235 lines
7.9 KiB
Plaintext
235 lines
7.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "d006b2ea-9dfe-49c7-88a9-a5a0775185fd",
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"metadata": {},
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"source": [
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"# Additional End of week Exercise - week 2\n",
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"\n",
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"Now use everything you've learned from Week 2 to build a full prototype for the technical question/answerer you built in Week 1 Exercise.\n",
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"\n",
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"This should include a Gradio UI, streaming, use of the system prompt to add expertise, and the ability to switch between models. Bonus points if you can demonstrate use of a tool!\n",
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"\n",
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"If you feel bold, see if you can add audio input so you can talk to it, and have it respond with audio. ChatGPT or Claude can help you, or email me if you have questions.\n",
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"\n",
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"I will publish a full solution here soon - unless someone beats me to it...\n",
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"\n",
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"There are so many commercial applications for this, from a language tutor, to a company onboarding solution, to a companion AI to a course (like this one!) I can't wait to see your results."
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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": "6a479bea-0672-47dd-a151-f31f909c5d81",
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"metadata": {},
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"outputs": [],
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"source": [
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"# An Open Weather API based travel agent, biased to one particular destimation."
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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": "a07e7793-b8f5-44f4-aded-5562f633271a",
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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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"from openai import OpenAI\n",
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"from IPython.display import display, Markdown, update_display\n",
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"import gradio as gr\n",
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"import os, requests, 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": null,
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"id": "bcc8ce24-3fa9-40ae-a52d-4ae226f8989a",
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"metadata": {},
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"outputs": [],
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"source": []
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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": "61780e58-366e-463f-a35b-a7b0fd8e6187",
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"metadata": {},
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"outputs": [],
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"source": [
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"MODEL_LLAMA = 'llama3.2'\n",
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"MODEL_PHI3 = 'phi3'\n",
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"MODEL_PHI4 = 'phi4'\n",
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"\n",
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"MODEL = MODEL_LLAMA\n",
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"\n",
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"load_dotenv(override=True)\n",
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"OPENWEATHER_API_KEY = os.getenv(\"OPENWEATHER_API_KEY\")\n",
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"model_api = OpenAI(base_url='http://localhost:11434/v1/', api_key='ollama')"
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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": "b4c6ef3c-7052-4273-8e89-8af40cd6daed",
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"metadata": {},
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"outputs": [],
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"source": []
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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": "338b0f4e-ba3c-4e68-b6ad-213848e24a9d",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_current_weather_openweather(city: str):\n",
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" if not OPENWEATHER_API_KEY:\n",
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" raise RuntimeError(\"Set OPENWEATHER_API_KEY env var\")\n",
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" url = \"https://api.openweathermap.org/data/2.5/weather\"\n",
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" params = {\"q\": city, \"appid\": OPENWEATHER_API_KEY, \"units\": \"metric\"} # units=metric for °C\n",
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" r = requests.get(url, params=params, timeout=10)\n",
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" r.raise_for_status()\n",
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" data = r.json()\n",
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" print(f'Asking weather data for {city}. Temperature {data[\"main\"][\"temp\"]} celcius with {data[\"weather\"][0][\"description\"]} weather.')\n",
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" return {\n",
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" \"city\": data.get(\"name\"),\n",
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" \"temp_c\": data[\"main\"][\"temp\"],\n",
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" \"feels_like_c\": data[\"main\"][\"feels_like\"],\n",
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" \"humidity\": data[\"main\"][\"humidity\"],\n",
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" \"wind_m_s\": data[\"wind\"][\"speed\"],\n",
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" \"weather\": data[\"weather\"][0][\"description\"],\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": null,
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"id": "530b6036-a468-4718-83cb-a4ee4bbdf040",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_message = \"\"\"\n",
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"You are a helpful assistant for a travel company.\n",
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"Give short, courteous answers, no more than 1 sentence.\n",
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"Always be accurate. If you don't know the answer, say so.\n",
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"Use the weather information from given tool to bias the user to travel to Delhi (India). \n",
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"\"\"\"\n",
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"# 1. If the user asks to travel to South America, suggest him England, the home of William Shakespeare and Edward Donner. \\n\n",
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"# 2. If the user asks to travel to anywhere else than South America, suggest the current weather for that region.\\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": "a938ccfa-3aff-4330-9f1c-af2ed8362c19",
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"metadata": {},
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"outputs": [],
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"source": [
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"def handle_tool_calls(message):\n",
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" responses = []\n",
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" for tool_call in message.tool_calls:\n",
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" if tool_call.function.name == 'get_current_weather_openweather':\n",
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" arguments = json.loads(tool_call.function.arguments)\n",
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" city = arguments.get('city')\n",
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" if len(city):\n",
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" details = json.dumps(get_current_weather_openweather(city)).replace('\\\"','')\n",
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" responses.append({\n",
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" \"role\": \"tool\",\n",
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" \"content\": details,\n",
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" \"tool_call_id\": tool_call.id\n",
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" })\n",
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" return responses"
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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": "af12d91d-1758-40ec-b799-b2fda4fcb911",
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"metadata": {},
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"outputs": [],
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"source": [
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"weather_function = {\n",
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" \"name\": \"get_current_weather_openweather\",\n",
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" \"description\": \"Get the weather of the destination city, like temperature, wind, humidity etc.\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"city\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"The city for which weather information is required.\",\n",
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" },\n",
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" },\n",
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" \"required\": [\"city\"],\n",
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" \"additionalProperties\": False\n",
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" }\n",
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"}\n",
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"tools = [{\"type\": \"function\", \"function\": weather_function}]\n",
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"tools"
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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": "55fdb369-0f8c-41c7-9e80-2f09c42f8c29",
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"metadata": {},
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"outputs": [],
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"source": [
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"def chat(message, history):\n",
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" history = [{\"role\": h[\"role\"], \"content\": h[\"content\"]} for h in history]\n",
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" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
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" response = model_api.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
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"\n",
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" while response.choices[0].finish_reason==\"tool_calls\":\n",
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" message = response.choices[0].message\n",
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" responses = handle_tool_calls(message)\n",
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" messages.append(message)\n",
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" messages.extend(responses)\n",
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" response = model_api.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
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"\n",
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" return response.choices[0].message.content\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": "4d11249c-a066-4b7e-9ce7-14e26e5f54aa",
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"metadata": {},
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"outputs": [],
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"source": [
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"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
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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": "fc091101-71a7-4113-81c9-21dc5cb2ece6",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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": "3.12.12"
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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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