Add Copilot and weather agent notebooks
Introduces Copilot.ipynb, an adaptive AI coding assistant with OpenAI and Gemini integration via Gradio, and weather_agent.ipynb, a weather chat agent supporting current, historical, and forecast queries using WeatherAPI and OpenAI tool-calling. Both notebooks provide interactive UIs for user queries.
This commit is contained in:
KiranAyyagari
212
week2/community-contributions/Copilot.ipynb
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212
week2/community-contributions/Copilot.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1877ad68",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import requests\n",
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"from openai import OpenAI\n",
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"import gradio as gr\n",
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"from dotenv import load_dotenv \n",
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"import google.generativeai as genai\n",
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"from IPython.display import Markdown, display, update_display\n",
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"load_dotenv(override=True)"
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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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"id": "008056a2",
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"metadata": {},
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"outputs": [],
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"source": [
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"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
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"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
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"\n",
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"if openai_api_key:\n",
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" print(f'OpenAi api key exists and its starts with {openai_api_key[:3]}')\n",
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"else:\n",
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" print(\"OpenAi api key doesn't exist\")\n",
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"\n",
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"if google_api_key:\n",
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" print('Google api key exists')\n",
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"else:\n",
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" print(\"Google api key doesn't exist\")\n",
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"\n",
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"OPENAI_MODEL = \"gpt-4o-mini\"\n",
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"GOOGLE_MODEL = \"gemini-1.5-flash\"\n",
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"\n",
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"openai = OpenAI()\n",
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"\n",
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"genai.configure()"
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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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"id": "5013ed7b",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_msg = \"\"\"\n",
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"You are CodeCopilot, an adaptive AI coding assistant that helps users solve problems in any programming language.\n",
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"Always provide correct, runnable, and well-formatted code with clear explanations.\n",
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"Adjust your style based on the user’s expertise: for beginners, break concepts down step by step with simple examples and commented code;\n",
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"for advanced users, deliver concise, production-ready, optimized solutions with best practices and trade-off insights.\n",
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"Ask clarifying questions when requirements are ambiguous, highlight pitfalls and edge cases,\n",
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"and act as a collaborative pair programmer or mentor whose goal is to help users learn, build, and ship high-quality code efficiently.\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": null,
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"id": "35c480a1",
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"metadata": {},
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"outputs": [],
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"source": [
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"def create_prompt(prompt, history):\n",
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" messages = [{\"role\": \"system\", \"content\": system_msg}]\n",
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"\n",
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" # history is a list of (user_msg, assistant_msg) tuples\n",
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" for user_msg, assistant_msg in history:\n",
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" if user_msg:\n",
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" messages.append({\"role\": \"user\", \"content\": user_msg})\n",
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" if assistant_msg:\n",
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" messages.append({\"role\": \"assistant\", \"content\": assistant_msg})\n",
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"\n",
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" # new user prompt\n",
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" messages.append({\"role\": \"user\", \"content\": prompt})\n",
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" return messages"
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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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"id": "5dfbecd0",
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"metadata": {},
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"outputs": [],
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"source": [
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"def openai_agent(prompt, history):\n",
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" openai.api_key = openai_api_key\n",
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" messages = create_prompt(prompt, history)\n",
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" response = openai.chat.completions.create(\n",
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" model=OPENAI_MODEL,\n",
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" messages=messages,\n",
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" stream=True\n",
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" )\n",
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" sent_any = False\n",
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" for chunk in response:\n",
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" delta = chunk.choices[0].delta\n",
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" if delta and delta.content:\n",
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" sent_any = True\n",
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" yield delta.content\n",
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" if not sent_any:\n",
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" yield \"(no response)\""
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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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"id": "535f7e3d",
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"metadata": {},
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"outputs": [],
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"source": [
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"def gemini_agent(prompt, history):\n",
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" genai.configure(api_key=google_api_key)\n",
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"\n",
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" # reuse OpenAI-style messages\n",
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" messages = create_prompt(prompt, history)\n",
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"\n",
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" gemini_history = []\n",
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" for m in messages:\n",
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" # Gemini does NOT support system role\n",
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" if m[\"role\"] == \"system\":\n",
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" continue\n",
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" gemini_history.append({\n",
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" \"role\": m[\"role\"],\n",
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" \"parts\": [m[\"content\"]]\n",
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" })\n",
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" prompt_with_system = f\"{system_msg}\\n\\n{prompt}\"\n",
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" model = genai.GenerativeModel(GOOGLE_MODEL)\n",
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" chat = model.start_chat(history=gemini_history)\n",
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"\n",
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" response = chat.send_message(prompt_with_system, stream=True)\n",
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" for chunk in response:\n",
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" if chunk and getattr(chunk, \"text\", None):\n",
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" yield chunk.text\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": null,
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"id": "21f61ff0",
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"metadata": {},
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"outputs": [],
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"source": [
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"def chat_agent(prompt, history, modelType):\n",
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" if modelType == \"OpenAI\":\n",
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" for token in openai_agent(prompt, history):\n",
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" yield token\n",
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" else:\n",
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" for token in gemini_agent(prompt, history):\n",
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" yield token\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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"id": "56686c1d",
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"metadata": {},
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"outputs": [],
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"source": [
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"def chat_fn(prompt, history, model):\n",
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" assistant_response = \"\"\n",
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" for token in chat_agent(prompt, history, model):\n",
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" assistant_response += token\n",
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" yield assistant_response \n",
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"\n",
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"# -------------------------------------------------------------------\n",
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"# UI\n",
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"# -------------------------------------------------------------------\n",
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"with gr.Blocks() as demo:\n",
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" model_choice = gr.Radio([\"OpenAI\", \"Gemini\"], value=\"OpenAI\", label=\"Model\")\n",
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"\n",
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" chat_ui = gr.ChatInterface(\n",
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" fn=chat_fn,\n",
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" additional_inputs=[model_choice],\n",
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" title=\"CodeCopilot\",\n",
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" description=\"An adaptive AI coding assistant that helps developers build and ship high-quality code.\"\n",
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" )\n",
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"\n",
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"demo.launch()"
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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": "llms",
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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": "3.11.13"
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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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370
week2/community-contributions/weather_agent.ipynb
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370
week2/community-contributions/weather_agent.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "60761989",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import requests\n",
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"from openai import OpenAI\n",
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"import gradio as gr\n",
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"import speech_recognition as sr\n",
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"import json\n",
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"from dotenv import load_dotenv"
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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": "e0b6610a",
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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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"OpenAI API Key exists and begins sk-proj-\n",
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"weather API Key exists\n"
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]
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}
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],
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"source": [
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"# Initialization\n",
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"\n",
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"load_dotenv(override=True)\n",
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"\n",
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"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
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"weather_api_key = os.getenv('WEATHER_API_KEY')\n",
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"if openai_api_key:\n",
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" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
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"else:\n",
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" print(\"OpenAI API Key not set\")\n",
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"if weather_api_key:\n",
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" print(\"weather API Key exists\")\n",
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"else:\n",
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" print(\"weather API Key not set\")\n",
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" \n",
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"MODEL = \"gpt-4o-mini\"\n",
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"openai = OpenAI()"
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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": "af9d2faf",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_message = \"You are a helpful assistant for weather. \"\n",
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"system_message += \"You need to fetch the current, historical and forecast the weather data using weather api and provide the response\""
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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": "2c5208d8",
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"metadata": {},
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"outputs": [],
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"source": [
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"def fetch_current_weather(location):\n",
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" url = f\"http://api.weatherapi.com/v1/current.json?key={weather_api_key}&q={location}&aqi=yes\"\n",
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" return requests.get(url).json()"
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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": 5,
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"id": "8e6a12e5",
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"metadata": {},
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"outputs": [],
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"source": [
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"def fetch_forecast_weather(location, days=3):\n",
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" url = f\"http://api.weatherapi.com/v1/forecast.json?key={weather_api_key}&q={location}&days={days}&aqi=yes&alerts=yes\"\n",
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" return requests.get(url).json()"
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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": 6,
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"id": "eafc468e",
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"metadata": {},
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"outputs": [],
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"source": [
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"def fetch_historical_weather(location, date):\n",
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" url = f\"http://api.weatherapi.com/v1/history.json?key={weather_api_key}&q={location}&dt={date}&aqi=yes\"\n",
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" return requests.get(url).json()"
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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": 7,
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"id": "2851ed55",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Weather function used as a tool by OpenAI\n",
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"def get_weatherapi_data(location, mode=\"current\", date=None, forecast_days=3):\n",
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" if mode == \"current\":\n",
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" return fetch_current_weather(location)\n",
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" elif mode == \"forecast\":\n",
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" return fetch_forecast_weather(location, days=forecast_days)\n",
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" elif mode == \"historical\":\n",
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" if not date:\n",
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" # Default: yesterday\n",
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" date = (datetime.date.today() - datetime.timedelta(days=1)).strftime(\"%Y-%m-%d\")\n",
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" return fetch_historical_weather(location, date)\n",
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" else:\n",
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" return {\"error\": \"Unknown mode.\"}"
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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": 8,
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"id": "368176c2",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Tool schema for OpenAI tool-calling\n",
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"weatherapi_tool_schema = [\n",
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" {\n",
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" \"type\": \"function\",\n",
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" \"function\": {\n",
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" \"name\": \"get_weatherapi_data\",\n",
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" \"description\": \"Fetches current, forecast, or historical weather data from WeatherAPI.com for a given location.\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"location\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"Name of the city, region, or coordinates.\"\n",
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" },\n",
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" \"mode\": {\n",
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" \"type\": \"string\",\n",
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" \"enum\": [\"current\", \"forecast\", \"historical\"],\n",
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" \"description\": \"Type of weather data required.\"\n",
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" },\n",
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" \"date\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"Date for historical data in YYYY-MM-DD format. Only needed if mode is 'historical'.\"\n",
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" },\n",
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" \"forecast_days\": {\n",
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" \"type\": \"integer\",\n",
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" \"description\": \"Number of forecast days (1-10). Only needed if mode is 'forecast'.\"\n",
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" }\n",
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" },\n",
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" \"required\": [\"location\", \"mode\"]\n",
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" }\n",
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" }\n",
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" }\n",
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"]"
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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": 9,
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"id": "bd9c4d38",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"def audio_to_text(audio_filepath):\n",
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" if audio_filepath is None or audio_filepath == \"\":\n",
|
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" return \"\"\n",
|
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" recognizer = sr.Recognizer()\n",
|
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" try:\n",
|
||||
" with sr.AudioFile(audio_filepath) as source:\n",
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" audio = recognizer.record(source)\n",
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" try:\n",
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" transcript = recognizer.recognize_google(audio)\n",
|
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" return transcript\n",
|
||||
" except sr.UnknownValueError:\n",
|
||||
" return \"\"\n",
|
||||
" except sr.RequestError as e:\n",
|
||||
" return f\"Speech recognition service error: {e}\"\n",
|
||||
" except Exception as e:\n",
|
||||
" return f\"Error opening audio file: {str(e)}\""
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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": 10,
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||||
"id": "61c5de82",
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||||
"metadata": {},
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||||
"outputs": [],
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"source": [
|
||||
"def chat_agent(city, mode, date, forecast_days, audio=None):\n",
|
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" user_query = city\n",
|
||||
" if audio:\n",
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||||
" spoken_text = audio_to_text(audio)\n",
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" print(\"Recognized speech:\", spoken_text)\n",
|
||||
" if spoken_text and spoken_text.strip().lower() != \"flic en flac\":\n",
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||||
" user_query = spoken_text\n",
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" else:\n",
|
||||
" if not city.strip():\n",
|
||||
" return \"Sorry, I could not recognize your speech. Please try again or type your city.\"\n",
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||||
"\n",
|
||||
" if not user_query.strip():\n",
|
||||
" return \"Please provide a location by text or speech.\"\n",
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||||
"\n",
|
||||
" # Compose tool function arguments as the LLM would\n",
|
||||
" args = {\n",
|
||||
" \"location\": user_query,\n",
|
||||
" \"mode\": mode\n",
|
||||
" }\n",
|
||||
" if mode == \"historical\" and date:\n",
|
||||
" args[\"date\"] = date\n",
|
||||
" if mode == \"forecast\":\n",
|
||||
" try:\n",
|
||||
" n_days = int(forecast_days)\n",
|
||||
" except:\n",
|
||||
" n_days = 3\n",
|
||||
" args[\"forecast_days\"] = n_days\n",
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||||
"\n",
|
||||
" openai.api_key = openai_api_key\n",
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||||
"\n",
|
||||
" # LLM call for tool use\n",
|
||||
" response = openai.chat.completions.create(\n",
|
||||
" model=\"gpt-4-0613\",\n",
|
||||
" messages=[{\"role\": \"user\", \"content\": f\"Get me {mode} weather for {user_query}\"+(f' on {date}' if date and mode==\"historical\" else \"\")+(f' for {forecast_days} days' if forecast_days and mode==\"forecast\" else \"\")}],\n",
|
||||
" tools=weatherapi_tool_schema,\n",
|
||||
" tool_choice={\"type\": \"function\", \"function\": {\"name\": \"get_weatherapi_data\", \"arguments\": json.dumps(args)}}\n",
|
||||
" )\n",
|
||||
" message = response.choices[0].message\n",
|
||||
"\n",
|
||||
" if hasattr(message, \"tool_calls\") and message.tool_calls:\n",
|
||||
" tool_call = message.tool_calls[0]\n",
|
||||
" args2 = json.loads(tool_call.function.arguments) # not really needed, already have args\n",
|
||||
" location = args2.get(\"location\", user_query)\n",
|
||||
" mode = args2.get(\"mode\", mode)\n",
|
||||
" date = args2.get(\"date\", date)\n",
|
||||
" forecast_days = args2.get(\"forecast_days\", forecast_days)\n",
|
||||
" weather_data = get_weatherapi_data(location, mode, date, forecast_days)\n",
|
||||
" tool_result = f\"Weather data (mode={mode}) for {location}:\\n{json.dumps(weather_data, indent=2)[:3000]}\"\n",
|
||||
" followup = openai.chat.completions.create(\n",
|
||||
" model=\"gpt-4-0613\",\n",
|
||||
" messages=[\n",
|
||||
" {\"role\": \"user\", \"content\": f\"Get me {mode} weather for {location}\"},\n",
|
||||
" message,\n",
|
||||
" {\n",
|
||||
" \"role\": \"tool\",\n",
|
||||
" \"tool_call_id\": tool_call.id,\n",
|
||||
" \"content\": tool_result\n",
|
||||
" }\n",
|
||||
" ]\n",
|
||||
" )\n",
|
||||
" answer = followup.choices[0].message.content.strip()\n",
|
||||
" return answer\n",
|
||||
" else:\n",
|
||||
" return getattr(message, \"content\", \"\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "44071389",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def update_date_visibility(mode):\n",
|
||||
" return gr.update(visible=(mode==\"historical\"))\n",
|
||||
"\n",
|
||||
"def update_days_visibility(mode):\n",
|
||||
" return gr.update(visible=(mode==\"forecast\"))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "618a5494",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"* Running on local URL: http://127.0.0.1:7861\n",
|
||||
"* To create a public link, set `share=True` in `launch()`.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div><iframe src=\"http://127.0.0.1:7861/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": []
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Recognized speech: Error opening audio file: FLAC conversion utility not available - consider installing the FLAC command line application by running `apt-get install flac` or your operating system's equivalent\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"with gr.Blocks() as demo:\n",
|
||||
" gr.Markdown(\"## Weather Chat Agent (Current, Historical, Forecast)\")\n",
|
||||
"\n",
|
||||
" with gr.Row():\n",
|
||||
" city_input = gr.Textbox(label=\"City/Location\")\n",
|
||||
" mode_input = gr.Dropdown(\n",
|
||||
" [\"current\", \"historical\", \"forecast\"],\n",
|
||||
" value=\"current\",\n",
|
||||
" label=\"Weather Mode\")\n",
|
||||
" with gr.Row():\n",
|
||||
" date_input = gr.Textbox(label=\"Date for historical (YYYY-MM-DD)\", visible=False)\n",
|
||||
" days_input = gr.Textbox(label=\"Forecast Days (for forecast)\", value=\"3\", visible=False)\n",
|
||||
" audio_input = gr.Audio(type=\"filepath\", format=\"wav\", label=\"Or Speak your City/Location (optional)\")\n",
|
||||
" output_box = gr.Textbox(label=\"Weather Info\", lines=8)\n",
|
||||
" btn = gr.Button(\"Get Weather\")\n",
|
||||
"\n",
|
||||
" # Show/hide date and days inputs based on dropdown\n",
|
||||
" mode_input.change(update_date_visibility, mode_input, date_input)\n",
|
||||
" mode_input.change(update_days_visibility, mode_input, days_input)\n",
|
||||
" btn.click(\n",
|
||||
" chat_agent,\n",
|
||||
" [city_input, mode_input, date_input, days_input, audio_input],\n",
|
||||
" output_box\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
"demo.launch()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "llms",
|
||||
"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
|
||||
}
|
||||
Reference in New Issue
Block a user