623 lines
24 KiB
Plaintext
623 lines
24 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": "7b624d5b-69a2-441f-9147-fde105d3d551",
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"metadata": {},
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"outputs": [],
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"source": [
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"# sample question to use in the Gradle UI that pops up\n",
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"\n",
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"question = \"\"\"\n",
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"How good at Software Development is Elijah Rwothoromo? \\\n",
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"He has a Wordpress site https://rwothoromo.wordpress.com/. \\\n",
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"He also has a LinkedIn profile https://www.linkedin.com/in/rwothoromoelaijah/. \\\n",
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"As well as a GitHub Profile https://www.github.com/rwothoromo/.\\\n",
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"What can we learn from him?\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": "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 re, requests, os, json, tempfile, gradio as gr, anthropic, google.generativeai, ollama\n",
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"from bs4 import BeautifulSoup\n",
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"from IPython.display import Markdown, display, update_display\n",
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"from dotenv import load_dotenv\n",
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"from openai import OpenAI\n",
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"from pydub import AudioSegment\n",
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"from pydub.playback import play\n",
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"from io import BytesIO\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": "efb88276-6d74-4d94-95a2-b8ca82a4716c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load environment variables\n",
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"load_dotenv()\n",
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"\n",
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"openai_api_key = os.getenv('OPENAI_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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"\n",
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"\n",
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"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
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"if anthropic_api_key:\n",
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" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:8]}\")\n",
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"else:\n",
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" print(\"Anthropic API Key not set\")\n",
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"\n",
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"\n",
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"google_api_key = os.getenv('GOOGLE_API_KEY')\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": null,
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"id": "484f0c3e-638d-4af7-bb9b-36faf6048f3c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# constants\n",
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"\n",
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"MODEL_CLAUDE = \"claude-sonnet-4-20250514\"\n",
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"MODEL_GEMINI = \"gemini-2.5-flash\"\n",
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"MODEL_GPT = 'gpt-4o-mini'\n",
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"MODEL_LLAMA = 'llama3.2'\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": "2e292401-e62f-4bfc-b060-07462ad20d3d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# system messages\n",
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"\n",
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"system_message = \"You are an expert assistant. Synthesize a comprehensive answer in markdown format.\"\n",
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"system_prompt_with_url_data = \"You are an expert assistant. \\\n",
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" Analyze the user's question and the provided text from relevant websites to synthesize a comprehensive answer in markdown format.\\\n",
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" Provide a short summary, ignoring text that might be navigation-related.\"\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": "84252e03-ccde-4ecf-975b-78227291ca5c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# set up environment\n",
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"\n",
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"headers = {\n",
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" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\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": "49396924-47c2-4f7d-baa2-9b0fece9da4a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Website class for URLs to be scraped\n",
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"\n",
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"class Website:\n",
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" def __init__(self, url):\n",
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" \"\"\"\n",
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" Create this Website object from the given url using the BeautifulSoup library\n",
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" \"\"\"\n",
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" self.url = url\n",
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" response = requests.get(url, headers=headers)\n",
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" soup = BeautifulSoup(response.content, 'html.parser')\n",
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" self.title = soup.title.string if soup.title else \"No title found\"\n",
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" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
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" irrelevant.decompose()\n",
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" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\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": "c4d23747-d78a-4f36-9862-c00e1e8d9e44",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Instantiate models with API keys from environment variables\n",
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"\n",
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"openai = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n",
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"claude = anthropic.Anthropic(api_key=os.getenv(\"ANTHROPIC_API_KEY\"))\n",
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"google.generativeai.configure(api_key=os.getenv(\"GOOGLE_API_KEY\"))\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": "67e150be-502e-4ba4-9586-3a2f3fae3830",
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"metadata": {},
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"outputs": [],
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"source": [
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"# To scrape data based on URLs in the user prompt\n",
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"\n",
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"def scrape_urls(text):\n",
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" try:\n",
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" # Extract all URLs from the text string using regular expressions\n",
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" urls = re.findall(r'https?://[^\\s)]+', text)\n",
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" \n",
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" if len(urls) > 0:\n",
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" scraped_content = []\n",
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" for url in urls:\n",
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" print(f\"Scraping: {url}\")\n",
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" try:\n",
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" site = Website(url)\n",
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" content = f\"Content from {url}:\\n---\\n{site.text}\\n---\\n\"\n",
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" scraped_content.append(content)\n",
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" print(f\"Scraping done!\")\n",
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" except Exception as e:\n",
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" print(f\"Could not scrape {url}: {e}\")\n",
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" scraped_content.append(f\"Could not retrieve content from {url}.\\n\")\n",
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" \n",
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" return \"\\n\".join(scraped_content)\n",
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" else:\n",
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" return None\n",
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" except Exception as e:\n",
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" print(f\"Error during website scraping: {e}\")\n",
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" return \"Sorry, I encountered an error and could not complete scraping the website(s).\"\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": "bd9d0511-2f78-4270-81f8-73708388dfad",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Tool definition for scrape_urls\n",
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"\n",
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"scraping_function = {\n",
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" \"name\": \"scrape_urls\",\n",
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" \"description\": \"Scrapes available URLs for data to update the User prompt. Call this whenever a customer provides a URL.\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"text\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"The website URL or user prompt containing URLs.\"\n",
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" }\n",
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" },\n",
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" \"required\": [\"text\"]\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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"cell_type": "code",
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"execution_count": null,
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"id": "47733d5b-bb0a-44dd-b56d-a54677c88f80",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Instantiate the tools\n",
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"\n",
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"# tools = [{\"type\": \"function\", \"function\": scraping_function}]\n",
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"\n",
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"# Define Ollama tools\n",
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"tools_gpt_ollama = [{\"type\": \"function\", \"function\": scraping_function}]\n",
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"\n",
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"# Define Claude tools\n",
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"tools_claude = [{\n",
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" \"name\": scraping_function[\"name\"],\n",
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" \"description\": scraping_function[\"description\"],\n",
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" \"input_schema\": scraping_function[\"parameters\"]\n",
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"}]\n",
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"\n",
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"# Gemini tool definition must be a FunctionDeclaration object without the top-level `type` in parameters.\n",
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"tools_gemini = [google.generativeai.protos.FunctionDeclaration(\n",
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" name=portable_scraping_function_definition[\"name\"],\n",
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" description=portable_scraping_function_definition[\"description\"],\n",
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" parameters=google.generativeai.protos.Schema(\n",
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" type=google.generativeai.protos.Type.OBJECT,\n",
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" properties={\n",
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" \"text\": google.generativeai.protos.Schema(\n",
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" type=google.generativeai.protos.Type.STRING,\n",
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" description=portable_scraping_function_definition[\"parameters\"][\"properties\"][\"text\"][\"description\"]\n",
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" )\n",
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" },\n",
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" required=portable_scraping_function_definition[\"parameters\"][\"required\"]\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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"cell_type": "code",
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"execution_count": null,
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"id": "aa3fa01b-97d0-443e-b0cc-55d277878cb7",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"# Handle multiple tools\n",
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"\n",
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"def handle_tool_call(tool_call, user_message):\n",
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" function_name = None\n",
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" arguments = None\n",
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" tool_call_id = None\n",
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" \n",
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" # Logic for different model tool call object formats\n",
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" if isinstance(tool_call, dict) and 'function' in tool_call: # Ollama\n",
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" function_name = tool_call['function']['name']\n",
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" try:\n",
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" arguments = json.loads(tool_call['function']['arguments'])\n",
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" except (json.JSONDecodeError, TypeError):\n",
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" arguments = {'text': tool_call['function'].get('arguments', user_message)}\n",
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" elif hasattr(tool_call, 'function'): # GPT, Claude\n",
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" function_name = tool_call.function.name\n",
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" tool_call_id = getattr(tool_call, 'id', None)\n",
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" if isinstance(tool_call.function.arguments, dict):\n",
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" arguments = tool_call.function.arguments\n",
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" else:\n",
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" try:\n",
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" arguments = json.loads(tool_call.function.arguments)\n",
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" except (json.JSONDecodeError, TypeError):\n",
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" arguments = {'text': tool_call.function.arguments}\n",
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" elif hasattr(tool_call, 'name'): # Gemini\n",
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" function_name = tool_call.name\n",
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" arguments = tool_call.args\n",
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"\n",
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" # Fallback if arguments are not parsed correctly\n",
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" if not arguments or 'text' not in arguments:\n",
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" arguments = {'text': user_message}\n",
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" \n",
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" if function_name == \"scrape_urls\":\n",
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" url_scraped_data = scrape_urls(arguments['text'])\n",
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" response_content = json.dumps({\"url_scraped_data\": url_scraped_data})\n",
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" else:\n",
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" response_content = json.dumps({\"error\": f\"Unknown tool: {function_name}\"})\n",
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"\n",
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" response = {\n",
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" \"role\": \"tool\",\n",
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" \"content\": response_content,\n",
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" \"tool_call_id\": tool_call_id\n",
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" }\n",
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" return response\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": "14083620-1b16-4c8b-8365-c221b831e678",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Audio output\n",
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"\n",
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"def talker(message):\n",
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" response = openai.audio.speech.create(\n",
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" model=\"tts-1\",\n",
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" voice=\"onyx\",\n",
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" input=message\n",
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" )\n",
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" \n",
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" audio_stream = BytesIO(response.content)\n",
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" audio = AudioSegment.from_file(audio_stream, format=\"mp3\")\n",
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" play(audio)\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": "f9601a49-a490-4454-bd47-591ad793dc30",
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"metadata": {},
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"outputs": [],
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"source": [
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"# To transcribe an audio prompt/input to text\n",
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"\n",
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"def transcribe_audio(audio_file):\n",
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" if audio_file is None:\n",
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" return \"\"\n",
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" \n",
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" with tempfile.NamedTemporaryFile(suffix=\".wav\", delete=True) as tmpfile:\n",
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" audio = AudioSegment.from_file(audio_file, format=\"wav\")\n",
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" audio.export(tmpfile.name, format=\"wav\")\n",
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" \n",
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" with open(tmpfile.name, \"rb\") as audio_file_obj:\n",
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" transcript = openai.audio.transcriptions.create(\n",
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" model=\"whisper-1\", \n",
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" file=audio_file_obj\n",
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" )\n",
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" return transcript.text\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": "70c79408-f5f4-424b-b96c-d07e6893af6a",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"# More involved Gradio code as we're not using the preset Chat interface!\n",
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"# Passing in inbrowser=True in the last line will cause a Gradio window to pop up immediately.\n",
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"\n",
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"with gr.Blocks() as ui:\n",
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" with gr.Row():\n",
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" chatbot = gr.Chatbot(height=500)\n",
|
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" with gr.Row():\n",
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" entry = gr.Textbox(label=\"Chat with our AI Assistant:\", scale=4)\n",
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" submit_btn = gr.Button(\"Submit\", scale=1)\n",
|
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" with gr.Row():\n",
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" audio_input = gr.Audio(sources=[\"microphone\"], type=\"filepath\", label=\"Speak to our AI Assistant\", scale=4)\n",
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" submit_audio_btn = gr.Button(\"Submit Audio\", scale=1)\n",
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"\n",
|
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" with gr.Row():\n",
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" models = [\"Claude\", \"Gemini\", \"GPT\", \"Ollama\"]\n",
|
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" model_dropdown = gr.Dropdown(\n",
|
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" label=\"Select a model\",\n",
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" choices=models,\n",
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" value=models[2]\n",
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" )\n",
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"\n",
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" audio_options = [\"Yes\", \"No\"]\n",
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" audio_dropdown = gr.Dropdown(\n",
|
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" label=\"Select whether to respond with audio\",\n",
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" choices=audio_options,\n",
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" value=audio_options[1]\n",
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" )\n",
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" \n",
|
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" with gr.Row():\n",
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" clear = gr.Button(\"Clear\")\n",
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"\n",
|
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" def user_message_updater(user_message, history):\n",
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" return \"\", history + [[user_message, None]]\n",
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"\n",
|
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" def chat_with_assistant(history, target_model, use_audio_output):\n",
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" messages = []\n",
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" for msg_user, msg_assistant in history:\n",
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" messages.append({\"role\": \"user\", \"content\": msg_user})\n",
|
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" if msg_assistant:\n",
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" messages.append({\"role\": \"assistant\", \"content\": msg_assistant})\n",
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" \n",
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" user_message = history[-1][0]\n",
|
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" final_response_content = \"\"\n",
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" \n",
|
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" if target_model == \"Claude\":\n",
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" response = claude.messages.create(\n",
|
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" model=MODEL_CLAUDE,\n",
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" max_tokens=200,\n",
|
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" temperature=0.7,\n",
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" system=system_prompt_with_url_data,\n",
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" messages=messages,\n",
|
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" tools=tools_claude,\n",
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" )\n",
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" \n",
|
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" tool_calls = [content_block for content_block in response.content if content_block.type == \"tool_use\"]\n",
|
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" if tool_calls:\n",
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" tool_use = tool_calls[0]\n",
|
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" tool_output_content = scrape_urls(tool_use.input[\"text\"])\n",
|
|
" \n",
|
|
" messages.append({\"role\": \"assistant\", \"content\": response.content})\n",
|
|
" messages.append({\n",
|
|
" \"role\": \"user\",\n",
|
|
" \"content\": [\n",
|
|
" {\n",
|
|
" \"type\": \"tool_result\",\n",
|
|
" \"tool_use_id\": tool_use.id,\n",
|
|
" \"content\": tool_output_content\n",
|
|
" }\n",
|
|
" ]\n",
|
|
" })\n",
|
|
"\n",
|
|
" response = claude.messages.create(\n",
|
|
" model=MODEL_CLAUDE,\n",
|
|
" max_tokens=200,\n",
|
|
" temperature=0.7,\n",
|
|
" system=system_prompt_with_url_data,\n",
|
|
" messages=messages,\n",
|
|
" )\n",
|
|
" final_response_content = response.content[0].text\n",
|
|
"\n",
|
|
" elif target_model == \"Gemini\":\n",
|
|
" messages_gemini = []\n",
|
|
" for m in history:\n",
|
|
" messages_gemini.append({\"role\": \"user\", \"parts\": [{\"text\": m[0]}]})\n",
|
|
" if m[1]:\n",
|
|
" messages_gemini.append({\"role\": \"model\", \"parts\": [{\"text\": m[1]}]})\n",
|
|
" \n",
|
|
" model = google.generativeai.GenerativeModel(\n",
|
|
" model_name=MODEL_GEMINI,\n",
|
|
" system_instruction=system_message,\n",
|
|
" tools=tools_gemini\n",
|
|
" )\n",
|
|
" \n",
|
|
" chat = model.start_chat(history=messages_gemini[:-1])\n",
|
|
" response = chat.send_message(messages_gemini[-1])\n",
|
|
"\n",
|
|
" # Check if the response is a tool call before trying to extract text\n",
|
|
" if response.candidates[0].content.parts[0].function_call:\n",
|
|
" tool_call = response.candidates[0].content.parts[0].function_call\n",
|
|
" response_tool = handle_tool_call(tool_call, user_message)\n",
|
|
"\n",
|
|
" tool_response_content = json.loads(response_tool[\"content\"])\n",
|
|
" tool_response_gemini = {\n",
|
|
" \"role\": \"tool\",\n",
|
|
" \"parts\": [{\n",
|
|
" \"function_response\": {\n",
|
|
" \"name\": tool_call.name,\n",
|
|
" \"response\": tool_response_content\n",
|
|
" }\n",
|
|
" }]\n",
|
|
" }\n",
|
|
" \n",
|
|
" # Send the tool output back and get a new response\n",
|
|
" response = chat.send_message(tool_response_gemini)\n",
|
|
" final_response_content = response.text\n",
|
|
" else:\n",
|
|
" # If the original response was not a tool call, get the text directly\n",
|
|
" final_response_content = response.text\n",
|
|
"\n",
|
|
" elif target_model == \"Ollama\":\n",
|
|
" messages_ollama = [{\"role\": \"system\", \"content\": system_message}] + messages\n",
|
|
" response = ollama.chat(\n",
|
|
" model=MODEL_LLAMA,\n",
|
|
" messages=messages_ollama,\n",
|
|
" stream=False,\n",
|
|
" tools=tools_gpt_ollama,\n",
|
|
" )\n",
|
|
"\n",
|
|
" if 'tool_calls' in response['message'] and response['message']['tool_calls']:\n",
|
|
" response_tool = handle_tool_call(response['message']['tool_calls'][0], user_message)\n",
|
|
" messages_ollama.append({\"role\": \"assistant\", \"content\": response['message']['content'], \"tool_calls\": response['message']['tool_calls']})\n",
|
|
" messages_ollama.append(response_tool)\n",
|
|
" \n",
|
|
" response = ollama.chat(\n",
|
|
" model=MODEL_LLAMA,\n",
|
|
" messages=messages_ollama,\n",
|
|
" stream=False,\n",
|
|
" )\n",
|
|
" final_response_content = response['message']['content']\n",
|
|
" \n",
|
|
" else: # Assuming GPT is default\n",
|
|
" messages_gpt = [{\"role\": \"system\", \"content\": system_message}] + messages\n",
|
|
" response_stream = openai.chat.completions.create(model=MODEL_GPT, messages=messages_gpt, stream=True, tools=tools_gpt_ollama)\n",
|
|
" final_response_content = \"\"\n",
|
|
" for chunk in response_stream:\n",
|
|
" content = chunk.choices[0].delta.content or \"\"\n",
|
|
" tool_calls_chunk = chunk.choices[0].delta.tool_calls\n",
|
|
" if content:\n",
|
|
" final_response_content += content\n",
|
|
" \n",
|
|
" if tool_calls_chunk:\n",
|
|
" tool_call = tool_calls_chunk[0]\n",
|
|
" response_tool = handle_tool_call(tool_call, user_message)\n",
|
|
" \n",
|
|
" messages_gpt.append({\"role\": \"assistant\", \"tool_calls\": [tool_call]})\n",
|
|
" messages_gpt.append(response_tool)\n",
|
|
" \n",
|
|
" response_stream_after_tool = openai.chat.completions.create(model=MODEL_GPT, messages=messages_gpt, stream=True)\n",
|
|
" for chunk_after_tool in response_stream_after_tool:\n",
|
|
" final_response_content += chunk_after_tool.choices[0].delta.content or \"\"\n",
|
|
" break\n",
|
|
"\n",
|
|
" history[-1][1] = final_response_content\n",
|
|
" \n",
|
|
" if use_audio_output != \"No\":\n",
|
|
" talker(final_response_content)\n",
|
|
"\n",
|
|
" return history\n",
|
|
"\n",
|
|
" def transcribe_and_chat(audio_file, history, target_model, use_audio_output):\n",
|
|
" if audio_file:\n",
|
|
" transcribed_text = transcribe_audio(audio_file)\n",
|
|
" new_history = history + [[transcribed_text, None]]\n",
|
|
" return chat_with_assistant(new_history, target_model, use_audio_output)\n",
|
|
" else:\n",
|
|
" return history\n",
|
|
"\n",
|
|
" entry.submit(\n",
|
|
" user_message_updater,\n",
|
|
" inputs=[entry, chatbot],\n",
|
|
" outputs=[entry, chatbot],\n",
|
|
" queue=False\n",
|
|
" ).then(\n",
|
|
" chat_with_assistant,\n",
|
|
" inputs=[chatbot, model_dropdown, audio_dropdown],\n",
|
|
" outputs=[chatbot]\n",
|
|
" )\n",
|
|
"\n",
|
|
" submit_btn.click(\n",
|
|
" user_message_updater,\n",
|
|
" inputs=[entry, chatbot],\n",
|
|
" outputs=[entry, chatbot],\n",
|
|
" queue=False\n",
|
|
" ).then(\n",
|
|
" chat_with_assistant,\n",
|
|
" inputs=[chatbot, model_dropdown, audio_dropdown],\n",
|
|
" outputs=[chatbot]\n",
|
|
" )\n",
|
|
"\n",
|
|
" audio_input.stop(\n",
|
|
" transcribe_and_chat,\n",
|
|
" inputs=[audio_input, chatbot, model_dropdown, audio_dropdown],\n",
|
|
" outputs=[chatbot],\n",
|
|
" queue=False\n",
|
|
" )\n",
|
|
"\n",
|
|
" submit_audio_btn.click(\n",
|
|
" transcribe_and_chat,\n",
|
|
" inputs=[audio_input, chatbot, model_dropdown, audio_dropdown],\n",
|
|
" outputs=[chatbot],\n",
|
|
" queue=False\n",
|
|
" )\n",
|
|
" \n",
|
|
" clear.click(lambda: None, inputs=None, outputs=[chatbot], queue=False)\n",
|
|
"\n",
|
|
"ui.launch(inbrowser=True)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "eb23b6cb-27af-43d6-8234-fe8295e7fe57",
|
|
"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": "3.11.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|