285 lines
8.1 KiB
Plaintext
285 lines
8.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "5e6b6966-8689-4e2c-8607-a1c5d948296c",
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"metadata": {},
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"source": [
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"### With this interface you can ask a question and get an answer from the GPT, Claude and Gemini"
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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": 49,
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"id": "c44c5494-950d-4d2f-8d4f-b87b57c5b330",
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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 requests\n",
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"from bs4 import BeautifulSoup\n",
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"from typing import List\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 google.generativeai\n",
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"import anthropic\n",
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"import time"
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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": "d1715421-cead-400b-99af-986388a97aff",
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"metadata": {},
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"outputs": [],
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"source": [
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"import gradio as gr # oh yeah!"
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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": "337d5dfc-0181-4e3b-8ab9-e78e0c3f657b",
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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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"Anthropic API Key exists and begins sk-ant-\n",
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"Google API Key exists and begins AIzaSyAJ\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()\n",
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"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
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"anthropic_api_key = os.getenv('ANTHROPIC_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 anthropic_api_key:\n",
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" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\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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"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": 4,
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"id": "22586021-1795-4929-8079-63f5bb4edd4c",
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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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"\n",
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"claude = anthropic.Anthropic()\n",
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"\n",
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"google.generativeai.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": 5,
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"id": "b16e6021-6dc4-4397-985a-6679d6c8ffd5",
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"metadata": {},
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"outputs": [],
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"source": [
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"# A generic system message - no more snarky adversarial AIs!\n",
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"\n",
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"system_message = \"You are a helpful assistant\""
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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": "88c04ebf-0671-4fea-95c9-bc1565d4bb4f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Let's create a call that streams back results\n",
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"# If you'd like a refresher on Generators (the \"yield\" keyword),\n",
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"# Please take a look at the Intermediate Python notebook in week1 folder.\n",
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"\n",
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"def stream_gpt(prompt):\n",
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" messages = [\n",
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" {\"role\": \"system\", \"content\": system_message},\n",
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" {\"role\": \"user\", \"content\": prompt}\n",
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" ]\n",
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" stream = openai.chat.completions.create(\n",
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" model='gpt-4o-mini',\n",
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" messages=messages,\n",
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" stream=True\n",
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" )\n",
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" result = \"\"\n",
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" for chunk in stream:\n",
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" result += chunk.choices[0].delta.content or \"\"\n",
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" yield result"
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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": "bbc8e930-ba2a-4194-8f7c-044659150626",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stream_claude(prompt):\n",
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" result = claude.messages.stream(\n",
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" model=\"claude-3-haiku-20240307\",\n",
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" max_tokens=1000,\n",
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" temperature=0.7,\n",
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" system=system_message,\n",
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" messages=[\n",
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" {\"role\": \"user\", \"content\": prompt},\n",
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" ],\n",
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" )\n",
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" response = \"\"\n",
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" with result as stream:\n",
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" for text in stream.text_stream:\n",
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" response += text or \"\"\n",
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" yield 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": 8,
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"id": "5e228aff-16d5-4141-bd04-ed9940ef7b3b",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stream_gemini(prompt):\n",
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" gemini = google.generativeai.GenerativeModel(\n",
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" model_name='gemini-2.0-flash-exp',\n",
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" system_instruction=system_message\n",
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" )\n",
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" result = \"\"\n",
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" for response in gemini.generate_content(prompt, stream=True):\n",
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" result += response.text or \"\"\n",
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" yield result"
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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": 92,
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"id": "db99aaf1-fe0a-4e79-9057-8599d1ca0149",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stream_models(prompt):\n",
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" response_gpt = \"\"\n",
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" response_claude = \"\"\n",
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" response_gemini = \"\"\n",
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" for gpt in stream_gpt(prompt):\n",
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" response_gpt = gpt\n",
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" yield response_gpt, response_claude, response_gemini\n",
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" for claude in stream_claude(prompt):\n",
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" response_claude = claude\n",
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" yield response_gpt, response_claude, response_gemini\n",
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" for gemini in stream_gemini(prompt):\n",
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" response_gemini = gemini\n",
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" yield response_gpt, response_claude, response_gemini"
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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": 113,
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"id": "3377f2fb-55f8-45cb-b713-d99d44748dad",
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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://127.0.0.1:7919\n",
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"\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://127.0.0.1:7919/\" 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": 113,
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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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"# Gradio interface\n",
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"with gr.Blocks() as view:\n",
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" user_input = gr.Textbox(label=\"What models can help with?\", placeholder=\"Type your question here\")\n",
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" ask_button = gr.Button(\"Ask\")\n",
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" with gr.Row():\n",
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" with gr.Column():\n",
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" gr.HTML(value=\"<b>GPT response:</b>\") \n",
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" gcp_stream = gr.Markdown()\n",
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" with gr.Column():\n",
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" gr.HTML(value=\"<b>Claude response:</b>\") \n",
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" claude_stream = gr.Markdown()\n",
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" with gr.Column():\n",
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" gr.HTML(value=\"<b>Gemine response:</b>\") \n",
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" gemini_stream = gr.Markdown()\n",
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"\n",
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" ask_button.click(\n",
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" fn=stream_models, # Function that yields multiple outputs\n",
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" inputs=user_input,\n",
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" outputs=[gcp_stream, claude_stream, gemini_stream] # Connect to multiple outputs\n",
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" )\n",
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"\n",
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"view.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": "3.11.11"
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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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