Add Week 1 notebook to community contributions
This commit is contained in:
Cosmus Mutuku
253
week1/community-contributions/Cosmus/Cosmus_week1_EXERCISE.ipynb
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253
week1/community-contributions/Cosmus/Cosmus_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": 1,
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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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"import json\n",
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"import requests\n",
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"from IPython.display import Markdown, display, update_display\n",
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"from dotenv import load_dotenv\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": 2,
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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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"\n",
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"# --- Load environment ---\n",
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"load_dotenv()\n",
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"\n",
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"MODEL_LLAMA = os.getenv(\"LOCAL_MODEL_NAME\", \"llama3.2\")\n",
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"OLLAMA_BASE = os.getenv(\"OLLAMA_BASE_URL\", \"http://localhost:11434/v1\")"
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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": 3,
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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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"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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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Getting explanation from llama3.2 using Ollama...\n"
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]
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},
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{
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"data": {
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"text/markdown": [
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"This piece of code is written in Python. It uses the `yield from` statement, which is a feature introduced in Python 3.3.\n",
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"\n",
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"Here's what it does:\n",
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"\n",
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"- It iterates over each `book` in the list `books`.\n",
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"\n",
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"- For each `book`, it tries to get the value associated with the key `\"author\"`. The `.get(\"author\")` method returns the value for the key `\"author\"` if it exists, and provides a default value (`None` by default) if the key does not exist.\n",
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"\n",
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"- It then yields this author value back through the generator that returned `this yield from`.\n",
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"\n",
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"In other words, when you use `yield from`, Python takes all values yielded from one inner iteration point and turns them into values yielded point-by-point on a containing iteration. The values are \"yielded\" in an order dictated to it by the innermost sequence.\n",
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"\n",
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"Here is how that would look like:\n",
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"\n",
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"```\n",
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" for book, author in books:\n",
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" for author_from_book in yield_from(book.get(\"author\") if book.get(\"author\") else []):\n",
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" # Code here\n",
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" pass\n",
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" pass\n",
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"``` \n",
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"\n",
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"Or a simplified version with `if-express` and `map` like so\n",
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"\n",
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"```python\n",
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"import expression\n",
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"\n",
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"books = [\n",
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" {\"id\": 1, \"title\": 'Blurred Horizons'},\n",
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" {\"id\": 2, \"author\": 'Judy Blume'}, \n",
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" {\"id\": 3},\n",
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"]\n",
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"authors= ['blum', *exp(expression,\" books get\")()]\n",
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"\n",
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"for author in authors:\n",
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" pass\n",
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"```\n",
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"But again this is using something that would probably be written in a real application like so"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Final explanation:\n",
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"\n"
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]
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},
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{
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"data": {
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"text/markdown": [
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"### Llama 3.2 Explanation\n",
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"\n",
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"This piece of code is written in Python. It uses the `yield from` statement, which is a feature introduced in Python 3.3.\n",
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"\n",
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"Here's what it does:\n",
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"\n",
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"- It iterates over each `book` in the list `books`.\n",
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"\n",
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"- For each `book`, it tries to get the value associated with the key `\"author\"`. The `.get(\"author\")` method returns the value for the key `\"author\"` if it exists, and provides a default value (`None` by default) if the key does not exist.\n",
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"\n",
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"- It then yields this author value back through the generator that returned `this yield from`.\n",
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"\n",
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"In other words, when you use `yield from`, Python takes all values yielded from one inner iteration point and turns them into values yielded point-by-point on a containing iteration. The values are \"yielded\" in an order dictated to it by the innermost sequence.\n",
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"\n",
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"Here is how that would look like:\n",
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"\n",
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"```\n",
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" for book, author in books:\n",
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" for author_from_book in yield_from(book.get(\"author\") if book.get(\"author\") else []):\n",
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" # Code here\n",
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" pass\n",
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" pass\n",
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"``` \n",
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"\n",
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"Or a simplified version with `if-express` and `map` like so\n",
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"\n",
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"```python\n",
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"import expression\n",
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"\n",
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"books = [\n",
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" {\"id\": 1, \"title\": 'Blurred Horizons'},\n",
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" {\"id\": 2, \"author\": 'Judy Blume'}, \n",
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" {\"id\": 3},\n",
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"]\n",
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"authors= ['blum', *exp(expression,\" books get\")()]\n",
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"\n",
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"for author in authors:\n",
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" pass\n",
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"```\n",
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"But again this is using something that would probably be written in a real application like so"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"\n",
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"\n",
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"print(f\"Getting explanation from {MODEL_LLAMA} using Ollama...\")\n",
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"\n",
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"try:\n",
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" response = requests.post(\n",
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" f\"{OLLAMA_BASE}/completions\",\n",
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" json={\n",
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" \"model\": MODEL_LLAMA,\n",
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" \"prompt\": question,\n",
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" \"stream\": True\n",
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" },\n",
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" stream=True,\n",
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" timeout=120\n",
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" )\n",
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"\n",
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" output = \"\"\n",
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" display_handle = display(Markdown(\"Generating response...\"), display_id=True)\n",
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"\n",
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" for line in response.iter_lines(decode_unicode=True):\n",
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" if not line.strip():\n",
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" continue\n",
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"\n",
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" # Each event line starts with \"data: \"\n",
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" if line.startswith(\"data: \"):\n",
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" line = line[len(\"data: \"):]\n",
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"\n",
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" if line.strip() == \"[DONE]\":\n",
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" break\n",
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"\n",
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" try:\n",
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" data = json.loads(line)\n",
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" except json.JSONDecodeError:\n",
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" continue\n",
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"\n",
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" # In Ollama /v1/completions, the text comes in data['choices'][0]['text']\n",
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" text = data.get(\"choices\", [{}])[0].get(\"text\", \"\")\n",
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" if text:\n",
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" output += text\n",
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" update_display(Markdown(output), display_id=display_handle.display_id)\n",
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"\n",
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" print(\"\\nFinal explanation:\\n\")\n",
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" display(Markdown(f\"### Llama 3.2 Explanation\\n\\n{output.strip()}\"))\n",
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"\n",
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"except requests.exceptions.ConnectionError:\n",
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" print(\"Could not connect to Ollama — make sure it’s running (run `ollama serve`).\")\n",
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"except Exception as e:\n",
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" print(\"Unexpected error:\", e)\n"
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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": ".venv",
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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": "3.13.5"
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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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