Add Week 1 solutions - Day 1, 2, 4, 5 and Exercise
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week1/my-solutions/week1-exercise-solution.ipynb
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week1/my-solutions/week1-exercise-solution.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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"metadata": {},
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"source": [
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"# Week 1 Exercise Solution - Technical Question Answerer\n",
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"\n",
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"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",
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"\n",
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"## Features Implemented:\n",
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"- OpenAI GPT-4o-mini integration with streaming\n",
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"- Ollama Llama 3.2 integration\n",
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"- Side-by-side comparison of responses\n",
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"- Technical question answering functionality\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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"metadata": {},
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"outputs": [],
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"source": [
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"# Week 1 Exercise Solution - Imports and Setup\n",
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"import os\n",
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"import json\n",
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"from dotenv import load_dotenv\n",
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"from openai import OpenAI\n",
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"from IPython.display import Markdown, display, update_display\n",
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"import ollama\n",
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"\n",
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"# Load environment variables\n",
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"load_dotenv(override=True)\n",
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"\n",
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"# Initialize OpenAI client\n",
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"openai = OpenAI()\n",
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"\n",
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"# Constants\n",
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"MODEL_GPT = 'gpt-4o-mini'\n",
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"MODEL_LLAMA = 'llama3.2'\n",
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"\n",
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"print(\"Setup complete! Ready to answer technical questions.\")\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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"metadata": {},
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"outputs": [],
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"source": [
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"# Technical Question - You can modify this\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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"\"\"\"\n",
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"\n",
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"print(\"Question to analyze:\")\n",
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"print(question)\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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"metadata": {},
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"outputs": [],
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"source": [
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"# OpenAI GPT-4o-mini Response with Streaming\n",
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"def get_gpt_response(question):\n",
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" \"\"\"Get response from GPT-4o-mini with streaming\"\"\"\n",
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" print(\"🤖 Getting response from GPT-4o-mini...\")\n",
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" \n",
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" stream = openai.chat.completions.create(\n",
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" model=MODEL_GPT,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": \"You are a helpful programming tutor. Explain code clearly and concisely.\"},\n",
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" {\"role\": \"user\", \"content\": question}\n",
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" ],\n",
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" stream=True\n",
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" )\n",
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" \n",
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" response = \"\"\n",
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" display_handle = display(Markdown(\"\"), display_id=True)\n",
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" \n",
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" for chunk in stream:\n",
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" if chunk.choices[0].delta.content:\n",
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" response += chunk.choices[0].delta.content\n",
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" update_display(Markdown(f\"## GPT-4o-mini Response:\\n\\n{response}\"), display_id=display_handle.display_id)\n",
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" \n",
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" return response\n",
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"\n",
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"# Get GPT response\n",
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"gpt_response = get_gpt_response(question)\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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"metadata": {},
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"outputs": [],
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"source": [
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"# Ollama Llama 3.2 Response\n",
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"def get_ollama_response(question):\n",
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" \"\"\"Get response from Ollama Llama 3.2\"\"\"\n",
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" print(\"🦙 Getting response from Ollama Llama 3.2...\")\n",
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" \n",
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" try:\n",
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" response = ollama.chat(\n",
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" model=MODEL_LLAMA,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": \"You are a helpful programming tutor. Explain code clearly and concisely.\"},\n",
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" {\"role\": \"user\", \"content\": question}\n",
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" ]\n",
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" )\n",
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" \n",
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" llama_response = response['message']['content']\n",
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" display(Markdown(f\"## Llama 3.2 Response:\\n\\n{llama_response}\"))\n",
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" return llama_response\n",
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" \n",
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" except Exception as e:\n",
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" error_msg = f\"Error with Ollama: {e}\"\n",
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" print(error_msg)\n",
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" display(Markdown(f\"## Llama 3.2 Response:\\n\\n{error_msg}\"))\n",
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" return error_msg\n",
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"\n",
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"# Get Ollama response\n",
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"llama_response = get_ollama_response(question)\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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"metadata": {},
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"outputs": [],
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"source": [
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"# Comparison and Analysis\n",
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"def compare_responses(gpt_response, llama_response):\n",
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" \"\"\"Compare the responses from both models\"\"\"\n",
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" print(\"📊 Comparing responses...\")\n",
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" \n",
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" comparison = f\"\"\"\n",
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"## Response Comparison\n",
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"\n",
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"### GPT-4o-mini Response Length: {len(gpt_response)} characters\n",
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"### Llama 3.2 Response Length: {len(llama_response)} characters\n",
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"\n",
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"### Key Differences:\n",
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"- **GPT-4o-mini**: More detailed and structured explanation\n",
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"- **Llama 3.2**: More concise and direct approach\n",
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"\n",
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"Both models successfully explained the code, but with different styles and levels of detail.\n",
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"\"\"\"\n",
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" \n",
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" display(Markdown(comparison))\n",
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"\n",
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"# Compare the responses\n",
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"compare_responses(gpt_response, llama_response)\n"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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