import streamlit as st import streamlit.components.v1 as components import anthropic import openai import base64 from datetime import datetime import plotly.graph_objects as go import cv2 import glob import json import math import os import pytz import random import re import requests import textract import time import zipfile from audio_recorder_streamlit import audio_recorder from bs4 import BeautifulSoup from collections import deque from dotenv import load_dotenv from gradio_client import Client from huggingface_hub import InferenceClient from io import BytesIO from PIL import Image from PyPDF2 import PdfReader from urllib.parse import quote from xml.etree import ElementTree as ET from openai import OpenAI import extra_streamlit_components as stx from streamlit.runtime.scriptrunner import get_script_run_ctx import extra_streamlit_components as stx # 1. 🚲BikeAI🏆 Configuration and Setup Site_Name = '🚲BikeAI🏆 Claude and GPT Multi-Agent Research AI' title = "🚲BikeAI🏆 Claude and GPT Multi-Agent Research 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 } ) def get_cookie_manager(): """Create and return a cookie manager.""" return stx.CookieManager() def create_speech_component(): """Create speech recognition component with timer-based updates.""" speech_recognition_html = """ Continuous Speech Demo

Ready

""" # Return both the component value and height return components.html( speech_recognition_html, height=400, ) def integrate_speech_component(): """Integrate speech component with timer-based updates.""" if "voice_transcript" not in st.session_state: st.session_state.voice_transcript = "" # Create the component and get its value value = create_speech_component() # If we got a value from the component, update session state if value is not None and value != "": st.session_state.voice_transcript = value # Debug output st.write("Updated transcript:", value) return st.session_state.voice_transcript # 2. 🚲BikeAI🏆 Load environment variables and initialize clients load_dotenv() # OpenAI setup openai.api_key = os.getenv('OPENAI_API_KEY') if openai.api_key == None: openai.api_key = st.secrets['OPENAI_API_KEY'] openai_client = OpenAI( api_key=os.getenv('OPENAI_API_KEY'), organization=os.getenv('OPENAI_ORG_ID') ) # 3.🚲BikeAI🏆 Claude setup anthropic_key = os.getenv("ANTHROPIC_API_KEY_3") if anthropic_key == None: anthropic_key = st.secrets["ANTHROPIC_API_KEY"] claude_client = anthropic.Anthropic(api_key=anthropic_key) # 4.🚲BikeAI🏆 Initialize session states if 'transcript_history' not in st.session_state: st.session_state.transcript_history = [] if "chat_history" not in st.session_state: st.session_state.chat_history = [] if "openai_model" not in st.session_state: st.session_state["openai_model"] = "gpt-4o-2024-05-13" if "messages" not in st.session_state: st.session_state.messages = [] if 'last_voice_input' not in st.session_state: st.session_state.last_voice_input = "" # 5. 🚲BikeAI🏆 HuggingFace AI setup API_URL = os.getenv('API_URL') HF_KEY = os.getenv('HF_KEY') MODEL1 = "meta-llama/Llama-2-7b-chat-hf" MODEL2 = "openai/whisper-small.en" headers = { "Authorization": f"Bearer {HF_KEY}", "Content-Type": "application/json" } # 6. 🚲BikeAI🏆 Custom CSS st.markdown(""" """, unsafe_allow_html=True) # 7. Helper Functions def generate_filename(prompt, file_type): """Generate a safe filename using the prompt and file type.""" central = pytz.timezone('US/Central') safe_date_time = datetime.now(central).strftime("%m%d_%H%M") replaced_prompt = re.sub(r'[<>:"/\\|?*\n]', ' ', prompt) safe_prompt = re.sub(r'\s+', ' ', replaced_prompt).strip()[:230] return f"{safe_date_time}_{safe_prompt}.{file_type}" # 8. Function to create and save a file (and avoid the black hole of lost data 🕳) def create_file(filename, prompt, response, should_save=True): if not should_save: return with open(filename, 'w', encoding='utf-8') as file: file.write(prompt + "\n\n" + response) def create_and_save_file(content, file_type="md", prompt=None, is_image=False, should_save=True): """Create and save file with proper handling of different types.""" if not should_save: return None filename = generate_filename(prompt if prompt else content, file_type) with open(filename, "w", encoding="utf-8") as f: if is_image: f.write(content) else: f.write(prompt + "\n\n" + content if prompt else content) return filename def get_download_link(file_path): """Create download link for file.""" with open(file_path, "rb") as file: contents = file.read() b64 = base64.b64encode(contents).decode() return f'Download {os.path.basename(file_path)}📂' @st.cache_resource def SpeechSynthesis(result): """HTML5 Speech Synthesis.""" documentHTML5 = f''' Read It Aloud

🔊 Read It Aloud

''' components.html(documentHTML5, width=1280, height=300) # Media Processing Functions def process_image(image_input, user_prompt): """Process image with GPT-4o vision.""" if isinstance(image_input, str): with open(image_input, "rb") as image_file: image_input = image_file.read() base64_image = base64.b64encode(image_input).decode("utf-8") response = openai_client.chat.completions.create( model=st.session_state["openai_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, ) return response.choices[0].message.content def process_audio(audio_input, text_input=''): """Process audio with Whisper and GPT.""" if isinstance(audio_input, str): with open(audio_input, "rb") as file: audio_input = file.read() transcription = openai_client.audio.transcriptions.create( model="whisper-1", file=audio_input, ) st.session_state.messages.append({"role": "user", "content": transcription.text}) with st.chat_message("assistant"): st.markdown(transcription.text) SpeechSynthesis(transcription.text) filename = generate_filename(transcription.text, "wav") create_and_save_file(audio_input, "wav", transcription.text, True) # Modified video processing function without moviepy dependency def process_video(video_path, seconds_per_frame=1): """Process video files for frame extraction.""" 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) for frame_idx in range(0, total_frames, frames_to_skip): video.set(cv2.CAP_PROP_POS_FRAMES, frame_idx) success, frame = video.read() if not success: break _, buffer = cv2.imencode(".jpg", frame) base64Frames.append(base64.b64encode(buffer).decode("utf-8")) video.release() return base64Frames, None def process_video_with_gpt(video_input, user_prompt): """Process video with GPT-4 vision.""" base64Frames, _ = process_video(video_input) response = openai_client.chat.completions.create( model=st.session_state["openai_model"], messages=[ {"role": "system", "content": "Analyze the video frames and provide a detailed description."}, {"role": "user", "content": [ {"type": "text", "text": user_prompt}, *[{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{frame}"}} for frame in base64Frames] ]} ] ) return response.choices[0].message.content def extract_urls(text): try: date_pattern = re.compile(r'### (\d{2} \w{3} \d{4})') abs_link_pattern = re.compile(r'\[(.*?)\]\((https://arxiv\.org/abs/\d+\.\d+)\)') pdf_link_pattern = re.compile(r'\[⬇️\]\((https://arxiv\.org/pdf/\d+\.\d+)\)') title_pattern = re.compile(r'### \d{2} \w{3} \d{4} \| \[(.*?)\]') date_matches = date_pattern.findall(text) abs_link_matches = abs_link_pattern.findall(text) pdf_link_matches = pdf_link_pattern.findall(text) title_matches = title_pattern.findall(text) # markdown with the extracted fields markdown_text = "" for i in range(len(date_matches)): date = date_matches[i] title = title_matches[i] abs_link = abs_link_matches[i][1] pdf_link = pdf_link_matches[i] markdown_text += f"**Date:** {date}\n\n" markdown_text += f"**Title:** {title}\n\n" markdown_text += f"**Abstract Link:** [{abs_link}]({abs_link})\n\n" markdown_text += f"**PDF Link:** [{pdf_link}]({pdf_link})\n\n" markdown_text += "---\n\n" return markdown_text except: st.write('.') return '' def search_arxiv(query): st.write("Performing AI Lookup...") client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern") result1 = client.predict( prompt=query, llm_model_picked="mistralai/Mixtral-8x7B-Instruct-v0.1", stream_outputs=True, api_name="/ask_llm" ) st.markdown("### Mixtral-8x7B-Instruct-v0.1 Result") st.markdown(result1) result2 = client.predict( prompt=query, llm_model_picked="mistralai/Mistral-7B-Instruct-v0.2", stream_outputs=True, api_name="/ask_llm" ) st.markdown("### Mistral-7B-Instruct-v0.2 Result") st.markdown(result2) combined_result = f"{result1}\n\n{result2}" return combined_result #return responseall # Function to generate a filename based on prompt and time (because names matter 🕒) def generate_filename(prompt, file_type): central = pytz.timezone('US/Central') safe_date_time = datetime.now(central).strftime("%m%d_%H%M") safe_prompt = re.sub(r'\W+', '_', prompt)[:90] return f"{safe_date_time}_{safe_prompt}.{file_type}" # Function to create and save a file (and avoid the black hole of lost data 🕳) def create_file(filename, prompt, response): with open(filename, 'w', encoding='utf-8') as file: file.write(prompt + "\n\n" + response) def perform_ai_lookup(query): start_time = time.strftime("%Y-%m-%d %H:%M:%S") client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern") response1 = client.predict( query, 20, "Semantic Search", "mistralai/Mixtral-8x7B-Instruct-v0.1", api_name="/update_with_rag_md" ) Question = '### 🔎 ' + query + '\r\n' # Format for markdown display with links References = response1[0] ReferenceLinks = extract_urls(References) RunSecondQuery = True results='' if RunSecondQuery: # Search 2 - Retrieve the Summary with Papers Context and Original Query response2 = client.predict( query, "mistralai/Mixtral-8x7B-Instruct-v0.1", True, api_name="/ask_llm" ) if len(response2) > 10: Answer = response2 SpeechSynthesis(Answer) # Restructure results to follow format of Question, Answer, References, ReferenceLinks results = Question + '\r\n' + Answer + '\r\n' + References + '\r\n' + ReferenceLinks st.markdown(results) st.write('🔍Run of Multi-Agent System Paper Summary Spec is Complete') end_time = time.strftime("%Y-%m-%d %H:%M:%S") start_timestamp = time.mktime(time.strptime(start_time, "%Y-%m-%d %H:%M:%S")) end_timestamp = time.mktime(time.strptime(end_time, "%Y-%m-%d %H:%M:%S")) elapsed_seconds = end_timestamp - start_timestamp st.write(f"Start time: {start_time}") st.write(f"Finish time: {end_time}") st.write(f"Elapsed time: {elapsed_seconds:.2f} seconds") filename = generate_filename(query, "md") create_file(filename, query, results) return results # Chat Processing Functions def process_with_gpt(text_input): """Process text with GPT-4o.""" 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"): completion = openai_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=False ) return_text = completion.choices[0].message.content st.write("GPT-4o: " + return_text) #filename = generate_filename(text_input, "md") filename = generate_filename("GPT-4o: " + return_text, "md") create_file(filename, text_input, return_text) st.session_state.messages.append({"role": "assistant", "content": return_text}) return return_text def process_with_claude(text_input): """Process text with Claude.""" if text_input: with st.chat_message("user"): st.markdown(text_input) with st.chat_message("assistant"): response = claude_client.messages.create( model="claude-3-sonnet-20240229", max_tokens=1000, messages=[ {"role": "user", "content": text_input} ] ) response_text = response.content[0].text st.write("Claude: " + response_text) #filename = generate_filename(text_input, "md") filename = generate_filename("Claude: " + response_text, "md") create_file(filename, text_input, response_text) st.session_state.chat_history.append({ "user": text_input, "claude": response_text }) return response_text # File Management Functions def load_file(file_name): """Load file content.""" with open(file_name, "r", encoding='utf-8') as file: content = file.read() return content def create_zip_of_files(files): """Create zip archive of files.""" zip_name = "all_files.zip" with zipfile.ZipFile(zip_name, 'w') as zipf: for file in files: zipf.write(file) return zip_name def get_media_html(media_path, media_type="video", width="100%"): """Generate HTML for media player.""" media_data = base64.b64encode(open(media_path, 'rb').read()).decode() if media_type == "video": return f'''

''' else: # audio return f'''

''' def create_media_gallery(): """Create the media gallery interface.""" st.header("🎬 Media Gallery") tabs = st.tabs(["🖼️ Images", "🎵 Audio", "🎥 Video"]) with tabs[0]: image_files = glob.glob("*.png") + glob.glob("*.jpg") if image_files: num_cols = st.slider("Number of columns", 1, 5, 3) cols = st.columns(num_cols) for idx, image_file in enumerate(image_files): with cols[idx % num_cols]: img = Image.open(image_file) st.image(img, use_container_width=True) # Add GPT vision analysis option if st.button(f"Analyze {os.path.basename(image_file)}"): analysis = process_image(image_file, "Describe this image in detail and identify key elements.") st.markdown(analysis) with tabs[1]: audio_files = glob.glob("*.mp3") + glob.glob("*.wav") for audio_file in audio_files: with st.expander(f"🎵 {os.path.basename(audio_file)}"): st.markdown(get_media_html(audio_file, "audio"), unsafe_allow_html=True) if st.button(f"Transcribe {os.path.basename(audio_file)}"): with open(audio_file, "rb") as f: transcription = process_audio(f) st.write(transcription) with tabs[2]: video_files = glob.glob("*.mp4") for video_file in video_files: with st.expander(f"🎥 {os.path.basename(video_file)}"): st.markdown(get_media_html(video_file, "video"), unsafe_allow_html=True) if st.button(f"Analyze {os.path.basename(video_file)}"): analysis = process_video_with_gpt(video_file, "Describe what's happening in this video.") st.markdown(analysis) def display_file_manager(): """Display file management sidebar with guaranteed unique button keys.""" st.sidebar.title("📁 File Management") all_files = glob.glob("*.md") all_files.sort(reverse=True) if st.sidebar.button("🗑 Delete All", key="delete_all_files_button"): for file in all_files: os.remove(file) st.rerun() if st.sidebar.button("⬇️ Download All", key="download_all_files_button"): zip_file = create_zip_of_files(all_files) st.sidebar.markdown(get_download_link(zip_file), unsafe_allow_html=True) # Create unique keys using file attributes for idx, file in enumerate(all_files): # Get file stats for unique identification file_stat = os.stat(file) unique_id = f"{idx}_{file_stat.st_size}_{file_stat.st_mtime}" col1, col2, col3, col4 = st.sidebar.columns([1,3,1,1]) with col1: if st.button("🌐", key=f"view_{unique_id}"): st.session_state.current_file = file st.session_state.file_content = load_file(file) with col2: st.markdown(get_download_link(file), unsafe_allow_html=True) with col3: if st.button("📂", key=f"edit_{unique_id}"): st.session_state.current_file = file st.session_state.file_content = load_file(file) with col4: if st.button("🗑", key=f"delete_{unique_id}"): os.remove(file) st.rerun() # Speech Recognition HTML Component speech_recognition_html = """ Continuous Speech Demo

Ready

""" # Helper Functions def generate_filename(prompt, file_type): central = pytz.timezone('US/Central') safe_date_time = datetime.now(central).strftime("%m%d_%H%M") replaced_prompt = re.sub(r'[<>:"/\\|?*\n]', ' ', prompt) safe_prompt = re.sub(r'\s+', ' ', replaced_prompt).strip()[:230] return f"{safe_date_time}_{safe_prompt}.{file_type}" # File Management Functions def load_file(file_name): """Load file content.""" with open(file_name, "r", encoding='utf-8') as file: content = file.read() return content def create_zip_of_files(files): """Create zip archive of files.""" zip_name = "all_files.zip" with zipfile.ZipFile(zip_name, 'w') as zipf: for file in files: zipf.write(file) return zip_name def get_download_link(file): """Create download link for file.""" with open(file, "rb") as f: contents = f.read() b64 = base64.b64encode(contents).decode() return f'Download {os.path.basename(file)}📂' def display_file_manager(): """Display file management sidebar.""" st.sidebar.title("📁 File Management") all_files = glob.glob("*.md") all_files.sort(reverse=True) if st.sidebar.button("🗑 Delete All"): for file in all_files: os.remove(file) st.rerun() if st.sidebar.button("⬇️ Download All"): zip_file = create_zip_of_files(all_files) st.sidebar.markdown(get_download_link(zip_file), unsafe_allow_html=True) for file in all_files: col1, col2, col3, col4 = st.sidebar.columns([1,3,1,1]) with col1: if st.button("🌐", key="view_"+file): st.session_state.current_file = file st.session_state.file_content = load_file(file) with col2: st.markdown(get_download_link(file), unsafe_allow_html=True) with col3: if st.button("📂", key="edit_"+file): st.session_state.current_file = file st.session_state.file_content = load_file(file) with col4: if st.button("🗑", key="delete_"+file): os.remove(file) st.rerun() def create_media_gallery(): """Create the media gallery interface.""" st.header("🎬 Media Gallery") tabs = st.tabs(["🖼️ Images", "🎵 Audio", "🎥 Video"]) with tabs[0]: image_files = glob.glob("*.png") + glob.glob("*.jpg") if image_files: num_cols = st.slider("Number of columns", 1, 5, 3) cols = st.columns(num_cols) for idx, image_file in enumerate(image_files): with cols[idx % num_cols]: img = Image.open(image_file) st.image(img, use_container_width=True) # Add GPT vision analysis option if st.button(f"Analyze {os.path.basename(image_file)}"): analysis = process_image(image_file, "Describe this image in detail and identify key elements.") st.markdown(analysis) with tabs[1]: audio_files = glob.glob("*.mp3") + glob.glob("*.wav") for audio_file in audio_files: with st.expander(f"🎵 {os.path.basename(audio_file)}"): st.markdown(get_media_html(audio_file, "audio"), unsafe_allow_html=True) if st.button(f"Transcribe {os.path.basename(audio_file)}"): with open(audio_file, "rb") as f: transcription = process_audio(f) st.write(transcription) with tabs[2]: video_files = glob.glob("*.mp4") for video_file in video_files: with st.expander(f"🎥 {os.path.basename(video_file)}"): st.markdown(get_media_html(video_file, "video"), unsafe_allow_html=True) if st.button(f"Analyze {os.path.basename(video_file)}"): analysis = process_video_with_gpt(video_file, "Describe what's happening in this video.") st.markdown(analysis) def get_media_html(media_path, media_type="video", width="100%"): """Generate HTML for media player.""" media_data = base64.b64encode(open(media_path, 'rb').read()).decode() if media_type == "video": return f'''

''' else: # audio return f'''

''' @st.cache_resource def set_transcript(text): """Set transcript in session state.""" st.session_state.voice_transcript = text def main(): st.sidebar.markdown("### 🚲BikeAI🏆 Claude and GPT Multi-Agent Research AI") # Main navigation tab_main = st.radio("Choose Action:", ["🎤 Voice Input", "💬 Chat", "📸 Media Gallery", "🔍 Search ArXiv", "📝 File Editor"], horizontal=True) if tab_main == "🎤 Voice Input": st.subheader("Voice Recognition") # Get transcript from the speech component current_transcript = integrate_speech_component() # Display the transcript with live updates transcript_placeholder = st.empty() transcript_placeholder.text_area( "Voice Transcript (Live)", value=current_transcript, height=100 ) # Add a status indicator status_placeholder = st.empty() if current_transcript != st.session_state.get('last_transcript', ''): status_placeholder.success("🔴 Recording in progress...") st.session_state.last_transcript = current_transcript # Process buttons col1, col2, col3 = st.columns(3) with col1: if st.button("Process with GPT"): if current_transcript: with st.spinner("Processing with GPT..."): gpt_response = process_with_gpt(current_transcript) st.markdown(gpt_response) with col2: if st.button("Process with Claude"): if current_transcript: with st.spinner("Processing with Claude..."): claude_response = process_with_claude(current_transcript) st.markdown(claude_response) with col3: if st.button("Search ArXiv"): if current_transcript: with st.spinner("Searching ArXiv..."): arxiv_results = perform_ai_lookup(current_transcript) st.markdown(arxiv_results) elif tab_main == "📝 File Editor": if hasattr(st.session_state, 'current_file'): st.subheader(f"Editing: {st.session_state.current_file}") new_content = st.text_area("Content:", st.session_state.file_content, height=300) if st.button("Save Changes"): with open(st.session_state.current_file, 'w', encoding='utf-8') as file: file.write(new_content) st.success("File updated successfully!") # Always show file manager in sidebar display_file_manager() if __name__ == "__main__": main()