336 lines
11 KiB
Plaintext
336 lines
11 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": 1,
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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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"\n",
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"import os\n",
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"import json\n",
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"from dotenv import load_dotenv\n",
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"from openai import OpenAI\n",
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"import gradio as gr"
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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": "2118e80a-6181-4488-95cf-c9da0500ea56",
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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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"Google API Key exists and begins AIzaSyA7\n"
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]
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}
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],
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"source": [
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"# Load environment variables in a file called .env\n",
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"# Print the key prefixes to help with any debugging\n",
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"\n",
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"load_dotenv(override=True)\n",
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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 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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" \n",
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"if google_api_key:\n",
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" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
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"else:\n",
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" print(\"Google API Key not set\")"
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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": "8ddc4764-e7f6-4512-8210-51bbfefbb3a9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Set base url\n",
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"\n",
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"GEMINI_BASE_URL = \"https://generativelanguage.googleapis.com/v1beta/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": 4,
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"id": "91bfd734-9c5e-4993-808e-b66489a92d4d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Connect to OpenAI, Anthropic and Google; comment out the Claude or Google lines if you're not using them\n",
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"\n",
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"openai = OpenAI()\n",
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"gemini = OpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)"
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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": "d9ee11ae-23e2-42cc-b63d-b446f6d83c99",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Set models\n",
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"\n",
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"gpt_model = \"gpt-4.1-mini\"\n",
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"gemini_model = \"gemini-2.0-flash\""
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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": "a01d270e-f62e-41b3-8e46-ac173d7a1493",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_gpt_prompt = \"You are an assistant with general knowledge obtained from the internet. \\\n",
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"Always respond with a cheerful tone. If you don’t know the answer to a question, simply say that you don’t know.\""
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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": "4e85c8ed-3ba4-4283-8480-6979b0d5602f",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_gemini_prompt = \"You are an expert translator with knowledge of all existing languages. \\\n",
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"Your only task is, given a provided sentence, to translate it into the specified target language. \\\n",
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"Do not provide anything else in your response only the translation itself.\""
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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": "8ee0c887-a63f-48dd-8eaf-68b0bf9263b6",
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"metadata": {},
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"outputs": [],
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"source": [
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"def count_letter_tool(sentence, letter):\n",
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"\n",
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" if len(letter) != 1:\n",
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" return \"You need to provide a single letter to count\"\n",
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" \n",
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" return sentence.lower().count(letter.lower())"
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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": "5f1ae918-cb99-4e60-80d3-37e16e514f55",
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"metadata": {},
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"outputs": [],
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"source": [
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"def translator_tool(sentence, language):\n",
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" user_message = f\"Please translate this sentence: \\\"{sentence}\\\" to this language: {language}\"\n",
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" messages = [{\"role\": \"system\", \"content\": system_gemini_prompt}, {\"role\": \"user\", \"content\":user_message}]\n",
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" response = gemini.chat.completions.create(model=gemini_model, messages=messages)\n",
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"\n",
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" return response.choices[0].message.content"
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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": 11,
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"id": "2d499f2a-23b2-4fff-9d2d-f2333cbd109a",
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"metadata": {},
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"outputs": [],
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"source": [
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"count_letter_function = {\n",
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" \"name\": \"count_letter_tool\",\n",
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" \"description\": \"Count the number of a particular letter in a sentence. Call this whenever you need to know how many times a letter appears in a sentence, for example when a user asks 'How many 'a' are in this sentence?'\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"sentence\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"The sentence provided by the user for counting.\"\n",
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" },\n",
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" \"letter\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"The letter to count in the sentence.\"\n",
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" }\n",
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" },\n",
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" \"required\": [\"sentence\", \"letter\"],\n",
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" \"additionalProperties\": False\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": 12,
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"id": "b58079a8-8def-4fa6-8273-34bf8eeb8cb5",
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"metadata": {},
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"outputs": [],
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"source": [
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"translator_function = {\n",
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" \"name\": \"translator_tool\",\n",
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" \"description\": \"Translate a sentence provided by the user. Call this whenever a translation is needed, for example when a user asks 'Can you translate \\\"hola como estás?\\\" to English?'\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"sentence\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"The sentence provided by the user to translate.\"\n",
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" },\n",
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" \"language\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"The target language to translate the sentence into.\"\n",
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" }\n",
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" },\n",
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" \"required\": [\"sentence\", \"language\"],\n",
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" \"additionalProperties\": False\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": 13,
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"id": "7ab7fc93-3540-48e5-bbe0-3e9ad2bbce15",
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"metadata": {},
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"outputs": [],
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"source": [
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"tools = [{\"type\": \"function\", \"function\": count_letter_function}, {\"type\": \"function\", \"function\": translator_function}]"
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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": 14,
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"id": "678ccc37-c034-4035-bc3c-00fa8bcd8e64",
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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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" messages = [{\"role\": \"system\", \"content\": system_gpt_prompt}] + history + [{\"role\": \"user\", \"content\": message}]\n",
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" response = openai.chat.completions.create(model=gpt_model, messages=messages, tools=tools)\n",
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"\n",
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" if response.choices[0].finish_reason==\"tool_calls\":\n",
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" message = response.choices[0].message\n",
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" response = handle_tool_call(message)\n",
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" messages.append(message)\n",
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" messages.append(response)\n",
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" response = openai.chat.completions.create(model=gpt_model, messages=messages)\n",
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" \n",
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" return response.choices[0].message.content"
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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": 15,
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"id": "2a1138e4-f849-4557-a74c-f9feb1572854",
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"metadata": {},
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"outputs": [],
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"source": [
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"def handle_tool_call(message):\n",
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" tool_call = message.tool_calls[0]\n",
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" arguments = json.loads(tool_call.function.arguments)\n",
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" sentence = arguments.get('sentence')\n",
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" response =\"\"\n",
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" match tool_call.function.name:\n",
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" case \"translator_tool\":\n",
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" language = arguments.get('language')\n",
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" translation = translator_tool(sentence, language)\n",
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" response = {\"role\": \"tool\", \"content\": json.dumps({\"translation\": translation}), \"tool_call_id\": tool_call.id}\n",
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" case \"count_letter_tool\":\n",
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" letter = arguments.get('letter')\n",
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" count = count_letter_tool(sentence, letter)\n",
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" response = {\"role\": \"tool\", \"content\": json.dumps({\"count\": count}), \"tool_call_id\": tool_call.id}\n",
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"\n",
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" return 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": 16,
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"id": "d39344cc-9e89-47a0-9249-2e182091ee43",
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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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"* Running on local URL: http://127.0.0.1:7860\n",
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"* To create a public link, set `share=True` in `launch()`.\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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],
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"text/plain": [
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"<IPython.core.display.HTML 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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"data": {
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"text/plain": []
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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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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"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": "3.11.13"
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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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