Add Emmy's week one solution
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Mogbeyi
226
week1/community-contributions/emmy/emmy_week1_EXERCISE.ipynb
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226
week1/community-contributions/emmy/emmy_week1_EXERCISE.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5",
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"metadata": {},
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"source": [
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"# End of week 1 exercise\n",
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"\n",
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"To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question, \n",
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"and responds with an explanation. This is a tool that you will be able to use yourself during the course!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c1070317-3ed9-4659-abe3-828943230e03",
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"metadata": {},
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"outputs": [],
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"source": [
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"# imports\n",
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"import os\n",
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"from dotenv import load_dotenv\n",
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"from openai import OpenAI\n",
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"import ollama\n",
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"import ipywidgets as widgets\n",
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"from IPython.display import display, Markdown"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# constants\n",
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"\n",
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"MODEL_GEMINI = \"gemini-2.5-flash\"\n",
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"MODEL_LLAMA = \"llama3.1:8b\"\n",
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"\n",
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"CHOICE_GEMINI = \"gemini\"\n",
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"CHOICE_OLLAMA = \"ollama\"\n",
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"\n",
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"SYSTEM_PROMPT = (\n",
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" \"You are a technical adviser. The student is learning LLM engineering \"\n",
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" \"and you will be asked to explain lines of code with an example, \"\n",
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" \"mostly in Python.\"\n",
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" \"You can answer other questions as well.\"\n",
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")\n",
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"\n",
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"GEMINI_BASE_URL = \"https://generativelanguage.googleapis.com/v1beta/openai/\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# set up environment\n",
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"load_dotenv(override=True)\n",
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"google_api_key = os.getenv(\"GOOGLE_API_KEY\")\n",
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"\n",
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"if not google_api_key:\n",
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" print(\"Warning: GOOGLE_API_KEY not found. Gemini calls will fail.\")\n",
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" print(\"Please create a .env file with GOOGLE_API_KEY=your_key\")\n",
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"\n",
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"gemini_client = OpenAI(\n",
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" base_url=GEMINI_BASE_URL,\n",
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" api_key=google_api_key,\n",
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")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
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"metadata": {},
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"outputs": [],
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"source": [
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"# here is the question; type over this to ask something new\n",
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"\n",
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"question = \"\"\"\n",
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"Please explain what this code does and why:\n",
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"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"def make_messages(user_question: str):\n",
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" return [\n",
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" {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
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" {\"role\": \"user\", \"content\": user_question},\n",
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" ]\n",
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"\n",
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"\n",
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"def stream_gemini(messages):\n",
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" \"\"\"Stream response chunks from Gemini.\"\"\"\n",
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" stream = gemini_client.chat.completions.create(\n",
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" model=MODEL_GEMINI,\n",
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" messages=messages,\n",
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" stream=True,\n",
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" )\n",
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"\n",
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" full = []\n",
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" for chunk in stream:\n",
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" piece = chunk.choices[0].delta.content or \"\"\n",
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" full.append(piece)\n",
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" return \"\".join(full)\n",
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"\n",
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"\n",
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"def stream_ollama(messages):\n",
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" \"\"\"Stream response chunks from local Ollama.\"\"\"\n",
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" stream = ollama.chat(\n",
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" model=MODEL_LLAMA,\n",
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" messages=messages,\n",
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" stream=True,\n",
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" )\n",
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"\n",
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" full = []\n",
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" for chunk in stream:\n",
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" piece = chunk[\"message\"][\"content\"]\n",
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" full.append(piece)\n",
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" return \"\".join(full)\n",
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"\n",
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"\n",
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"def get_explanation(question: str, model_choice: str):\n",
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" \"\"\"Gets a technical explanation from the chosen model and streams the response.\"\"\"\n",
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" messages = make_messages(question)\n",
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" try:\n",
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" if model_choice == CHOICE_GEMINI:\n",
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" return stream_gemini(messages)\n",
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" elif model_choice == CHOICE_OLLAMA:\n",
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" return stream_ollama(messages)\n",
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" else:\n",
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" print(\"Unknown model choice.\")\n",
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" return \"\"\n",
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" except Exception as e:\n",
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" print(f\"\\nAn error occurred: {e}\")\n",
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" return \"\"\n",
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"\n",
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"print(\"💡 Your personal technical tutor is ready.\\n\")\n",
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"\n",
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"# Dropdown for model selection\n",
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"model_dropdown = widgets.Dropdown(\n",
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" options=[\n",
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" (\"Gemini (gemini-2.5-flash)\", CHOICE_GEMINI),\n",
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" (\"Ollama (llama3.1:8b)\", CHOICE_OLLAMA),\n",
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" ],\n",
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" value=CHOICE_GEMINI,\n",
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" description=\"Model:\",\n",
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" style={\"description_width\": \"initial\"},\n",
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")\n",
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"\n",
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"# Text input for question\n",
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"question_box = widgets.Textarea(\n",
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" placeholder=\"Type your technical question here...\",\n",
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" description=\"Question:\",\n",
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" layout=widgets.Layout(width=\"100%\", height=\"100px\"),\n",
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" style={\"description_width\": \"initial\"},\n",
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")\n",
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"\n",
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"submit_button = widgets.Button(description=\"Ask\", button_style=\"success\", icon=\"paper-plane\")\n",
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"\n",
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"output_area = widgets.Output()\n",
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"loader_label = widgets.Label(value=\"\")\n",
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"\n",
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"def on_submit(_):\n",
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" output_area.clear_output()\n",
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" question = question_box.value.strip()\n",
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" if not question:\n",
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" with output_area:\n",
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" print(\"Please enter a question.\")\n",
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" return\n",
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"\n",
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" loader_label.value = \"⏳ Thinking...\"\n",
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" submit_button.disabled = True\n",
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"\n",
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" answer = get_explanation(question, model_dropdown.value)\n",
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"\n",
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" loader_label.value = \"\"\n",
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" submit_button.disabled = False\n",
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"\n",
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" with output_area:\n",
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" print(f\"🤖 Model: {model_dropdown.label}\")\n",
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" print(f\"📜 Question: {question}\\n\")\n",
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" display(Markdown(answer))\n",
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" print(\"\\n--- End of response ---\")\n",
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"\n",
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"submit_button.on_click(on_submit)\n",
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"\n",
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"# Display everything\n",
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"display(model_dropdown, question_box, submit_button, loader_label, output_area)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "llm-engineering (3.12.10)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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