Deal where we estimate
+    it should cost more than it's being offered
+    """
+    deal: Deal
+    estimate: float
+    discount: float
\ No newline at end of file
diff --git a/week8/community_contributions/Ensemble_with_xgboost/agents/ensemble_agent.py b/week8/community_contributions/Ensemble_with_xgboost/agents/ensemble_agent.py
new file mode 100644
index 0000000..c46e523
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/agents/ensemble_agent.py
@@ -0,0 +1,52 @@
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+import joblib
+
+from agents.agent import Agent
+from agents.specialist_agent import SpecialistAgent
+from agents.frontier_agent import FrontierAgent
+from agents.random_forest_agent import RandomForestAgent
+from agents.gradient_boosting_agent import GradientBoostingAgent
+
+class EnsembleAgent(Agent):
+
+    name = "Ensemble Agent"
+    color = Agent.YELLOW
+    
+    def __init__(self, collection):
+        """
+        Create an instance of Ensemble, by creating each of the models
+        And loading the weights of the Ensemble
+        """
+        self.log("Initializing Ensemble Agent")
+        self.specialist = SpecialistAgent()
+        self.frontier = FrontierAgent(collection)
+        self.random_forest = RandomForestAgent()
+        self.gradient_boosting = GradientBoostingAgent()
+        self.model = joblib.load('ensemble_model.pkl')
+        self.log("Ensemble Agent is ready")
+
+    def price(self, description: str) -> float:
+        """
+        Run this ensemble model
+        Ask each of the models to price the product
+        Then use the Linear Regression model to return the weighted price
+        :param description: the description of a product
+        :return: an estimate of its price
+        """
+        self.log("Running Ensemble Agent - collaborating with specialist, frontier and random forest agents")
+        specialist = self.specialist.price(description)
+        frontier = self.frontier.price(description)
+        random_forest = self.random_forest.price(description)
+        gradient_boosting = self.gradient_boosting.price(description)
+        X = pd.DataFrame({
+            'Specialist': [specialist],
+            'Frontier': [frontier],
+            'RandomForest': [random_forest],
+            'GradientBoosting': [gradient_boosting],
+            'Min': [min(specialist, frontier, random_forest)],
+            'Max': [max(specialist, frontier, random_forest)],
+        })
+        y = max(0, self.model.predict(X)[0])
+        self.log(f"Ensemble Agent complete - returning ${y:.2f}")
+        return y
\ No newline at end of file
diff --git a/week8/community_contributions/Ensemble_with_xgboost/agents/frontier_agent.py b/week8/community_contributions/Ensemble_with_xgboost/agents/frontier_agent.py
new file mode 100644
index 0000000..590c9e8
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/agents/frontier_agent.py
@@ -0,0 +1,109 @@
+# imports
+
+import os
+import re
+import math
+import json
+from typing import List, Dict
+import openai 
+from openai import OpenAI
+from sentence_transformers import SentenceTransformer
+from datasets import load_dataset
+import chromadb
+from items import Item
+from testing import Tester
+from agents.agent import Agent
+
+
+class FrontierAgent(Agent):
+
+    name = "Frontier Agent"
+    color = Agent.BLUE
+
+    MODEL = "gpt-4o-mini"
+    
+    def __init__(self, collection):
+        """
+        Set up this instance by connecting to OpenAI or DeepSeek, to the Chroma Datastore,
+        And setting up the vector encoding model
+        """
+        self.log("Initializing Frontier Agent")
+        openai.api_key = os.getenv("OPENAI_API_KEY")
+        self.client = OpenAI()
+        self.MODEL = "gpt-4o-mini"
+        self.log("Frontier Agent is setting up with OpenAI")
+        self.collection = collection
+        self.model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+        self.log("Frontier Agent is ready")
+
+    def make_context(self, similars: List[str], prices: List[float]) -> str:
+        """
+        Create context that can be inserted into the prompt
+        :param similars: similar products to the one being estimated
+        :param prices: prices of the similar products
+        :return: text to insert in the prompt that provides context
+        """
+        message = "To provide some context, here are some other items that might be similar to the item you need to estimate.\n\n"
+        for similar, price in zip(similars, prices):
+            message += f"Potentially related product:\n{similar}\nPrice is ${price:.2f}\n\n"
+        return message
+
+    def messages_for(self, description: str, similars: List[str], prices: List[float]) -> List[Dict[str, str]]:
+        """
+        Create the message list to be included in a call to OpenAI
+        With the system and user prompt
+        :param description: a description of the product
+        :param similars: similar products to this one
+        :param prices: prices of similar products
+        :return: the list of messages in the format expected by OpenAI
+        """
+        system_message = "You estimate prices of items. Reply only with the price, no explanation. Price is always below $1000."
+        user_prompt = self.make_context(similars, prices)
+        user_prompt += "And now the question for you:\n\n"
+        user_prompt += "How much does this cost?\n\n" + description
+        return [
+            {"role": "system", "content": system_message},
+            {"role": "user", "content": user_prompt},
+            {"role": "assistant", "content": "Price is $"}
+        ]
+
+    def find_similars(self, description: str):
+        """
+        Return a list of items similar to the given one by looking in the Chroma datastore
+        """
+        self.log("Frontier Agent is performing a RAG search of the Chroma datastore to find 5 similar products")
+        vector = self.model.encode([description])
+        results = self.collection.query(query_embeddings=vector.astype(float).tolist(), n_results=5)
+        documents = results['documents'][0][:]
+        prices = [m['price'] for m in results['metadatas'][0][:]]
+        self.log("Frontier Agent has found similar products")
+        return documents, prices
+
+    def get_price(self, s) -> float:
+        """
+        A utility that plucks a floating point number out of a string
+        """
+        s = s.replace('$','').replace(',','')
+        match = re.search(r"[-+]?\d*\.\d+|\d+", s)
+        return float(match.group()) if match else 0.0
+
+    def price(self, description: str) -> float:
+        """
+        Make a call to OpenAI to estimate the price of the described product,
+        by looking up 5 similar products and including them in the prompt to give context
+        :param description: a description of the product
+        :return: an estimate of the price
+        """
+        documents, prices = self.find_similars(description)
+        self.log(f"Frontier Agent is about to call {self.MODEL} with context including 5 similar products")
+        response = self.client.chat.completions.create(
+            model=self.MODEL, 
+            messages=self.messages_for(description, documents, prices),
+            seed=42,
+            max_tokens=5
+        )
+        reply = response.choices[0].message.content
+        result = self.get_price(reply)
+        self.log(f"Frontier Agent completed - predicting ${result:.2f}")
+        return result
+        
\ No newline at end of file
diff --git a/week8/community_contributions/Ensemble_with_xgboost/agents/gradient_boosting_agent.py b/week8/community_contributions/Ensemble_with_xgboost/agents/gradient_boosting_agent.py
new file mode 100644
index 0000000..01274fb
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/agents/gradient_boosting_agent.py
@@ -0,0 +1,37 @@
+# imports
+
+import os
+import re
+from typing import List
+from sentence_transformers import SentenceTransformer
+import joblib
+from agents.agent import Agent
+
+
+
+class GradientBoostingAgent(Agent):
+
+    name = "Gradient Boosting Agent"
+    color = Agent.MAGENTA
+
+    def __init__(self):
+        """
+        Initialize this object by loading in the saved model weights
+        and the SentenceTransformer vector encoding model
+        """
+        self.log("Gradient Boosting Agent is initializing")
+        self.vectorizer = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+        self.model = joblib.load('gradient_boosting_model.pkl')
+        self.log("Gradient Boosting Agent is ready")
+
+    def price(self, description: str) -> float:
+        """
+        Use a Random Forest model to estimate the price of the described item
+        :param description: the product to be estimated
+        :return: the price as a float
+        """        
+        self.log("Gradient Boosting Agent is starting a prediction")
+        vector = self.vectorizer.encode([description])
+        result = max(0, self.model.predict(vector)[0])
+        self.log(f"Gradient Boosting Agent completed - predicting ${result:.2f}")
+        return result
\ No newline at end of file
diff --git a/week8/community_contributions/Ensemble_with_xgboost/agents/messaging_agent.py b/week8/community_contributions/Ensemble_with_xgboost/agents/messaging_agent.py
new file mode 100644
index 0000000..7494703
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/agents/messaging_agent.py
@@ -0,0 +1,79 @@
+import os
+# from twilio.rest import Client
+from agents.deals import Opportunity
+import http.client
+import urllib
+from agents.agent import Agent
+
+# Uncomment the Twilio lines if you wish to use Twilio
+
+DO_TEXT = False
+DO_PUSH = True
+
+class MessagingAgent(Agent):
+
+    name = "Messaging Agent"
+    color = Agent.WHITE
+
+    def __init__(self):
+        """
+        Set up this object to either do push notifications via Pushover,
+        or SMS via Twilio,
+        whichever is specified in the constants
+        """
+        self.log(f"Messaging Agent is initializing")
+        if DO_TEXT:
+            account_sid = os.getenv('TWILIO_ACCOUNT_SID', 'your-sid-if-not-using-env')
+            auth_token = os.getenv('TWILIO_AUTH_TOKEN', 'your-auth-if-not-using-env')
+            self.me_from = os.getenv('TWILIO_FROM', 'your-phone-number-if-not-using-env')
+            self.me_to = os.getenv('MY_PHONE_NUMBER', 'your-phone-number-if-not-using-env')
+            # self.client = Client(account_sid, auth_token)
+            self.log("Messaging Agent has initialized Twilio")
+        if DO_PUSH:
+            self.pushover_user = os.getenv('PUSHOVER_USER', 'your-pushover-user-if-not-using-env')
+            self.pushover_token = os.getenv('PUSHOVER_TOKEN', 'your-pushover-user-if-not-using-env')
+            self.log("Messaging Agent has initialized Pushover")
+
+    def message(self, text):
+        """
+        Send an SMS message using the Twilio API
+        """
+        self.log("Messaging Agent is sending a text message")
+        message = self.client.messages.create(
+          from_=self.me_from,
+          body=text,
+          to=self.me_to
+        )
+
+    def push(self, text):
+        """
+        Send a Push Notification using the Pushover API
+        """
+        self.log("Messaging Agent is sending a push notification")
+        conn = http.client.HTTPSConnection("api.pushover.net:443")
+        conn.request("POST", "/1/messages.json",
+          urllib.parse.urlencode({
+            "token": self.pushover_token,
+            "user": self.pushover_user,
+            "message": text,
+            "sound": "cashregister"
+          }), { "Content-type": "application/x-www-form-urlencoded" })
+        conn.getresponse()
+
+    def alert(self, opportunity: Opportunity):
+        """
+        Make an alert about the specified Opportunity
+        """
+        text = f"Deal Alert! Price=${opportunity.deal.price:.2f}, "
+        text += f"Estimate=${opportunity.estimate:.2f}, "
+        text += f"Discount=${opportunity.discount:.2f} :"
+        text += opportunity.deal.product_description[:10]+'... '
+        text += opportunity.deal.url
+        if DO_TEXT:
+            self.message(text)
+        if DO_PUSH:
+            self.push(text)
+        self.log("Messaging Agent has completed")
+        
+    
+        
\ No newline at end of file
diff --git a/week8/community_contributions/Ensemble_with_xgboost/agents/planning_agent.py b/week8/community_contributions/Ensemble_with_xgboost/agents/planning_agent.py
new file mode 100644
index 0000000..547536a
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/agents/planning_agent.py
@@ -0,0 +1,57 @@
+from typing import Optional, List
+from agents.agent import Agent
+from agents.deals import ScrapedDeal, DealSelection, Deal, Opportunity
+from agents.scanner_agent import ScannerAgent
+from agents.ensemble_agent import EnsembleAgent
+from agents.messaging_agent import MessagingAgent
+
+
+class PlanningAgent(Agent):
+
+    name = "Planning Agent"
+    color = Agent.GREEN
+    DEAL_THRESHOLD = 50
+
+    def __init__(self, collection):
+        """
+        Create instances of the 3 Agents that this planner coordinates across
+        """
+        self.log("Planning Agent is initializing")
+        self.scanner = ScannerAgent()
+        self.ensemble = EnsembleAgent(collection)
+        self.messenger = MessagingAgent()
+        self.log("Planning Agent is ready")
+
+    def run(self, deal: Deal) -> Opportunity:
+        """
+        Run the workflow for a particular deal
+        :param deal: the deal, summarized from an RSS scrape
+        :returns: an opportunity including the discount
+        """
+        self.log("Planning Agent is pricing up a potential deal")
+        estimate = self.ensemble.price(deal.product_description)
+        discount = estimate - deal.price
+        self.log(f"Planning Agent has processed a deal with discount ${discount:.2f}")
+        return Opportunity(deal=deal, estimate=estimate, discount=discount)
+
+    def plan(self, memory: List[str] = []) -> Optional[Opportunity]:
+        """
+        Run the full workflow:
+        1. Use the ScannerAgent to find deals from RSS feeds
+        2. Use the EnsembleAgent to estimate them
+        3. Use the MessagingAgent to send a notification of deals
+        :param memory: a list of URLs that have been surfaced in the past
+        :return: an Opportunity if one was surfaced, otherwise None
+        """
+        self.log("Planning Agent is kicking off a run")
+        selection = self.scanner.scan(memory=memory)
+        if selection:
+            opportunities = [self.run(deal) for deal in selection.deals[:5]]
+            opportunities.sort(key=lambda opp: opp.discount, reverse=True)
+            best = opportunities[0]
+            self.log(f"Planning Agent has identified the best deal has discount ${best.discount:.2f}")
+            if best.discount > self.DEAL_THRESHOLD:
+                self.messenger.alert(best)
+            self.log("Planning Agent has completed a run")
+            return best if best.discount > self.DEAL_THRESHOLD else None
+        return None
\ No newline at end of file
diff --git a/week8/community_contributions/Ensemble_with_xgboost/agents/random_forest_agent.py b/week8/community_contributions/Ensemble_with_xgboost/agents/random_forest_agent.py
new file mode 100644
index 0000000..bfe9715
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/agents/random_forest_agent.py
@@ -0,0 +1,37 @@
+# imports
+
+import os
+import re
+from typing import List
+from sentence_transformers import SentenceTransformer
+import joblib
+from agents.agent import Agent
+
+
+
+class RandomForestAgent(Agent):
+
+    name = "Random Forest Agent"
+    color = Agent.MAGENTA
+
+    def __init__(self):
+        """
+        Initialize this object by loading in the saved model weights
+        and the SentenceTransformer vector encoding model
+        """
+        self.log("Random Forest Agent is initializing")
+        self.vectorizer = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+        self.model = joblib.load('random_forest_model.pkl')
+        self.log("Random Forest Agent is ready")
+
+    def price(self, description: str) -> float:
+        """
+        Use a Random Forest model to estimate the price of the described item
+        :param description: the product to be estimated
+        :return: the price as a float
+        """        
+        self.log("Random Forest Agent is starting a prediction")
+        vector = self.vectorizer.encode([description])
+        result = max(0, self.model.predict(vector)[0])
+        self.log(f"Random Forest Agent completed - predicting ${result:.2f}")
+        return result
\ No newline at end of file
diff --git a/week8/community_contributions/Ensemble_with_xgboost/agents/scanner_agent.py b/week8/community_contributions/Ensemble_with_xgboost/agents/scanner_agent.py
new file mode 100644
index 0000000..8dc6674
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/agents/scanner_agent.py
@@ -0,0 +1,94 @@
+import os
+import json
+from typing import Optional, List
+from openai import OpenAI
+from agents.deals import ScrapedDeal, DealSelection
+from agents.agent import Agent
+
+
+class ScannerAgent(Agent):
+
+    MODEL = "gpt-4o-mini"
+
+    SYSTEM_PROMPT = """You identify and summarize the 5 most detailed deals from a list, by selecting deals that have the most detailed, high quality description and the most clear price.
+    Respond strictly in JSON with no explanation, using this format. You should provide the price as a number derived from the description. If the price of a deal isn't clear, do not include that deal in your response.
+    Most important is that you respond with the 5 deals that have the most detailed product description with price. It's not important to mention the terms of the deal; most important is a thorough description of the product.
+    Be careful with products that are described as "$XXX off" or "reduced by $XXX" - this isn't the actual price of the product. Only respond with products when you are highly confident about the price. 
+    
+    {"deals": [
+        {
+            "product_description": "Your clearly expressed summary of the product in 4-5 sentences. Details of the item are much more important than why it's a good deal. Avoid mentioning discounts and coupons; focus on the item itself. There should be a paragpraph of text for each item you choose.",
+            "price": 99.99,
+            "url": "the url as provided"
+        },
+        ...
+    ]}"""
+    
+    USER_PROMPT_PREFIX = """Respond with the most promising 5 deals from this list, selecting those which have the most detailed, high quality product description and a clear price that is greater than 0.
+    Respond strictly in JSON, and only JSON. You should rephrase the description to be a summary of the product itself, not the terms of the deal.
+    Remember to respond with a paragraph of text in the product_description field for each of the 5 items that you select.
+    Be careful with products that are described as "$XXX off" or "reduced by $XXX" - this isn't the actual price of the product. Only respond with products when you are highly confident about the price. 
+    
+    Deals:
+    
+    """
+
+    USER_PROMPT_SUFFIX = "\n\nStrictly respond in JSON and include exactly 5 deals, no more."
+
+    name = "Scanner Agent"
+    color = Agent.CYAN
+
+    def __init__(self):
+        """
+        Set up this instance by initializing OpenAI
+        """
+        self.log("Scanner Agent is initializing")
+        self.openai = OpenAI()
+        self.log("Scanner Agent is ready")
+
+    def fetch_deals(self, memory) -> List[ScrapedDeal]:
+        """
+        Look up deals published on RSS feeds
+        Return any new deals that are not already in the memory provided
+        """
+        self.log("Scanner Agent is about to fetch deals from RSS feed")
+        urls = [opp.deal.url for opp in memory]
+        scraped = ScrapedDeal.fetch()
+        result = [scrape for scrape in scraped if scrape.url not in urls]
+        self.log(f"Scanner Agent received {len(result)} deals not already scraped")
+        return result
+
+    def make_user_prompt(self, scraped) -> str:
+        """
+        Create a user prompt for OpenAI based on the scraped deals provided
+        """
+        user_prompt = self.USER_PROMPT_PREFIX
+        user_prompt += '\n\n'.join([scrape.describe() for scrape in scraped])
+        user_prompt += self.USER_PROMPT_SUFFIX
+        return user_prompt
+
+    def scan(self, memory: List[str]=[]) -> Optional[DealSelection]:
+        """
+        Call OpenAI to provide a high potential list of deals with good descriptions and prices
+        Use StructuredOutputs to ensure it conforms to our specifications
+        :param memory: a list of URLs representing deals already raised
+        :return: a selection of good deals, or None if there aren't any
+        """
+        scraped = self.fetch_deals(memory)
+        if scraped:
+            user_prompt = self.make_user_prompt(scraped)
+            self.log("Scanner Agent is calling OpenAI using Structured Output")
+            result = self.openai.beta.chat.completions.parse(
+                model=self.MODEL,
+                messages=[
+                    {"role": "system", "content": self.SYSTEM_PROMPT},
+                    {"role": "user", "content": user_prompt}
+              ],
+                response_format=DealSelection
+            )
+            result = result.choices[0].message.parsed
+            result.deals = [deal for deal in result.deals if deal.price>0]
+            self.log(f"Scanner Agent received {len(result.deals)} selected deals with price>0 from OpenAI")
+            return result
+        return None
+                
diff --git a/week8/community_contributions/Ensemble_with_xgboost/agents/specialist_agent.py b/week8/community_contributions/Ensemble_with_xgboost/agents/specialist_agent.py
new file mode 100644
index 0000000..1bab0d5
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/agents/specialist_agent.py
@@ -0,0 +1,29 @@
+import modal
+from agents.agent import Agent
+
+
+class SpecialistAgent(Agent):
+    """
+    An Agent that runs our fine-tuned LLM that's running remotely on Modal
+    """
+
+    name = "Specialist Agent"
+    color = Agent.RED
+
+    def __init__(self):
+        """
+        Set up this Agent by creating an instance of the modal class
+        """
+        self.log("Specialist Agent is initializing - connecting to modal")
+        Pricer = modal.Cls.from_name("pricer-service", "Pricer")
+        self.pricer = Pricer()
+        self.log("Specialist Agent is ready")
+        
+    def price(self, description: str) -> float:
+        """
+        Make a remote call to return the estimate of the price of this item
+        """
+        self.log("Specialist Agent is calling remote fine-tuned model")
+        result = self.pricer.price.remote(description)
+        self.log(f"Specialist Agent completed - predicting ${result:.2f}")
+        return result
diff --git a/week8/community_contributions/Ensemble_with_xgboost/deal_agent_framework.py b/week8/community_contributions/Ensemble_with_xgboost/deal_agent_framework.py
new file mode 100644
index 0000000..9692107
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/deal_agent_framework.py
@@ -0,0 +1,99 @@
+import os
+import sys
+import logging
+import json
+from typing import List, Optional
+from twilio.rest import Client
+from dotenv import load_dotenv
+import chromadb
+from agents.planning_agent import PlanningAgent
+from agents.deals import Opportunity
+from sklearn.manifold import TSNE
+import numpy as np
+
+
+# Colors for logging
+BG_BLUE = '\033[44m'
+WHITE = '\033[37m'
+RESET = '\033[0m'
+
+# Colors for plot
+CATEGORIES = ['Appliances', 'Automotive', 'Cell_Phones_and_Accessories', 'Electronics','Musical_Instruments', 'Office_Products', 'Tools_and_Home_Improvement', 'Toys_and_Games']
+COLORS = ['red', 'blue', 'brown', 'orange', 'yellow', 'green' , 'purple', 'cyan']
+
+def init_logging():
+    root = logging.getLogger()
+    root.setLevel(logging.INFO)
+    
+    handler = logging.StreamHandler(sys.stdout)
+    handler.setLevel(logging.INFO)
+    formatter = logging.Formatter(
+        "[%(asctime)s] [Agents] [%(levelname)s] %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S %z",
+    )
+    handler.setFormatter(formatter)
+    root.addHandler(handler)
+
+class DealAgentFramework:
+
+    DB = "products_vectorstore"
+    MEMORY_FILENAME = "memory.json"
+
+    def __init__(self):
+        init_logging()
+        load_dotenv()
+        client = chromadb.PersistentClient(path=self.DB)
+        self.memory = self.read_memory()
+        self.collection = client.get_or_create_collection('products')
+        self.planner = None
+
+    def init_agents_as_needed(self):
+        if not self.planner:
+            self.log("Initializing Agent Framework")
+            self.planner = PlanningAgent(self.collection)
+            self.log("Agent Framework is ready")
+        
+    def read_memory(self) -> List[Opportunity]:
+        if os.path.exists(self.MEMORY_FILENAME):
+            with open(self.MEMORY_FILENAME, "r") as file:
+                data = json.load(file)
+            opportunities = [Opportunity(**item) for item in data]
+            return opportunities
+        return []
+
+    def write_memory(self) -> None:
+        data = [opportunity.dict() for opportunity in self.memory]
+        with open(self.MEMORY_FILENAME, "w") as file:
+            json.dump(data, file, indent=2)
+
+    def log(self, message: str):
+        text = BG_BLUE + WHITE + "[Agent Framework] " + message + RESET
+        logging.info(text)
+
+    def run(self) -> List[Opportunity]:
+        self.init_agents_as_needed()
+        logging.info("Kicking off Planning Agent")
+        result = self.planner.plan(memory=self.memory)
+        logging.info(f"Planning Agent has completed and returned: {result}")
+        if result:
+            self.memory.append(result)
+            self.write_memory()
+        return self.memory
+
+    @classmethod
+    def get_plot_data(cls, max_datapoints=10000):
+        client = chromadb.PersistentClient(path=cls.DB)
+        collection = client.get_or_create_collection('products')
+        result = collection.get(include=['embeddings', 'documents', 'metadatas'], limit=max_datapoints)
+        vectors = np.array(result['embeddings'])
+        documents = result['documents']
+        categories = [metadata['category'] for metadata in result['metadatas']]
+        colors = [COLORS[CATEGORIES.index(c)] for c in categories]
+        tsne = TSNE(n_components=3, random_state=42, n_jobs=-1)
+        reduced_vectors = tsne.fit_transform(vectors)
+        return documents, reduced_vectors, colors
+
+
+if __name__=="__main__":
+    DealAgentFramework().run()
+    
\ No newline at end of file
diff --git a/week8/community_contributions/Ensemble_with_xgboost/items.py b/week8/community_contributions/Ensemble_with_xgboost/items.py
new file mode 100644
index 0000000..1acaf5d
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/items.py
@@ -0,0 +1,101 @@
+from typing import Optional
+from transformers import AutoTokenizer
+import re
+
+BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B"
+MIN_TOKENS = 150
+MAX_TOKENS = 160
+MIN_CHARS = 300
+CEILING_CHARS = MAX_TOKENS * 7
+
+class Item:
+    """
+    An Item is a cleaned, curated datapoint of a Product with a Price
+    """
+    
+    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
+    PREFIX = "Price is $"
+    QUESTION = "How much does this cost to the nearest dollar?"
+    REMOVALS = ['"Batteries Included?": "No"', '"Batteries Included?": "Yes"', '"Batteries Required?": "No"', '"Batteries Required?": "Yes"', "By Manufacturer", "Item", "Date First", "Package", ":", "Number of", "Best Sellers", "Number", "Product "]
+
+    title: str
+    price: float
+    category: str
+    token_count: int = 0
+    details: Optional[str]
+    prompt: Optional[str] = None
+    include = False
+
+    def __init__(self, data, price):
+        self.title = data['title']
+        self.price = price
+        self.parse(data)
+
+    def scrub_details(self):
+        """
+        Clean up the details string by removing common text that doesn't add value
+        """
+        details = self.details
+        for remove in self.REMOVALS:
+            details = details.replace(remove, "")
+        return details
+
+    def scrub(self, stuff):
+        """
+        Clean up the provided text by removing unnecessary characters and whitespace
+        Also remove words that are 7+ chars and contain numbers, as these are likely irrelevant product numbers
+        """
+        stuff = re.sub(r'[:\[\]"{}【】\s]+', ' ', stuff).strip()
+        stuff = stuff.replace(" ,", ",").replace(",,,",",").replace(",,",",")
+        words = stuff.split(' ')
+        select = [word for word in words if len(word)<7 or not any(char.isdigit() for char in word)]
+        return " ".join(select)
+    
+    def parse(self, data):
+        """
+        Parse this datapoint and if it fits within the allowed Token range,
+        then set include to True
+        """
+        contents = '\n'.join(data['description'])
+        if contents:
+            contents += '\n'
+        features = '\n'.join(data['features'])
+        if features:
+            contents += features + '\n'
+        self.details = data['details']
+        if self.details:
+            contents += self.scrub_details() + '\n'
+        if len(contents) > MIN_CHARS:
+            contents = contents[:CEILING_CHARS]
+            text = f"{self.scrub(self.title)}\n{self.scrub(contents)}"
+            tokens = self.tokenizer.encode(text, add_special_tokens=False)
+            if len(tokens) > MIN_TOKENS:
+                tokens = tokens[:MAX_TOKENS]
+                text = self.tokenizer.decode(tokens)
+                self.make_prompt(text)
+                self.include = True
+
+    def make_prompt(self, text):
+        """
+        Set the prompt instance variable to be a prompt appropriate for training
+        """
+        self.prompt = f"{self.QUESTION}\n\n{text}\n\n"
+        self.prompt += f"{self.PREFIX}{str(round(self.price))}.00"
+        self.token_count = len(self.tokenizer.encode(self.prompt, add_special_tokens=False))
+
+    def test_prompt(self):
+        """
+        Return a prompt suitable for testing, with the actual price removed
+        """
+        return self.prompt.split(self.PREFIX)[0] + self.PREFIX
+
+    def __repr__(self):
+        """
+        Return a String version of this Item
+        """
+        return f"<{self.title} = ${self.price}>"
+
+        
+
+    
+    
\ No newline at end of file
diff --git a/week8/community_contributions/Ensemble_with_xgboost/log_utils.py b/week8/community_contributions/Ensemble_with_xgboost/log_utils.py
new file mode 100644
index 0000000..8bc33fb
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/log_utils.py
@@ -0,0 +1,35 @@
+# Foreground colors
+RED = '\033[31m'
+GREEN = '\033[32m'
+YELLOW = '\033[33m'
+BLUE = '\033[34m'
+MAGENTA = '\033[35m'
+CYAN = '\033[36m'
+WHITE = '\033[37m'
+
+# Background color
+BG_BLACK = '\033[40m'
+BG_BLUE = '\033[44m'
+
+# Reset code to return to default color
+RESET = '\033[0m'
+
+mapper = {
+    BG_BLACK+RED: "#dd0000",
+    BG_BLACK+GREEN: "#00dd00",
+    BG_BLACK+YELLOW: "#dddd00",
+    BG_BLACK+BLUE: "#0000ee",
+    BG_BLACK+MAGENTA: "#aa00dd",
+    BG_BLACK+CYAN: "#00dddd",
+    BG_BLACK+WHITE: "#87CEEB",
+    BG_BLUE+WHITE: "#ff7800"
+}
+
+
+def reformat(message):
+    for key, value in mapper.items():
+        message = message.replace(key, f'<span style="color: {value}">')
+    message = message.replace(RESET, '</span>')
+    return message
+    
+    
\ No newline at end of file
diff --git a/week8/community_contributions/Ensemble_with_xgboost/price_is_right.py b/week8/community_contributions/Ensemble_with_xgboost/price_is_right.py
new file mode 100644
index 0000000..bc9b537
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/price_is_right.py
@@ -0,0 +1,62 @@
+import gradio as gr
+from deal_agent_framework import DealAgentFramework
+from agents.deals import Opportunity, Deal
+
+class App:
+
+    def __init__(self):    
+        self.agent_framework = None
+
+    def run(self):
+        with gr.Blocks(title="Deal Intel", fill_width=True) as ui:
+        
+            def table_for(opps):
+                return [[opp.deal.product_description, f"${opp.deal.price:.2f}", f"${opp.estimate:.2f}", f"${opp.discount:.2f}", opp.deal.url] for opp in opps]
+        
+            def start():
+                self.agent_framework = DealAgentFramework()
+                self.agent_framework.init_agents_as_needed()
+                opportunities = self.agent_framework.memory
+                table = table_for(opportunities)
+                return table
+        
+            def go():
+                self.agent_framework.run()
+                new_opportunities = self.agent_framework.memory
+                table = table_for(new_opportunities)
+                return table
+        
+            def do_select(selected_index: gr.SelectData):
+                opportunities = self.agent_framework.memory
+                row = selected_index.index[0]
+                opportunity = opportunities[row]
+                self.agent_framework.planner.messenger.alert(opportunity)
+        
+            with gr.Row():
+                gr.Markdown('<div style="text-align: center;font-size:24px">"Deal Intel" - Deal Hunting Agentic AI</div>')
+            with gr.Row():
+                gr.Markdown('<div style="text-align: center;font-size:14px">Autonomous agent framework that finds online deals, collaborating with a proprietary fine-tuned LLM deployed on Modal, and a RAG pipeline with a frontier model and Chroma.</div>')
+            with gr.Row():
+                gr.Markdown('<div style="text-align: center;font-size:14px">Deals surfaced so far:</div>')
+            with gr.Row():
+                opportunities_dataframe = gr.Dataframe(
+                    headers=["Description", "Price", "Estimate", "Discount", "URL"],
+                    wrap=True,
+                    column_widths=[4, 1, 1, 1, 2],
+                    row_count=10,
+                    col_count=5,
+                    max_height=400,
+                )
+        
+            ui.load(start, inputs=[], outputs=[opportunities_dataframe])
+
+            timer = gr.Timer(value=60)
+            timer.tick(go, inputs=[], outputs=[opportunities_dataframe])
+
+            opportunities_dataframe.select(do_select)
+        
+        ui.launch(share=False, inbrowser=True)
+
+if __name__=="__main__":
+    App().run()
+    
\ No newline at end of file
diff --git a/week8/community_contributions/Ensemble_with_xgboost/price_is_right_final.py b/week8/community_contributions/Ensemble_with_xgboost/price_is_right_final.py
new file mode 100644
index 0000000..fb242f1
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/price_is_right_final.py
@@ -0,0 +1,166 @@
+import logging
+import queue
+import threading
+import time
+import gradio as gr
+from deal_agent_framework import DealAgentFramework
+from agents.deals import Opportunity, Deal
+from log_utils import reformat
+import plotly.graph_objects as go
+
+
+class QueueHandler(logging.Handler):
+    def __init__(self, log_queue):
+        super().__init__()
+        self.log_queue = log_queue
+
+    def emit(self, record):
+        self.log_queue.put(self.format(record))
+
+def html_for(log_data):
+    output = '<br>'.join(log_data[-18:])
+    return f"""
+    <div id="scrollContent" style="height: 400px; overflow-y: auto; border: 1px solid #ccc; background-color: #222229; padding: 10px;">
+    {output}
+    </div>
+    """
+
+def setup_logging(log_queue):
+    handler = QueueHandler(log_queue)
+    formatter = logging.Formatter(
+        "[%(asctime)s] %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S %z",
+    )
+    handler.setFormatter(formatter)
+    logger = logging.getLogger()
+    logger.addHandler(handler)
+    logger.setLevel(logging.INFO)
+                
+
+class App:
+
+    def __init__(self):    
+        self.agent_framework = None
+
+    def get_agent_framework(self):
+        if not self.agent_framework:
+            self.agent_framework = DealAgentFramework()
+            self.agent_framework.init_agents_as_needed()
+        return self.agent_framework
+
+    def run(self):
+        with gr.Blocks(title="Deal Intel", fill_width=True) as ui:
+            
+            log_data = gr.State([])
+            
+            def table_for(opps):
+                return [[opp.deal.product_description, f"${opp.deal.price:.2f}", f"${opp.estimate:.2f}", f"${opp.discount:.2f}", opp.deal.url] for opp in opps]
+
+            def update_output(log_data, log_queue, result_queue):
+                initial_result = table_for(self.get_agent_framework().memory)
+                final_result = None
+                while True:
+                    try:
+                        message = log_queue.get_nowait()
+                        log_data.append(reformat(message))
+                        yield log_data, html_for(log_data), final_result or initial_result
+                    except queue.Empty:
+                        try:
+                            final_result = result_queue.get_nowait()
+                            yield log_data, html_for(log_data), final_result or initial_result
+                        except queue.Empty:
+                            if final_result is not None:
+                                break
+                            time.sleep(0.1)
+
+            def get_initial_plot():
+                fig = go.Figure()
+                fig.update_layout(
+                    title='Loading vector DB...',
+                    height=400,
+                )
+                return fig
+
+            def get_plot():
+                documents, vectors, colors = DealAgentFramework.get_plot_data(max_datapoints=1000)
+                # Create the 3D scatter plot
+                fig = go.Figure(data=[go.Scatter3d(
+                    x=vectors[:, 0],
+                    y=vectors[:, 1],
+                    z=vectors[:, 2],
+                    mode='markers',
+                    marker=dict(size=2, color=colors, opacity=0.7),
+                )])
+                
+                fig.update_layout(
+                    scene=dict(xaxis_title='x', 
+                               yaxis_title='y', 
+                               zaxis_title='z',
+                               aspectmode='manual',
+                               aspectratio=dict(x=2.2, y=2.2, z=1),  # Make x-axis twice as long
+                               camera=dict(
+                                   eye=dict(x=1.6, y=1.6, z=0.8)  # Adjust camera position
+                               )),
+                    height=400,
+                    margin=dict(r=5, b=1, l=5, t=2)
+                )
+
+                return fig
+        
+            def do_run():
+                new_opportunities = self.get_agent_framework().run()
+                table = table_for(new_opportunities)
+                return table
+
+            def run_with_logging(initial_log_data):
+                log_queue = queue.Queue()
+                result_queue = queue.Queue()
+                setup_logging(log_queue)
+                
+                def worker():
+                    result = do_run()
+                    result_queue.put(result)
+                
+                thread = threading.Thread(target=worker)
+                thread.start()
+                
+                for log_data, output, final_result in update_output(initial_log_data, log_queue, result_queue):
+                    yield log_data, output, final_result
+
+            def do_select(selected_index: gr.SelectData):
+                opportunities = self.get_agent_framework().memory
+                row = selected_index.index[0]
+                opportunity = opportunities[row]
+                self.get_agent_framework().planner.messenger.alert(opportunity)
+        
+            with gr.Row():
+                gr.Markdown('<div style="text-align: center;font-size:24px"><strong>Deal Intel</strong> - Autonomous Agent Framework that hunts for deals</div>')
+            with gr.Row():
+                gr.Markdown('<div style="text-align: center;font-size:14px">A proprietary fine-tuned LLM deployed on Modal and a RAG pipeline with a frontier model collaborate to send push notifications with great online deals.</div>')
+            with gr.Row():
+                opportunities_dataframe = gr.Dataframe(
+                    headers=["Deals found so far", "Price", "Estimate", "Discount", "URL"],
+                    wrap=True,
+                    column_widths=[6, 1, 1, 1, 3],
+                    row_count=10,
+                    col_count=5,
+                    max_height=400,
+                )
+            with gr.Row():
+                with gr.Column(scale=1):
+                    logs = gr.HTML()
+                with gr.Column(scale=1):
+                    plot = gr.Plot(value=get_plot(), show_label=False)
+        
+            ui.load(run_with_logging, inputs=[log_data], outputs=[log_data, logs, opportunities_dataframe])
+
+            timer = gr.Timer(value=300, active=True)
+            timer.tick(run_with_logging, inputs=[log_data], outputs=[log_data, logs, opportunities_dataframe])
+
+            opportunities_dataframe.select(do_select)
+        
+        ui.launch(share=False, inbrowser=True)
+
+if __name__=="__main__":
+    App().run()
+    
\ No newline at end of file
diff --git a/week8/community_contributions/Ensemble_with_xgboost/pricer_ephemeral.py b/week8/community_contributions/Ensemble_with_xgboost/pricer_ephemeral.py
new file mode 100644
index 0000000..6fd56ab
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/pricer_ephemeral.py
@@ -0,0 +1,66 @@
+import modal
+from modal import App, Image
+
+# Setup
+
+app = modal.App("pricer")
+image = Image.debian_slim().pip_install("torch", "transformers", "bitsandbytes", "accelerate", "peft")
+secrets = [modal.Secret.from_name("hf-secret")]
+
+# Constants
+
+GPU = "T4"
+BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B"
+PROJECT_NAME = "pricer"
+HF_USER = "ed-donner" # your HF name here! Or use mine if you just want to reproduce my results.
+RUN_NAME = "2024-09-13_13.04.39"
+PROJECT_RUN_NAME = f"{PROJECT_NAME}-{RUN_NAME}"
+REVISION = "e8d637df551603dc86cd7a1598a8f44af4d7ae36"
+FINETUNED_MODEL = f"{HF_USER}/{PROJECT_RUN_NAME}"
+
+
+@app.function(image=image, secrets=secrets, gpu=GPU, timeout=1800)
+def price(description: str) -> float:
+    import os
+    import re
+    import torch
+    from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed
+    from peft import PeftModel
+
+    QUESTION = "How much does this cost to the nearest dollar?"
+    PREFIX = "Price is $"
+
+    prompt = f"{QUESTION}\n{description}\n{PREFIX}"
+    
+    # Quant Config
+    quant_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_use_double_quant=True,
+        bnb_4bit_compute_dtype=torch.bfloat16,
+        bnb_4bit_quant_type="nf4"
+    )
+
+    # Load model and tokenizer
+    
+    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.padding_side = "right"
+    
+    base_model = AutoModelForCausalLM.from_pretrained(
+        BASE_MODEL, 
+        quantization_config=quant_config,
+        device_map="auto"
+    )
+
+    fine_tuned_model = PeftModel.from_pretrained(base_model, FINETUNED_MODEL, revision=REVISION)
+
+    set_seed(42)
+    inputs = tokenizer.encode(prompt, return_tensors="pt").to("cuda")
+    attention_mask = torch.ones(inputs.shape, device="cuda")
+    outputs = fine_tuned_model.generate(inputs, attention_mask=attention_mask, max_new_tokens=5, num_return_sequences=1)
+    result = tokenizer.decode(outputs[0])
+
+    contents = result.split("Price is $")[1]
+    contents = contents.replace(',','')
+    match = re.search(r"[-+]?\d*\.\d+|\d+", contents)
+    return float(match.group()) if match else 0
diff --git a/week8/community_contributions/Ensemble_with_xgboost/pricer_service.py b/week8/community_contributions/Ensemble_with_xgboost/pricer_service.py
new file mode 100644
index 0000000..16d276b
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/pricer_service.py
@@ -0,0 +1,89 @@
+import modal
+from modal import App, Volume, Image
+
+# Setup - define our infrastructure with code!
+
+app = modal.App("pricer-service")
+image = Image.debian_slim().pip_install("huggingface", "torch", "transformers", "bitsandbytes", "accelerate", "peft")
+secrets = [modal.Secret.from_name("hf-secret")]
+
+# Constants
+
+GPU = "T4"
+BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B"
+PROJECT_NAME = "pricer"
+HF_USER = "ed-donner" # your HF name here! Or use mine if you just want to reproduce my results.
+RUN_NAME = "2024-09-13_13.04.39"
+PROJECT_RUN_NAME = f"{PROJECT_NAME}-{RUN_NAME}"
+REVISION = "e8d637df551603dc86cd7a1598a8f44af4d7ae36"
+FINETUNED_MODEL = f"{HF_USER}/{PROJECT_RUN_NAME}"
+MODEL_DIR = "hf-cache/"
+BASE_DIR = MODEL_DIR + BASE_MODEL
+FINETUNED_DIR = MODEL_DIR + FINETUNED_MODEL
+
+QUESTION = "How much does this cost to the nearest dollar?"
+PREFIX = "Price is $"
+
+@app.cls(image=image, secrets=secrets, gpu=GPU, timeout=1800)
+class Pricer:
+    @modal.build()
+    def download_model_to_folder(self):
+        from huggingface_hub import snapshot_download
+        import os
+        os.makedirs(MODEL_DIR, exist_ok=True)
+        snapshot_download(BASE_MODEL, local_dir=BASE_DIR)
+        snapshot_download(FINETUNED_MODEL, revision=REVISION, local_dir=FINETUNED_DIR)
+
+    @modal.enter()
+    def setup(self):
+        import os
+        import torch
+        from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed
+        from peft import PeftModel
+        
+        # Quant Config
+        quant_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_compute_dtype=torch.bfloat16,
+            bnb_4bit_quant_type="nf4"
+        )
+    
+        # Load model and tokenizer
+        
+        self.tokenizer = AutoTokenizer.from_pretrained(BASE_DIR)
+        self.tokenizer.pad_token = self.tokenizer.eos_token
+        self.tokenizer.padding_side = "right"
+        
+        self.base_model = AutoModelForCausalLM.from_pretrained(
+            BASE_DIR, 
+            quantization_config=quant_config,
+            device_map="auto"
+        )
+    
+        self.fine_tuned_model = PeftModel.from_pretrained(self.base_model, FINETUNED_DIR, revision=REVISION)
+
+    @modal.method()
+    def price(self, description: str) -> float:
+        import os
+        import re
+        import torch
+        from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed
+        from peft import PeftModel
+    
+        set_seed(42)
+        prompt = f"{QUESTION}\n\n{description}\n\n{PREFIX}"
+        inputs = self.tokenizer.encode(prompt, return_tensors="pt").to("cuda")
+        attention_mask = torch.ones(inputs.shape, device="cuda")
+        outputs = self.fine_tuned_model.generate(inputs, attention_mask=attention_mask, max_new_tokens=5, num_return_sequences=1)
+        result = self.tokenizer.decode(outputs[0])
+    
+        contents = result.split("Price is $")[1]
+        contents = contents.replace(',','')
+        match = re.search(r"[-+]?\d*\.\d+|\d+", contents)
+        return float(match.group()) if match else 0
+
+    @modal.method()
+    def wake_up(self) -> str:
+        return "ok"
+
diff --git a/week8/community_contributions/Ensemble_with_xgboost/testing.py b/week8/community_contributions/Ensemble_with_xgboost/testing.py
new file mode 100644
index 0000000..cd43924
--- /dev/null
+++ b/week8/community_contributions/Ensemble_with_xgboost/testing.py
@@ -0,0 +1,75 @@
+import math
+import matplotlib.pyplot as plt
+
+GREEN = "\033[92m"
+YELLOW = "\033[93m"
+RED = "\033[91m"
+RESET = "\033[0m"
+COLOR_MAP = {"red":RED, "orange": YELLOW, "green": GREEN}
+
+class Tester:
+
+    def __init__(self, predictor, data, title=None, size=250):
+        self.predictor = predictor
+        self.data = data
+        self.title = title or predictor.__name__.replace("_", " ").title()
+        self.size = size
+        self.guesses = []
+        self.truths = []
+        self.errors = []
+        self.sles = []
+        self.colors = []
+
+    def color_for(self, error, truth):
+        if error<40 or error/truth < 0.2:
+            return "green"
+        elif error<80 or error/truth < 0.4:
+            return "orange"
+        else:
+            return "red"
+    
+    def run_datapoint(self, i):
+        datapoint = self.data[i]
+        guess = self.predictor(datapoint)
+        truth = datapoint.price
+        error = abs(guess - truth)
+        log_error = math.log(truth+1) - math.log(guess+1)
+        sle = log_error ** 2
+        color = self.color_for(error, truth)
+        title = datapoint.title if len(datapoint.title) <= 40 else datapoint.title[:40]+"..."
+        self.guesses.append(guess)
+        self.truths.append(truth)
+        self.errors.append(error)
+        self.sles.append(sle)
+        self.colors.append(color)
+        print(f"{COLOR_MAP[color]}{i+1}: Guess: ${guess:,.2f} Truth: ${truth:,.2f} Error: ${error:,.2f} SLE: {sle:,.2f} Item: {title}{RESET}")
+
+    def chart(self, title):
+        max_error = max(self.errors)
+        plt.figure(figsize=(12, 8))
+        max_val = max(max(self.truths), max(self.guesses))
+        plt.plot([0, max_val], [0, max_val], color='deepskyblue', lw=2, alpha=0.6)
+        plt.scatter(self.truths, self.guesses, s=3, c=self.colors)
+        plt.xlabel('Ground Truth')
+        plt.ylabel('Model Estimate')
+        plt.xlim(0, max_val)
+        plt.ylim(0, max_val)
+        plt.title(title)
+        plt.show()
+
+    def report(self):
+        average_error = sum(self.errors) / self.size
+        rmsle = math.sqrt(sum(self.sles) / self.size)
+        hits = sum(1 for color in self.colors if color=="green")
+        title = f"{self.title} Error=${average_error:,.2f} RMSLE={rmsle:,.2f} Hits={hits/self.size*100:.1f}%"
+        self.chart(title)
+
+    def run(self):
+        self.error = 0
+        for i in range(self.size):
+            self.run_datapoint(i)
+        self.report()
+
+    @classmethod
+    def test(cls, function, data):
+        cls(function, data).run()
\ No newline at end of file