{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5",
   "metadata": {},
   "source": [
    "# End of week 1 exercise\n",
    "\n",
    "To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question,  \n",
    "and responds with an explanation. This is a tool that you will be able to use yourself during the course!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ea775a9-12c7-4a63-a676-d7bd0cdb100c",
   "metadata": {},
   "source": [
    "# imports\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "from IPython.display import Markdown, display, update_display\n",
    "from openai import OpenAI\n",
    "import ollama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# constants\n",
    "MODEL_GPT = 'gpt-4o-mini'\n",
    "MODEL_LLAMA = 'llama3.2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up environment\n",
    "load_dotenv(override=True)\n",
    "api_key = os.getenv('OPENAI_API_KEY')\n",
    "\n",
    "if not api_key:\n",
    "    print(\"No API key was found!\")\n",
    "else:\n",
    "    print(\"API key found and looks good so far!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
   "metadata": {},
   "outputs": [],
   "source": [
    "# here is the question\n",
    "question = \"\"\"\n",
    "Please explain why do tennis players often use topspin on their forehand shots, and what advantages does it provide?\n",
    "\"\"\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "967aac6b-9f9c-4def-8659-d9382b0c59e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "system_prompt = \"You are a helpful tennis coach who answers questions about tennis rules, techniques, strategies, training, and equipment.\"\n",
    "user_prompt = \"Please give a detailed explanation to the following question: \" + question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7936b5af-e912-4e0e-b43e-87673c4857cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "messages = [\n",
    "    {\"role\": \"system\", \"content\": system_prompt},\n",
    "    {\"role\": \"user\", \"content\": user_prompt}\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get gpt-4o-mini to answer, with streaming\n",
    "openai = OpenAI()\n",
    "stream = openai.chat.completions.create(model=MODEL_GPT, messages=messages, stream=True)\n",
    "response = \"\"\n",
    "display_handle = display(Markdown(\"\"), display_id=True)\n",
    "for chunk in stream:\n",
    "    response += chunk.choices[0].delta.content or ''\n",
    "    response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
    "    update_display(Markdown(response), display_id=display_handle.display_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Llama 3.2 to answer\n",
    "response = ollama.chat(model=MODEL_LLAMA, messages=messages)\n",
    "result = response['message']['content']\n",
    "display(Markdown(result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29e9cdd3-5adc-4428-9758-f761dc91783a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}