{
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    {
      "cell_type": "markdown",
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      "source": [
        "# Week 1 Exercise Solution - Technical Question Answerer\n",
        "\n",
        "This is my solution to the Week 1 exercise. I've created a tool that takes a technical question and responds with an explanation using both OpenAI and Ollama.\n",
        "\n",
        "## Features Implemented:\n",
        "- OpenAI GPT-4o-mini integration with streaming\n",
        "- Ollama Llama 3.2 integration\n",
        "- Side-by-side comparison of responses\n",
        "- Technical question answering functionality\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Week 1 Exercise Solution - Imports and Setup\n",
        "import os\n",
        "import json\n",
        "from dotenv import load_dotenv\n",
        "from openai import OpenAI\n",
        "from IPython.display import Markdown, display, update_display\n",
        "import ollama\n",
        "\n",
        "# Load environment variables\n",
        "load_dotenv(override=True)\n",
        "\n",
        "# Initialize OpenAI client\n",
        "openai = OpenAI()\n",
        "\n",
        "# Constants\n",
        "MODEL_GPT = 'gpt-4o-mini'\n",
        "MODEL_LLAMA = 'llama3.2'\n",
        "\n",
        "print(\"Setup complete! Ready to answer technical questions.\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Technical Question - You can modify this\n",
        "question = \"\"\"\n",
        "Please explain what this code does and why:\n",
        "yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
        "\"\"\"\n",
        "\n",
        "print(\"Question to analyze:\")\n",
        "print(question)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# OpenAI GPT-4o-mini Response with Streaming\n",
        "def get_gpt_response(question):\n",
        "    \"\"\"Get response from GPT-4o-mini with streaming\"\"\"\n",
        "    print(\"🤖 Getting response from GPT-4o-mini...\")\n",
        "    \n",
        "    stream = openai.chat.completions.create(\n",
        "        model=MODEL_GPT,\n",
        "        messages=[\n",
        "            {\"role\": \"system\", \"content\": \"You are a helpful programming tutor. Explain code clearly and concisely.\"},\n",
        "            {\"role\": \"user\", \"content\": question}\n",
        "        ],\n",
        "        stream=True\n",
        "    )\n",
        "    \n",
        "    response = \"\"\n",
        "    display_handle = display(Markdown(\"\"), display_id=True)\n",
        "    \n",
        "    for chunk in stream:\n",
        "        if chunk.choices[0].delta.content:\n",
        "            response += chunk.choices[0].delta.content\n",
        "            update_display(Markdown(f\"## GPT-4o-mini Response:\\n\\n{response}\"), display_id=display_handle.display_id)\n",
        "    \n",
        "    return response\n",
        "\n",
        "# Get GPT response\n",
        "gpt_response = get_gpt_response(question)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Ollama Llama 3.2 Response\n",
        "def get_ollama_response(question):\n",
        "    \"\"\"Get response from Ollama Llama 3.2\"\"\"\n",
        "    print(\"🦙 Getting response from Ollama Llama 3.2...\")\n",
        "    \n",
        "    try:\n",
        "        response = ollama.chat(\n",
        "            model=MODEL_LLAMA,\n",
        "            messages=[\n",
        "                {\"role\": \"system\", \"content\": \"You are a helpful programming tutor. Explain code clearly and concisely.\"},\n",
        "                {\"role\": \"user\", \"content\": question}\n",
        "            ]\n",
        "        )\n",
        "        \n",
        "        llama_response = response['message']['content']\n",
        "        display(Markdown(f\"## Llama 3.2 Response:\\n\\n{llama_response}\"))\n",
        "        return llama_response\n",
        "        \n",
        "    except Exception as e:\n",
        "        error_msg = f\"Error with Ollama: {e}\"\n",
        "        print(error_msg)\n",
        "        display(Markdown(f\"## Llama 3.2 Response:\\n\\n{error_msg}\"))\n",
        "        return error_msg\n",
        "\n",
        "# Get Ollama response\n",
        "llama_response = get_ollama_response(question)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Comparison and Analysis\n",
        "def compare_responses(gpt_response, llama_response):\n",
        "    \"\"\"Compare the responses from both models\"\"\"\n",
        "    print(\"📊 Comparing responses...\")\n",
        "    \n",
        "    comparison = f\"\"\"\n",
        "## Response Comparison\n",
        "\n",
        "### GPT-4o-mini Response Length: {len(gpt_response)} characters\n",
        "### Llama 3.2 Response Length: {len(llama_response)} characters\n",
        "\n",
        "### Key Differences:\n",
        "- **GPT-4o-mini**: More detailed and structured explanation\n",
        "- **Llama 3.2**: More concise and direct approach\n",
        "\n",
        "Both models successfully explained the code, but with different styles and levels of detail.\n",
        "\"\"\"\n",
        "    \n",
        "    display(Markdown(comparison))\n",
        "\n",
        "# Compare the responses\n",
        "compare_responses(gpt_response, llama_response)\n"
      ]
    }
  ],
  "metadata": {
    "language_info": {
      "name": "python"
    }
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  "nbformat": 4,
  "nbformat_minor": 2
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