import base64 import cv2 import glob import json import math import os import pytz import random import re import requests import streamlit as st import streamlit.components.v1 as components import textract import time import zipfile import uuid import platform import extra_streamlit_components as stx from audio_recorder_streamlit import audio_recorder from bs4 import BeautifulSoup from collections import deque from datetime import datetime from dotenv import load_dotenv from gradio_client import Client from huggingface_hub import InferenceClient from io import BytesIO from moviepy.editor import VideoFileClip from PIL import Image from PyPDF2 import PdfReader from templates import bot_template, css, user_template from urllib.parse import quote from xml.etree import ElementTree as ET import openai from openai import OpenAI # Load environment variables load_dotenv() # Configuration Site_Name = 'Scholarly-Article-Document-Search-With-Memory' title = "🔬🧠ScienceBrain.AI" helpURL = 'https://huggingface.co/awacke1' bugURL = 'https://huggingface.co/spaces/awacke1' icons = '🔬' st.set_page_config( page_title=title, page_icon=icons, layout="wide", initial_sidebar_state="auto", menu_items={ 'Get Help': helpURL, 'Report a bug': bugURL, 'About': title } ) # Initialize cookie manager cookie_manager = stx.CookieManager() # File to store chat history and user data CHAT_FILE = "chat_history.txt" # API configurations API_URL = 'https://qe55p8afio98s0u3.us-east-1.aws.endpoints.huggingface.cloud' API_KEY = st.secrets['API_KEY'] MODEL1 = "meta-llama/Llama-2-7b-chat-hf" MODEL1URL = "https://huggingface.co/meta-llama/Llama-2-7b-chat-hf" HF_KEY = st.secrets['HF_KEY'] headers = { "Authorization": f"Bearer {HF_KEY}", "Content-Type": "application/json" } # OpenAI client setup client = OpenAI(api_key=st.secrets['OPENAI_API_KEY'], organization=st.secrets['OPENAI_ORG_ID']) MODEL = "gpt-4-1106-preview" # Session state initialization if "openai_model" not in st.session_state: st.session_state["openai_model"] = MODEL 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() # Sidebar configurations should_save = st.sidebar.checkbox("💾 Save", value=True, help="Save your session data.") if st.sidebar.button("Clear Session"): st.session_state.messages = [] # 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 if 'data_loaded' not in st.session_state: load_data() st.session_state.data_loaded = True # 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 # HTML5 based Speech Synthesis (Text to Speech in Browser) @st.cache_resource def SpeechSynthesis(result): documentHTML5 = f""" Read It Aloud

🔊 Read It Aloud

""" components.html(documentHTML5, width=1280, height=300) # Function to generate filename 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 == "_")[:240] return f"{safe_date_time}_{safe_prompt}.{file_type}" # Function to process text def process_text(text_input): if text_input: st.session_state.messages.append({"role": "user", "content": text_input}) with st.chat_message("user"): st.markdown(text_input) with st.chat_message("assistant"): message_placeholder = st.empty() full_response = "" for response in client.chat.completions.create( model=st.session_state["openai_model"], messages=[ {"role": m["role"], "content": m["content"]} for m in st.session_state.messages ], stream=True, ): full_response += (response.choices[0].delta.content or "") message_placeholder.markdown(full_response + "▌") message_placeholder.markdown(full_response) st.session_state.messages.append({"role": "assistant", "content": full_response}) filename = generate_filename(text_input, "md") create_file(filename, text_input, full_response, should_save) return full_response # Function to create file def create_file(filename, prompt, response, should_save=True): if should_save: with open(filename, "w", encoding="utf-8") as f: f.write(prompt + "\n\n" + response) # ArXiv search function def search_arxiv(query): base_url = "http://export.arxiv.org/api/query?" search_query = f"search_query=all:{quote(query)}&start=0&max_results=5" response = requests.get(base_url + search_query) if response.status_code == 200: results = [] for entry in response.text.split('')[1:]: title = entry.split('')[1].split('')[0] summary = entry.split('

')[1].split('

')[0] link = entry.split('')[1].split('')[0] results.append(f"Title: {title}\nSummary: {summary}\nLink: {link}\n") return "\n".join(results) else: return "Error fetching results from ArXiv." # Sidebar for user information and settings 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}") st.rerun() st.title("Active Users") for user in st.session_state.users: st.write(f"{user['name']} ({user['browser']})") # Main chat area st.title("Personalized Real-Time Chat with ArXiv Search and AI") # Display chat messages for message in st.session_state.messages: with st.chat_message(message["role"]): st.markdown(message["content"]) # Chat input if prompt := st.chat_input("What would you like to know?"): st.session_state.messages.append({"role": "user", "content": prompt}) with st.chat_message("user"): st.markdown(prompt) # Check if it's an ArXiv search query if prompt.lower().startswith("arxiv:"): query = prompt[6:].strip() with st.chat_message("assistant"): with st.spinner("Searching ArXiv..."): search_results = search_arxiv(query) st.markdown(f"Search results for '{query}':\n\n{search_results}") # Get AI commentary on the search results ai_commentary = process_text(f"Provide a brief analysis of these ArXiv search results: {search_results}") st.markdown(f"\nAI Analysis:\n{ai_commentary}") st.session_state.messages.append({"role": "assistant", "content": f"Search results for '{query}':\n\n{search_results}\n\nAI Analysis:\n{ai_commentary}"}) else: # Regular chat processing with st.chat_message("assistant"): message_placeholder = st.empty() full_response = "" for response in client.chat.completions.create( model=st.session_state["openai_model"], messages=[ {"role": m["role"], "content": m["content"]} for m in st.session_state.messages ], stream=True, ): full_response += (response.choices[0].delta.content or "") message_placeholder.markdown(full_response + "▌") message_placeholder.markdown(full_response) st.session_state.messages.append({"role": "assistant", "content": full_response}) save_data() st.rerun() # Polling for updates if st.button("Refresh Chat"): st.rerun() # Auto-refresh if 'last_refresh' not in st.session_state: st.session_state.last_refresh = time.time() if time.time() - st.session_state.last_refresh > 5: # Refresh every 5 seconds st.session_state.last_refresh = time.time() st.rerun() # Main function to handle different input types def main(): st.markdown("##### GPT-4 Multimodal AI Assistant: Text, Audio, Image, & Video") option = st.selectbox("Select an option", ("Text", "Image", "Audio", "Video")) if option == "Text": text_input = st.text_input("Enter your text:") if text_input: process_text(text_input) elif option == "Image": text = "Help me understand what is in this picture and list ten facts as markdown outline with appropriate emojis that describes what you see." text_input = st.text_input(label="Enter text prompt to use with Image context.", value=text) image_input = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"]) if image_input: image = Image.open(image_input) st.image(image, caption="Uploaded Image", use_column_width=True) if st.button("Analyze Image"): with st.spinner("Analyzing..."): image_byte_arr = BytesIO() image.save(image_byte_arr, format='PNG') image_byte_arr = image_byte_arr.getvalue() response = client.chat.completions.create( model="gpt-4-vision-preview", messages=[ { "role": "user", "content": [ {"type": "text", "text": text_input}, { "type": "image_url", "image_url": { "url": f"data:image/jpeg;base64,{base64.b64encode(image_byte_arr).decode()}" } }, ], } ], max_tokens=300, ) st.write(response.choices[0].message.content) elif option == "Audio": text = "You are generating a transcript summary. Create a summary of the provided transcription. Respond in Markdown." text_input = st.text_input(label="Enter text prompt to use with Audio context.", value=text) audio_file = st.file_uploader("Upload an audio file", type=["mp3", "wav"]) if audio_file: if st.button("Transcribe Audio"): with st.spinner("Transcribing..."): transcription = client.audio.transcriptions.create( model="whisper-1", file=audio_file ) st.write(transcription.text) st.session_state.messages.append({"role": "user", "content": f"Transcription: {transcription.text}"}) process_text(f"{text}\n\nTranscription: {transcription.text}")