305 lines
9.7 KiB
Plaintext
305 lines
9.7 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": "markdown",
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"id": "b6197b62-57fb-4569-a82d-bd4a15f4471b",
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"metadata": {},
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"source": [
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"# Imports"
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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": "b538792e-2414-4922-bb51-95c61f6c2128",
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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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"import json\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 IPython.display import Markdown, display, update_display, Audio\n",
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"from openai import OpenAI\n",
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"import anthropic\n",
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"import base64\n",
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"from io import BytesIO\n",
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"from PIL import Image\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": "markdown",
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"id": "95afec00-4fe5-4cc9-ae0a-37c6ea4934d6",
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"metadata": {},
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"source": [
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"# OpenAI 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": null,
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"id": "6c23ef55-daad-4ecf-893e-b83e507f2732",
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"metadata": {},
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"outputs": [],
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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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"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
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"\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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]
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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": "e260a7ea-fe79-4222-b6d7-7982f55a52d7",
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"metadata": {},
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"outputs": [],
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"source": [
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"openai = OpenAI()\n",
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"\n",
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"claude = anthropic.Anthropic()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "270e937e-aef9-4032-b51e-ffa0fec184a1",
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"metadata": {},
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"source": [
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"# SYSTEM prompt"
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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": "5639ebd6-d197-43c3-a1c4-4308b117e5ef",
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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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"def system_prompt(selected_model):\n",
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" return f\"\"\"\n",
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" You are a tech expert and know every coding language, and can give \n",
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" nice, detailed and simple explanations for the given questions.\n",
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" Introduce yourself by saying which model you are every time you answer. For example, this is {selected_model}. \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": null,
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"id": "d1d960d3-db2e-4f7c-ab7f-d5816f3bd994",
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"metadata": {},
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"outputs": [],
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"source": [
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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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" audio_stream = BytesIO(response.content)\n",
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" output_filename = \"output_audio.mp3\"\n",
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" with open(output_filename, \"wb\") as f:\n",
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" f.write(audio_stream.read())\n",
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"\n",
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" display(Audio(output_filename, autoplay=True))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f39f1bc7-87c2-4b44-8ba7-c320acb33361",
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"metadata": {},
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"outputs": [],
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"source": [
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"def listener(audio_file):\n",
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" with open(audio_file, \"rb\") as audio:\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\n",
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" )\n",
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" return transcript.text"
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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": "f88078a9-49ac-48b1-9f07-5b3977e88a52",
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"metadata": {},
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"outputs": [],
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"source": [
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"def chat(cleared_entry, history, selected_model):\n",
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" messages = [{\"role\": \"system\", \"content\": system_prompt(selected_model)}] + history\n",
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"\n",
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" print(messages)\n",
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" \n",
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" if selected_model == \"GPT-4o-mini\":\n",
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" stream = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages, stream=True)\n",
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" response = \"\"\n",
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" \n",
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" for chunk in stream:\n",
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" try:\n",
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" response += chunk.choices[0].delta.content or ''\n",
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" \n",
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" updated_history = history + [{\"role\": \"assistant\", \"content\": response}]\n",
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" # talker(response)\n",
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" yield updated_history, None \n",
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" except Exception as e:\n",
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" print(f\"Streaming error: {e}\")\n",
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" yield \"Sorry, there was an error processing your request.\"\n",
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" # talker(response)\n",
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" elif selected_model == \"Claude-sonnet-4\":\n",
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" claude_messages = [{\"role\": msg[\"role\"], \"content\": msg[\"content\"]} for msg in history]\n",
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" print(claude_messages)\n",
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" result = claude.messages.stream(\n",
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" model=\"claude-sonnet-4-20250514\",\n",
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" max_tokens=200,\n",
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" temperature=0.7,\n",
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" system=system_prompt(selected_model), \n",
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" messages=claude_messages, \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\n",
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" \n",
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" updated_history = history + [{\"role\": \"assistant\", \"content\": response}]\n",
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" \n",
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" yield updated_history, None \n",
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" # talker(response)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d1863d62-a2e8-454b-9296-710c4e5f9396",
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"metadata": {},
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"outputs": [],
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"source": [
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"with gr.Blocks() as ui:\n",
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"\n",
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" gr.Markdown(\"## AI Chat Assistant\")\n",
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" gr.Markdown(\"**Select your preferred AI model:**\")\n",
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" \n",
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" model_dropdown = gr.Dropdown(\n",
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" choices=[\"GPT-4o-mini\", \"Claude-sonnet-4\"], \n",
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" value=\"GPT-4o-mini\", # default selection\n",
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" label=\"Choose Model\"\n",
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" )\n",
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"\n",
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" \n",
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" with gr.Row():\n",
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" chatbot = gr.Chatbot(height=200, type=\"messages\")\n",
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" image_output = gr.Image(height=200)\n",
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" with gr.Row():\n",
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" entry = gr.Textbox(label=\"Chat with our AI Assistant:\")\n",
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" with gr.Row():\n",
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" # Audio input for voice messages\n",
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" audio_input = gr.Audio(\n",
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" sources=[\"microphone\", \"upload\"], \n",
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" type=\"filepath\", \n",
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" label=\"🎙️ Voice Message\"\n",
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" )\n",
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" with gr.Row():\n",
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" voice_submit = gr.Button(\"Send Voice Message\", variant=\"secondary\")\n",
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" clear = gr.Button(\"Clear\")\n",
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"\n",
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"\n",
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" def do_entry(message, history):\n",
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" history += [{\"role\":\"user\", \"content\":message}]\n",
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" return \"\", history\n",
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"\n",
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" def process_voice_input(audio_file):\n",
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" \"\"\"Convert voice to text and put it in the text box\"\"\"\n",
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" if audio_file is not None:\n",
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" transcribed_text = listener(audio_file)\n",
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" if transcribed_text and not transcribed_text.startswith(\"Error\"):\n",
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" return transcribed_text\n",
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" return \"\"\n",
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"\n",
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" entry.submit(do_entry, inputs=[entry, chatbot], outputs=[entry, chatbot]).then(\n",
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" chat, inputs=[entry,chatbot, model_dropdown], outputs=[chatbot, image_output]\n",
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" )\n",
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"\n",
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" voice_submit.click(\n",
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" process_voice_input,\n",
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" inputs=[audio_input],\n",
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" outputs=[entry]\n",
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" )\n",
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" \n",
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" clear.click(lambda: None, inputs=None, outputs=chatbot, queue=False)\n",
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"\n",
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"ui.launch(inbrowser=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "46085f59-8945-4a64-9db1-d10cd44284db",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.13"
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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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