Merge pull request #865 from joeymaza/jmz/lab1_exercises

Week 1 Exercise- AI Tutor

week1/community-contributions/week1_exercise_jmz.ipynb

@@ -0,0 +1,172 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "712506d5",
+   "metadata": {},
+   "source": [
+    "This is my week 1 exercise experiment.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3058139d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Imports\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "from dotenv import load_dotenv\n",
+    "from IPython.display import Markdown, display, update_display\n",
+    "from openai import OpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd4d9f32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Constants andn Initializing GPT\n",
+    "\n",
+    "MODEL_GPT = 'gpt-4o-mini'\n",
+    "MODEL_LLAMA = 'llama3.2'\n",
+    "OLLAMA_BASE_URL = \"http://localhost:11434/v1\"\n",
+    "openai = OpenAI()\n",
+    "ollama = OpenAI(base_url=OLLAMA_BASE_URL, api_key='ollama')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0199945b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Check API key\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "api_key = os.getenv('OPENAI_API_KEY')\n",
+    "\n",
+    "if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n",
+    "    print(\"API key looks good so far\")\n",
+    "else:\n",
+    "    print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a671fa0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Prompts\n",
+    "\n",
+    "system_prompt = \"\"\"\n",
+    "You are a senior software coding master. \n",
+    "You will help explain an input of code, check if there are errors and correct them.\n",
+    "Show how this code works and suggest other ways of writing this code efficiently if there is an alternative.\n",
+    "Respond to a user who is a beginner. \"\"\"\n",
+    "\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",
+    "Show some examples on the use of this code.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1fbc6aa5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Function to stream response of output from OpenAI API\n",
+    "\n",
+    "def code_examiner_stream(question):\n",
+    "    stream = openai.chat.completions.create(\n",
+    "        model=MODEL_GPT,\n",
+    "        messages=[\n",
+    "            {\"role\": \"system\", \"content\": system_prompt},\n",
+    "            {\"role\": \"user\", \"content\": question}\n",
+    "          ],\n",
+    "        stream=True\n",
+    "    )    \n",
+    "    response = \"\"\n",
+    "    display_handle = display(Markdown(\"\"), display_id=True)\n",
+    "    for chunk in stream:\n",
+    "        response += chunk.choices[0].delta.content or ''\n",
+    "        update_display(Markdown(response), display_id=display_handle.display_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07d93dba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "code_examiner_stream(question)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb7184cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Function for Ollama (locally) to reponse with output.\n",
+    "\n",
+    "def code_examiner_ollama(question):\n",
+    "    response = ollama.chat.completions.create(\n",
+    "        model=MODEL_LLAMA,\n",
+    "        messages=[\n",
+    "            {\"role\": \"system\", \"content\":system_prompt},\n",
+    "            {\"role\": \"user\", \"content\": question}\n",
+    "        ],\n",
+    "    )\n",
+    "    result = response.choices[0].message.content\n",
+    "    display(Markdown(result))\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "code_examiner_ollama(question)"
+   ]
+  }
+ ],
+ "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}