import streamlit as st import time import random import json from datetime import datetime import pytz import platform import uuid import extra_streamlit_components as stx from io import BytesIO from PIL import Image import base64 import cv2 import requests from moviepy.editor import VideoFileClip from gradio_client import Client from openai import OpenAI import openai import os from collections import deque import numpy as np from dotenv import load_dotenv # Load environment variables load_dotenv() # Set page config st.set_page_config(page_title="Personalized Real-Time Chat", page_icon="💬", layout="wide") # Initialize cookie manager cookie_manager = stx.CookieManager() # File to store chat history and user data CHAT_FILE = "chat_history.txt" # Function to save chat history and user data to file def save_data(): with open(CHAT_FILE, 'w') as f: json.dump({ 'messages': st.session_state.messages, 'users': st.session_state.users }, f) # Function to load chat history and user data from file def load_data(): try: with open(CHAT_FILE, 'r') as f: data = json.load(f) st.session_state.messages = data['messages'] st.session_state.users = data['users'] except FileNotFoundError: st.session_state.messages = [] st.session_state.users = [] # Load data at the start load_data() # Function to get or create user def get_or_create_user(): user_id = cookie_manager.get(cookie='user_id') if not user_id: user_id = str(uuid.uuid4()) cookie_manager.set('user_id', user_id) user = next((u for u in st.session_state.users if u['id'] == user_id), None) if not user: user = { 'id': user_id, 'name': random.choice(['Alice', 'Bob', 'Charlie', 'David', 'Eve', 'Frank', 'Grace', 'Henry']), 'browser': f"{platform.system()} - {st.session_state.get('browser_info', 'Unknown')}" } st.session_state.users.append(user) save_data() return user # Initialize session state if 'messages' not in st.session_state: st.session_state.messages = [] if 'users' not in st.session_state: st.session_state.users = [] if 'current_user' not in st.session_state: st.session_state.current_user = get_or_create_user() # Initialize OpenAI client openai.api_key = os.getenv('OPENAI_API_KEY') openai.organization = os.getenv('OPENAI_ORG_ID') client = OpenAI(api_key=openai.api_key, organization=openai.organization) GPT4O_MODEL = "gpt-4o-2024-05-13" # Initialize HuggingFace client hf_client = OpenAI( base_url="https://api-inference.huggingface.co/v1", api_key=os.environ.get('API_KEY') ) # Create supported models model_links = { "GPT-4o": GPT4O_MODEL, "Meta-Llama-3.1-70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct", "Meta-Llama-3.1-405B-Instruct-FP8": "meta-llama/Meta-Llama-3.1-405B-Instruct-FP8", "Meta-Llama-3.1-405B-Instruct": "meta-llama/Meta-Llama-3.1-405B-Instruct", "Meta-Llama-3.1-8B-Instruct": "meta-llama/Meta-Llama-3.1-8B-Instruct", "Meta-Llama-3-70B-Instruct": "meta-llama/Meta-Llama-3-70B-Instruct", "Meta-Llama-3-8B-Instruct": "meta-llama/Meta-Llama-3-8B-Instruct", "C4ai-command-r-plus": "CohereForAI/c4ai-command-r-plus", "Aya-23-35B": "CohereForAI/aya-23-35B", "Zephyr-orpo-141b-A35b-v0.1": "HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1", "Mixtral-8x7B-Instruct-v0.1": "mistralai/Mixtral-8x7B-Instruct-v0.1", "Codestral-22B-v0.1": "mistralai/Codestral-22B-v0.1", "Nous-Hermes-2-Mixtral-8x7B-DPO": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO", "Yi-1.5-34B-Chat": "01-ai/Yi-1.5-34B-Chat", "Gemma-2-27b-it": "google/gemma-2-27b-it", "Meta-Llama-2-70B-Chat-HF": "meta-llama/Llama-2-70b-chat-hf", "Meta-Llama-2-7B-Chat-HF": "meta-llama/Llama-2-7b-chat-hf", "Meta-Llama-2-13B-Chat-HF": "meta-llama/Llama-2-13b-chat-hf", "Mistral-7B-Instruct-v0.1": "mistralai/Mistral-7B-Instruct-v0.1", "Mistral-7B-Instruct-v0.2": "mistralai/Mistral-7B-Instruct-v0.2", "Mistral-7B-Instruct-v0.3": "mistralai/Mistral-7B-Instruct-v0.3", "Gemma-1.1-7b-it": "google/gemma-1.1-7b-it", "Gemma-1.1-2b-it": "google/gemma-1.1-2b-it", "Zephyr-7B-Beta": "HuggingFaceH4/zephyr-7b-beta", "Zephyr-7B-Alpha": "HuggingFaceH4/zephyr-7b-alpha", "Phi-3-mini-128k-instruct": "microsoft/Phi-3-mini-128k-instruct", "Phi-3-mini-4k-instruct": "microsoft/Phi-3-mini-4k-instruct", } # Function to reset conversation def reset_conversation(): st.session_state.conversation = [] st.session_state.messages = [] # Function to generate filenames def generate_filename(prompt, file_type): central = pytz.timezone('US/Central') safe_date_time = datetime.now(central).strftime("%m%d_%H%M") replaced_prompt = prompt.replace(" ", "_").replace("\n", "_") safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:90] return f"{safe_date_time}_{safe_prompt}.{file_type}" # Function to create files def create_file(filename, prompt, response, user_name, timestamp): with open(filename, "w", encoding="utf-8") as f: f.write(f"User: {user_name}\nTimestamp: {timestamp}\n\nPrompt:\n{prompt}\n\nResponse:\n{response}") # Function to extract video frames def extract_video_frames(video_path, seconds_per_frame=2): base64Frames = [] video = cv2.VideoCapture(video_path) total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) fps = video.get(cv2.CAP_PROP_FPS) frames_to_skip = int(fps * seconds_per_frame) curr_frame = 0 while curr_frame < total_frames - 1: video.set(cv2.CAP_PROP_POS_FRAMES, curr_frame) success, frame = video.read() if not success: break _, buffer = cv2.imencode(".jpg", frame) base64Frames.append(base64.b64encode(buffer).decode("utf-8")) curr_frame += frames_to_skip video.release() return base64Frames, None # Function to process audio for video def process_audio_for_video(video_input): #try: transcription = client.audio.transcriptions.create( model="whisper-1", file=video_input, ) return transcription.text #except: # return '' # Function to process text with selected model def process_text(user_name, text_input, selected_model, temp_values): timestamp = datetime.now(pytz.utc).strftime('%Y-%m-%d %H:%M:%S %Z') st.session_state.messages.append({"user": user_name, "message": text_input, "timestamp": timestamp}) with st.chat_message(user_name): st.markdown(f"{user_name} ({timestamp}): {text_input}") with st.chat_message("Assistant"): if selected_model == "GPT-4o": completion = client.chat.completions.create( model=GPT4O_MODEL, messages=[ {"role": "user", "content": m["message"]} for m in st.session_state.messages ], stream=True, temperature=temp_values ) return_text = st.write_stream(completion) else: #try: stream = hf_client.chat.completions.create( model=model_links[selected_model], messages=[ #{"role": m["role"], "content": m["content"]} {"role": "user", "content": m["message"]} for m in st.session_state.messages ], temperature=temp_values, stream=True, max_tokens=3000, ) return_text = st.write_stream(stream) #except Exception as e: # return_text = f"Error: {str(e)}" # st.error(return_text) st.markdown(f"Assistant ({timestamp}): {return_text}") filename = generate_filename(text_input, "md") create_file(filename, text_input, return_text, user_name, timestamp) st.session_state.messages.append({"user": "Assistant", "message": return_text, "timestamp": timestamp}) save_data() # Function to process image (using GPT-4o) def process_image(user_name, image_input, user_prompt): image = Image.open(BytesIO(image_input)) base64_image = base64.b64encode(image_input).decode("utf-8") response = client.chat.completions.create( model=GPT4O_MODEL, messages=[ {"role": "system", "content": "You are a helpful assistant that responds in Markdown."}, {"role": "user", "content": [ {"type": "text", "text": user_prompt}, {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{base64_image}"}} ]} ], temperature=0.0, ) image_response = response.choices[0].message.content timestamp = datetime.now(pytz.utc).strftime('%Y-%m-%d %H:%M:%S %Z') st.session_state.messages.append({"user": user_name, "message": image_response, "timestamp": timestamp}) with st.chat_message(user_name): st.image(image) st.markdown(f"{user_name} ({timestamp}): {user_prompt}") with st.chat_message("Assistant"): st.markdown(image_response) filename_md = generate_filename(user_prompt, "md") create_file(filename_md, user_prompt, image_response, user_name, timestamp) save_data() return image_response # Function to process audio (using GPT-4o for transcription) def process_audio(user_name, audio_input, text_input): if audio_input: transcription = client.audio.transcriptions.create( model="whisper-1", file=audio_input, ) timestamp = datetime.now(pytz.utc).strftime('%Y-%m-%d %H:%M:%S %Z') st.session_state.messages.append({"user": user_name, "message": transcription.text, "timestamp": timestamp}) with st.chat_message(user_name): st.markdown(f"{user_name} ({timestamp}): {transcription.text}") with st.chat_message("Assistant"): st.markdown(transcription.text) filename = generate_filename(transcription.text, "wav") create_file(filename, text_input, transcription.text, user_name, timestamp) st.session_state.messages.append({"user": "Assistant", "message": transcription.text, "timestamp": timestamp}) save_data() # Function to process video (using GPT-4o) def process_video(user_name, video_input, user_prompt): if isinstance(video_input, str): with open(video_input, "rb") as video_file: video_input = video_file.read() base64Frames, audio_path = extract_video_frames(video_input) transcript = process_audio_for_video(video_input) response = client.chat.completions.create( model=GPT4O_MODEL, messages=[ {"role": "system", "content": "You are generating a video summary. Create a summary of the provided video and its transcript. Respond in Markdown"}, {"role": "user", "content": [ "These are the frames from the video.", *map(lambda x: {"type": "image_url", "image_url": {"url": f'data:image/jpg;base64,{x}', "detail": "low"}}, base64Frames), {"type": "text", "text": f"The audio transcription is: {transcript}"}, {"type": "text", "text": user_prompt} ]} ], temperature=0, ) video_response = response.choices[0].message.content st.markdown(video_response) timestamp = datetime.now(pytz.utc).strftime('%Y-%m-%d %H:%M:%S %Z') filename_md = generate_filename(user_prompt, "md") create_file(filename_md, user_prompt, video_response, user_name, timestamp) st.session_state.messages.append({"user": user_name, "message": video_response, "timestamp": timestamp}) save_data() return video_response # Main function for each column def main_column(column_name): st.markdown(f"##### {column_name}") selected_model = st.selectbox(f"Select Model for {column_name}", list(model_links.keys()), key=f"{column_name}_model") temp_values = st.slider(f'Select a temperature value for {column_name}', 0.0, 1.0, (0.5), key=f"{column_name}_temp") option = st.selectbox(f"Select an option for {column_name}", ("Text", "Image", "Audio", "Video"), key=f"{column_name}_option") if option == "Text": text_input = st.text_input(f"Enter your text for {column_name}:", key=f"{column_name}_text") if text_input: process_text(st.session_state.current_user['name'], text_input, selected_model, temp_values) elif option == "Image": text_input = st.text_input(f"Enter text prompt to use with Image context for {column_name}:", key=f"{column_name}_image_text") uploaded_files = st.file_uploader(f"Upload images for {column_name}", type=["png", "jpg", "jpeg"], accept_multiple_files=True, key=f"{column_name}_image_upload") for image_input in uploaded_files: image_bytes = image_input.read() process_image(st.session_state.current_user['name'], image_bytes, text_input) elif option == "Audio": text_input = st.text_input(f"Enter text prompt to use with Audio context for {column_name}:", key=f"{column_name}_audio_text") uploaded_files = st.file_uploader(f"Upload an audio file for {column_name}", type=["mp3", "wav"], accept_multiple_files=True, key=f"{column_name}_audio_upload") for audio_input in uploaded_files: process_audio(st.session_state.current_user['name'], audio_input, text_input) elif option == "Video": video_input = st.file_uploader(f"Upload a video file for {column_name}", type=["mp4"], key=f"{column_name}_video_upload") text_input = st.text_input(f"Enter text prompt to use with Video context for {column_name}:", key=f"{column_name}_video_text") if video_input and text_input: process_video(st.session_state.current_user['name'], video_input, text_input) # Main Streamlit app st.title("Personalized Real-Time Chat") # Sidebar with st.sidebar: st.title("User Info") st.write(f"Current User: {st.session_state.current_user['name']}") st.write(f"Browser: {st.session_state.current_user['browser']}") new_name = st.text_input("Change your name:") if st.button("Update Name"): if new_name: for user in st.session_state.users: if user['id'] == st.session_state.current_user['id']: user['name'] = new_name st.session_state.current_user['name'] = new_name save_data() st.success(f"Name updated to {new_name}") break st.title("Active Users") for user in st.session_state.users: st.write(f"{user['name']} ({user['browser']})") if st.button('Reset Chat'): reset_conversation() # Create two columns col1, col2 = st.columns(2) # Run main function for each column with col1: main_column("Column 1") with col2: main_column("Column 2") # Run the Streamlit app if __name__ == "__main__": st.markdown("*by Aaron Wacker*") st.markdown("\n[Aaron Wacker](https://huggingface.co/spaces/awacke1/).")