diff --git a/week8/community_contributions/solisoma/price_is_right_fixed.ipynb b/week8/community_contributions/solisoma/price_is_right_fixed.ipynb new file mode 100644 index 0000000..49ccc5b --- /dev/null +++ b/week8/community_contributions/solisoma/price_is_right_fixed.ipynb @@ -0,0 +1,456 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Price is Right - Fixed Version\n", + "\n", + "This notebook fixes the issue where existing deals disappear from the table when the system makes new calls.\n", + "\n", + "**Key Fix**: The table now continuously shows current memory during updates, so existing deals remain visible while new ones are being searched.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Imports\n", + "import sys\n", + "sys.path.append('../..')\n", + "\n", + "import logging\n", + "import queue\n", + "import threading\n", + "import time\n", + "import gradio as gr\n", + "from deal_agent_framework import DealAgentFramework\n", + "from agents.deals import Opportunity, Deal\n", + "from log_utils import reformat\n", + "import plotly.graph_objects as go\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Helper Functions\n", + "\n", + "class QueueHandler(logging.Handler):\n", + " def __init__(self, log_queue):\n", + " super().__init__()\n", + " self.log_queue = log_queue\n", + "\n", + " def emit(self, record):\n", + " self.log_queue.put(self.format(record))\n", + "\n", + "\n", + "def html_for(log_data):\n", + " \"\"\"Convert log data to HTML format for display\"\"\"\n", + " output = '
'.join(log_data[-18:])\n", + " return f\"\"\"\n", + "
\n", + " {output}\n", + "
\n", + " \"\"\"\n", + "\n", + "\n", + "def setup_logging(log_queue):\n", + " \"\"\"Setup logging to capture messages in a queue\"\"\"\n", + " handler = QueueHandler(log_queue)\n", + " formatter = logging.Formatter(\n", + " \"[%(asctime)s] %(message)s\",\n", + " datefmt=\"%Y-%m-%d %H:%M:%S %z\",\n", + " )\n", + " handler.setFormatter(formatter)\n", + " logger = logging.getLogger()\n", + " logger.addHandler(handler)\n", + " logger.setLevel(logging.INFO)\n", + "\n", + "\n", + "def get_plot():\n", + " \"\"\"Generate 3D visualization of vector database - handles empty database gracefully\"\"\"\n", + " try:\n", + " documents, vectors, colors = DealAgentFramework.get_plot_data(max_datapoints=1000)\n", + "\n", + " print(vectors, flush=True)\n", + " \n", + " # Check if we have any data\n", + " if len(vectors) == 0:\n", + " # Return placeholder plot if database is empty\n", + " fig = go.Figure()\n", + " fig.update_layout(\n", + " title='Vector Database Empty',\n", + " height=400,\n", + " annotations=[dict(\n", + " text=\"The vector database is empty.
Run the data loading notebook (day2.0) to populate it.\",\n", + " x=0.5,\n", + " y=0.5,\n", + " xref=\"paper\",\n", + " yref=\"paper\",\n", + " showarrow=False,\n", + " font=dict(size=14)\n", + " )]\n", + " )\n", + " return fig\n", + " \n", + " # Normal case: create 3D scatter plot\n", + " fig = go.Figure(data=[go.Scatter3d(\n", + " x=vectors[:, 0],\n", + " y=vectors[:, 1],\n", + " z=vectors[:, 2],\n", + " mode='markers',\n", + " marker=dict(size=2, color=colors, opacity=0.7),\n", + " )])\n", + " \n", + " fig.update_layout(\n", + " scene=dict(xaxis_title='x', \n", + " yaxis_title='y', \n", + " zaxis_title='z',\n", + " aspectmode='manual',\n", + " aspectratio=dict(x=2.2, y=2.2, z=1),\n", + " camera=dict(\n", + " eye=dict(x=1.6, y=1.6, z=0.8)\n", + " )),\n", + " height=400,\n", + " margin=dict(r=5, b=1, l=5, t=2)\n", + " )\n", + " return fig\n", + " except Exception as e:\n", + " # Handle any errors gracefully\n", + " fig = go.Figure()\n", + " fig.update_layout(\n", + " title='Error Loading Vector Database',\n", + " height=400,\n", + " annotations=[dict(\n", + " text=f\"Error: {str(e)}

Make sure the vector database is set up correctly.
Run day2.0 notebook to populate it.\",\n", + " x=0.5,\n", + " y=0.5,\n", + " xref=\"paper\",\n", + " yref=\"paper\",\n", + " showarrow=False,\n", + " font=dict(size=12)\n", + " )]\n", + " )\n", + " return fig\n", + "\n", + "\n", + "def create_opportunity_from_dict(data: dict) -> Opportunity:\n", + " \"\"\"Helper function to create Opportunity from dictionary - uses Deal and Opportunity classes\"\"\"\n", + " deal = Deal(**data['deal']) if isinstance(data['deal'], dict) else data['deal']\n", + " return Opportunity(deal=deal, estimate=data['estimate'], discount=data['discount'])\n", + "\n", + "\n", + "def validate_opportunities(opportunities) -> list:\n", + " \"\"\"Validate and ensure all items are Opportunity instances - uses Opportunity class\"\"\"\n", + " validated = []\n", + " for opp in opportunities:\n", + " if not isinstance(opp, Opportunity):\n", + " if isinstance(opp, dict):\n", + " opp = create_opportunity_from_dict(opp)\n", + " else:\n", + " continue\n", + " validated.append(opp)\n", + " return validated\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Main App Class\n", + "\n", + "class App:\n", + "\n", + " def __init__(self): \n", + " self.agent_framework = None\n", + "\n", + " def get_agent_framework(self):\n", + " \"\"\"Get or initialize the agent framework\"\"\"\n", + " if not self.agent_framework:\n", + " self.agent_framework = DealAgentFramework()\n", + " self.agent_framework.init_agents_as_needed()\n", + " return self.agent_framework\n", + "\n", + " def table_for(self, opps):\n", + " \"\"\"Convert opportunities to table format - uses Opportunity and Deal classes\"\"\"\n", + " # Validate opportunities are Opportunity instances\n", + " validated_opps = validate_opportunities(opps)\n", + " return [[opp.deal.product_description, f\"${opp.deal.price:.2f}\", f\"${opp.estimate:.2f}\", f\"${opp.discount:.2f}\", opp.deal.url] \n", + " for opp in validated_opps \n", + " if isinstance(opp, Opportunity)]\n", + "\n", + " def update_output(self, log_data, log_queue, result_queue):\n", + " \"\"\"Keep showing current memory during updates - fixes disappearing table issue\"\"\"\n", + " framework = self.get_agent_framework()\n", + " current_table = self.table_for(framework.memory)\n", + " \n", + " while True:\n", + " try:\n", + " message = log_queue.get_nowait()\n", + " log_data.append(reformat(message))\n", + " # Always refresh table from current memory during updates\n", + " current_table = self.table_for(framework.memory)\n", + " yield log_data, html_for(log_data), current_table\n", + " except queue.Empty:\n", + " try:\n", + " # When result is ready, update with final result\n", + " final_result = result_queue.get_nowait()\n", + " yield log_data, html_for(log_data), final_result\n", + " return\n", + " except queue.Empty:\n", + " # Continue showing current memory while waiting\n", + " current_table = self.table_for(framework.memory)\n", + " yield log_data, html_for(log_data), current_table\n", + " time.sleep(0.1)\n", + "\n", + " def do_run(self):\n", + " \"\"\"Run framework and return updated table\"\"\"\n", + " framework = self.get_agent_framework()\n", + " new_opportunities = framework.run()\n", + " return self.table_for(new_opportunities)\n", + "\n", + " def run_with_logging(self, initial_log_data):\n", + " \"\"\"Run agent framework with logging in a separate thread\"\"\"\n", + " log_queue = queue.Queue()\n", + " result_queue = queue.Queue()\n", + " setup_logging(log_queue)\n", + " \n", + " def worker():\n", + " result = self.do_run()\n", + " result_queue.put(result)\n", + " \n", + " thread = threading.Thread(target=worker)\n", + " thread.start()\n", + " \n", + " for log_data, output, final_result in self.update_output(initial_log_data, log_queue, result_queue):\n", + " yield log_data, output, final_result\n", + "\n", + " def do_select(self, selected_index: gr.SelectData):\n", + " \"\"\"Handle deal selection - send alert\"\"\"\n", + " framework = self.get_agent_framework()\n", + " opportunities = framework.memory\n", + " row = selected_index.index[0]\n", + " if row < len(opportunities):\n", + " opportunity = opportunities[row]\n", + " framework.planner.messenger.alert(opportunity)\n", + " return f\"Alert sent for: {opportunity.deal.product_description[:50]}...\"\n", + " return \"No opportunity found at that index\"\n", + "\n", + " def load_initial(self):\n", + " \"\"\"Load initial state with existing deals - uses Opportunity and Deal classes\"\"\"\n", + " framework = self.get_agent_framework()\n", + " # Ensure memory contains Opportunity instances\n", + " opportunities = validate_opportunities(framework.memory)\n", + " initial_table = self.table_for(opportunities)\n", + " return [], \"\", initial_table\n", + "\n", + " def run(self):\n", + " \"\"\"Launch the Gradio interface\"\"\"\n", + " with gr.Blocks(title=\"The Price is Right\", fill_width=True) as ui:\n", + " \n", + " log_data = gr.State([])\n", + " \n", + " with gr.Row():\n", + " gr.Markdown('
The Price is Right - Autonomous Agent Framework that hunts for deals
')\n", + " with gr.Row():\n", + " gr.Markdown('
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.
')\n", + " with gr.Row():\n", + " opportunities_dataframe = gr.Dataframe(\n", + " headers=[\"Deals found so far\", \"Price\", \"Estimate\", \"Discount\", \"URL\"],\n", + " wrap=True,\n", + " column_widths=[6, 1, 1, 1, 3],\n", + " row_count=10,\n", + " col_count=5,\n", + " max_height=400,\n", + " )\n", + " with gr.Row():\n", + " with gr.Column(scale=1):\n", + " logs = gr.HTML()\n", + " with gr.Column(scale=1):\n", + " plot = gr.Plot(value=get_plot(), show_label=False)\n", + " \n", + " # Initial load - show existing deals\n", + " ui.load(self.load_initial, inputs=[], outputs=[log_data, logs, opportunities_dataframe])\n", + "\n", + " # Timer that runs every 5 minutes (300 seconds)\n", + " timer = gr.Timer(value=300, active=True)\n", + " timer.tick(self.run_with_logging, inputs=[log_data], outputs=[log_data, logs, opportunities_dataframe])\n", + "\n", + " # Selection handler\n", + " selection_feedback = gr.Textbox(visible=False)\n", + " opportunities_dataframe.select(self.do_select, inputs=[], outputs=[selection_feedback])\n", + " \n", + " ui.launch(share=False, inbrowser=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2025-10-30 12:15:06 +0100] [Agents] [INFO] HTTP Request: GET https://api.gradio.app/pkg-version \"HTTP/1.1 200 OK\"\n", + "[2025-10-30 12:15:06 +0100] [Agents] [INFO] HTTP Request: GET https://api.gradio.app/pkg-version \"HTTP/1.1 200 OK\"\n", + "* Running on local URL: http://127.0.0.1:7862\n", + "[2025-10-30 12:15:07 +0100] [Agents] [INFO] HTTP Request: GET http://127.0.0.1:7862/gradio_api/startup-events \"HTTP/1.1 200 OK\"\n", + "[2025-10-30 12:15:07 +0100] [Agents] [INFO] HTTP Request: GET http://127.0.0.1:7862/gradio_api/startup-events \"HTTP/1.1 200 OK\"\n", + "[2025-10-30 12:15:07 +0100] [Agents] [INFO] HTTP Request: HEAD http://127.0.0.1:7862/ \"HTTP/1.1 200 OK\"\n", + "[2025-10-30 12:15:07 +0100] [Agents] [INFO] HTTP Request: HEAD http://127.0.0.1:7862/ \"HTTP/1.1 200 OK\"\n", + "* To create a public link, set `share=True` in `launch()`.\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2025-10-30 12:15:10 +0100] [Agents] [INFO] \u001b[44m\u001b[37m[Agent Framework] Initializing Agent Framework\u001b[0m\n", + "[2025-10-30 12:15:10 +0100] [Agents] [INFO] \u001b[44m\u001b[37m[Agent Framework] Initializing Agent Framework\u001b[0m\n", + "[2025-10-30 12:15:10 +0100] [Agents] [INFO] \u001b[44m\u001b[37m[Agent Framework] Initializing Agent Framework\u001b[0m\n", + "[2025-10-30 12:15:10 +0100] [Agents] [INFO] \u001b[40m\u001b[32m[Planning Agent] Planning Agent is initializing\u001b[0m\n", + "[2025-10-30 12:15:10 +0100] [Agents] [INFO] \u001b[40m\u001b[32m[Planning Agent] Planning Agent is initializing\u001b[0m\n", + "[2025-10-30 12:15:10 +0100] [Agents] [INFO] \u001b[40m\u001b[32m[Planning Agent] Planning Agent is initializing\u001b[0m\n", + "[2025-10-30 12:15:10 +0100] [Agents] [INFO] \u001b[40m\u001b[36m[Scanner Agent] Scanner Agent is initializing\u001b[0m\n", + "[2025-10-30 12:15:10 +0100] [Agents] [INFO] \u001b[40m\u001b[36m[Scanner Agent] Scanner Agent is initializing\u001b[0m\n", + "[2025-10-30 12:15:10 +0100] [Agents] [INFO] \u001b[40m\u001b[36m[Scanner Agent] Scanner Agent is initializing\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] \u001b[40m\u001b[36m[Scanner Agent] Scanner Agent is ready\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] \u001b[40m\u001b[36m[Scanner Agent] Scanner Agent is ready\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] \u001b[40m\u001b[36m[Scanner Agent] Scanner Agent is ready\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] \u001b[40m\u001b[33m[Ensemble Agent] Initializing Ensemble Agent\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] \u001b[40m\u001b[33m[Ensemble Agent] Initializing Ensemble Agent\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] \u001b[40m\u001b[33m[Ensemble Agent] Initializing Ensemble Agent\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] \u001b[40m\u001b[31m[Specialist Agent] Specialist Agent is initializing - connecting to modal\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] \u001b[40m\u001b[31m[Specialist Agent] Specialist Agent is initializing - connecting to modal\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] \u001b[40m\u001b[31m[Specialist Agent] Specialist Agent is initializing - connecting to modal\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] \u001b[40m\u001b[31m[Specialist Agent] Specialist Agent is ready\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] \u001b[40m\u001b[31m[Specialist Agent] Specialist Agent is ready\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] \u001b[40m\u001b[31m[Specialist Agent] Specialist Agent is ready\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] \u001b[40m\u001b[34m[Frontier Agent] Initializing Frontier Agent\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] \u001b[40m\u001b[34m[Frontier Agent] Initializing Frontier Agent\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] \u001b[40m\u001b[34m[Frontier Agent] Initializing Frontier Agent\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] \u001b[40m\u001b[34m[Frontier Agent] Frontier Agent is set up with DeepSeek\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] \u001b[40m\u001b[34m[Frontier Agent] Frontier Agent is set up with DeepSeek\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] \u001b[40m\u001b[34m[Frontier Agent] Frontier Agent is set up with DeepSeek\u001b[0m\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] Use pytorch device_name: cpu\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] Use pytorch device_name: cpu\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] Use pytorch device_name: cpu\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2\n", + "[2025-10-30 12:15:11 +0100] [Agents] [INFO] Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2\n", + "[2025-10-30 12:15:17 +0100] [Agents] [INFO] \u001b[40m\u001b[34m[Frontier Agent] Frontier Agent is ready\u001b[0m\n", + "[2025-10-30 12:15:17 +0100] [Agents] [INFO] \u001b[40m\u001b[34m[Frontier Agent] Frontier Agent is ready\u001b[0m\n", + "[2025-10-30 12:15:17 +0100] [Agents] [INFO] \u001b[40m\u001b[34m[Frontier Agent] Frontier Agent is ready\u001b[0m\n", + "[2025-10-30 12:15:17 +0100] [Agents] [INFO] \u001b[40m\u001b[35m[Random Forest Agent] Random Forest Agent is initializing\u001b[0m\n", + "[2025-10-30 12:15:17 +0100] [Agents] [INFO] \u001b[40m\u001b[35m[Random Forest Agent] Random Forest Agent is initializing\u001b[0m\n", + "[2025-10-30 12:15:17 +0100] [Agents] [INFO] \u001b[40m\u001b[35m[Random Forest Agent] Random Forest Agent is initializing\u001b[0m\n", + "[2025-10-30 12:15:17 +0100] [Agents] [INFO] Use pytorch device_name: cpu\n", + "[2025-10-30 12:15:17 +0100] [Agents] [INFO] Use pytorch device_name: cpu\n", + "[2025-10-30 12:15:17 +0100] [Agents] [INFO] Use pytorch device_name: cpu\n", + "[2025-10-30 12:15:17 +0100] [Agents] [INFO] Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2\n", + "[2025-10-30 12:15:17 +0100] [Agents] [INFO] Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2\n", + "[2025-10-30 12:15:17 +0100] [Agents] [INFO] Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"c:\\Users\\hp\\projects\\gen-ai\\llm_engineering\\.venv\\Lib\\site-packages\\gradio\\queueing.py\", line 745, in process_events\n", + " response = await route_utils.call_process_api(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\hp\\projects\\gen-ai\\llm_engineering\\.venv\\Lib\\site-packages\\gradio\\route_utils.py\", line 354, in call_process_api\n", + " output = await app.get_blocks().process_api(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\hp\\projects\\gen-ai\\llm_engineering\\.venv\\Lib\\site-packages\\gradio\\blocks.py\", line 2116, in process_api\n", + " result = await self.call_function(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\hp\\projects\\gen-ai\\llm_engineering\\.venv\\Lib\\site-packages\\gradio\\blocks.py\", line 1623, in call_function\n", + " prediction = await anyio.to_thread.run_sync( # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\hp\\projects\\gen-ai\\llm_engineering\\.venv\\Lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n", + " return await get_async_backend().run_sync_in_worker_thread(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\hp\\projects\\gen-ai\\llm_engineering\\.venv\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2485, in run_sync_in_worker_thread\n", + " return await future\n", + " ^^^^^^^^^^^^\n", + " File \"c:\\Users\\hp\\projects\\gen-ai\\llm_engineering\\.venv\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 976, in run\n", + " result = context.run(func, *args)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\hp\\projects\\gen-ai\\llm_engineering\\.venv\\Lib\\site-packages\\gradio\\utils.py\", line 915, in wrapper\n", + " response = f(*args, **kwargs)\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_560\\1866679463.py\", line 82, in load_initial\n", + " framework = self.get_agent_framework()\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_560\\1866679463.py\", line 12, in get_agent_framework\n", + " self.agent_framework.init_agents_as_needed()\n", + " File \"c:\\Users\\hp\\projects\\gen-ai\\llm_engineering\\week8\\community_contributions\\solisoma\\../..\\deal_agent_framework.py\", line 54, in init_agents_as_needed\n", + " self.log(\"Agent Framework is ready\")\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\hp\\projects\\gen-ai\\llm_engineering\\week8\\community_contributions\\solisoma\\../..\\agents\\planning_agent.py\", line 21, in __init__\n", + " self.ensemble = EnsembleAgent(collection)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\hp\\projects\\gen-ai\\llm_engineering\\week8\\community_contributions\\solisoma\\../..\\agents\\ensemble_agent.py\", line 23, in __init__\n", + " self.random_forest = RandomForestAgent()\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\hp\\projects\\gen-ai\\llm_engineering\\week8\\community_contributions\\solisoma\\../..\\agents\\random_forest_agent.py\", line 24, in __init__\n", + " self.model = joblib.load('random_forest_model.pkl')\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\hp\\projects\\gen-ai\\llm_engineering\\.venv\\Lib\\site-packages\\joblib\\numpy_pickle.py\", line 735, in load\n", + " with open(filename, \"rb\") as f:\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + "FileNotFoundError: [Errno 2] No such file or directory: 'random_forest_model.pkl'\n" + ] + } + ], + "source": [ + "# Run the application\n", + "app = App()\n", + "app.run()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}