The length of this program is 968 lines I believe. Can you rewrite the code in a way to reduce the code number of lines while still making it better by using emojis and other things appropriate to a unicode compliant streamlit python program launchingg on linux on huggingface? import streamlit as st
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 streamlit.components.v1 as components
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
# 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
}
)
load_dotenv()
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')
)
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)
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"
}
# markdown target for viewing files in markdown (number one feature)
markdown_target = st.empty()
# 2.🚲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 = ""
# 3. 🚲BikeAI🏆 Custom CSS
st.markdown("""
<style>
.main {
background: linear-gradient(to right, #1a1a1a, #2d2d2d);
color: #ffffff;
}
.stMarkdown {
font-family: 'Helvetica Neue', sans-serif;
}
.category-header {
background: linear-gradient(45deg, #2b5876, #4e4376);
padding: 20px;
border-radius: 10px;
margin: 10px 0;
}
.scene-card {
background: rgba(0,0,0,0.3);
padding: 15px;
border-radius: 8px;
margin: 10px 0;
border: 1px solid rgba(255,255,255,0.1);
}
.media-gallery {
display: grid;
gap: 1rem;
padding: 1rem;
}
.bike-card {
background: rgba(255,255,255,0.05);
border-radius: 10px;
padding: 15px;
transition: transform 0.3s;
}
.bike-card:hover {
transform: scale(1.02);
}
</style>
""", unsafe_allow_html=True)
# create and save a file (and avoid the black hole of lost data 🕳)
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]
safe_prompt = re.sub(r'\s+', ' ', replaced_prompt).strip()[:90] # Ensures file name is long enough but doesnt prevent unzip due to path length
return f"{safe_date_time}_{safe_prompt}.{file_type}"
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
# Load a file, base64 it, return as link
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'<a href="data:file/txt;base64,{b64}" download="{os.path.basename(file_path)}">Download {os.path.basename(file_path)}📂</a>'
# Speech Synth Browser Style
@st.cache_resource
def SpeechSynthesis(result):
"""HTML5 Speech Synthesis."""
documentHTML5 = f'''
<!DOCTYPE html>
<html>
<head>
<title>Read It Aloud</title>
<script type="text/javascript">
function readAloud() {{
const text = document.getElementById("textArea").value;
const speech = new SpeechSynthesisUtterance(text);
window.speechSynthesis.speak(speech);
}}
</script>
</head>
<body>
<h1>🔊 Read It Aloud</h1>
<textarea id="textArea" rows="10" cols="80">{result}</textarea>
<br>
<button onclick="readAloud()">🔊 Read Aloud</button>
</body>
</html>
'''
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'''
<video width="{width}" controls autoplay muted loop>
<source src="data:video/mp4;base64,{media_data}" type="video/mp4">
Your browser does not support the video tag.
</video>
'''
else: # audio
return f'''
<audio controls style="width: {width};">
<source src="data:audio/mpeg;base64,{media_data}" type="audio/mpeg">
Your browser does not support the audio element.
</audio>
'''
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 = """
<!DOCTYPE html>
<html>
<head>
<title>Continuous Speech Demo</title>
<style>
body {
font-family: sans-serif;
padding: 20px;
max-width: 800px;
margin: 0 auto;
}
button {
padding: 10px 20px;
margin: 10px 5px;
font-size: 16px;
}
#status {
margin: 10px 0;
padding: 10px;
background: #e8f5e9;
border-radius: 4px;
}
#output {
white-space: pre-wrap;
padding: 15px;
background: #f5f5f5;
border-radius: 4px;
margin: 10px 0;
min-height: 100px;
max-height: 400px;
overflow-y: auto;
}
.controls {
margin: 10px 0;
}
</style>
</head>
<body>
<div class="controls">
<button id="start">Start Listening</button>
<button id="stop" disabled>Stop Listening</button>
<button id="clear">Clear Text</button>
</div>
<div id="status">Ready</div>
<div id="output"></div>
<!-- Add the hidden input here -->
<input type="hidden" id="streamlit-data" value="">
<script>
if (!('webkitSpeechRecognition' in window)) {
alert('Speech recognition not supported');
} else {
const recognition = new webkitSpeechRecognition();
const startButton = document.getElementById('start');
const stopButton = document.getElementById('stop');
const clearButton = document.getElementById('clear');
const status = document.getElementById('status');
const output = document.getElementById('output');
let fullTranscript = '';
let lastUpdateTime = Date.now();
// Configure recognition
recognition.continuous = true;
recognition.interimResults = true;
// Function to start recognition
const startRecognition = () => {
try {
recognition.start();
status.textContent = 'Listening...';
startButton.disabled = true;
stopButton.disabled = false;
} catch (e) {
console.error(e);
status.textContent = 'Error: ' + e.message;
}
};
// Auto-start on load
window.addEventListener('load', () => {
setTimeout(startRecognition, 1000);
});
startButton.onclick = startRecognition;
stopButton.onclick = () => {
recognition.stop();
status.textContent = 'Stopped';
startButton.disabled = false;
stopButton.disabled = true;
};
clearButton.onclick = () => {
fullTranscript = '';
output.textContent = '';
window.parent.postMessage({
type: 'clear_transcript',
}, '*');
};
recognition.onresult = (event) => {
let interimTranscript = '';
let finalTranscript = '';
for (let i = event.resultIndex; i < event.results.length; i++) {
const transcript = event.results[i][0].transcript;
if (event.results[i].isFinal) {
finalTranscript += transcript + '\\n';
} else {
interimTranscript += transcript;
}
}
if (finalTranscript || (Date.now() - lastUpdateTime > 5000)) {
if (finalTranscript) {
fullTranscript += finalTranscript;
// Update the hidden input value
document.getElementById('streamlit-data').value = fullTranscript;
}
lastUpdateTime = Date.now();
}
output.textContent = fullTranscript + (interimTranscript ? '... ' + interimTranscript : '');
output.scrollTop = output.scrollHeight;
document.getElementById('streamlit-data').value = fullTranscript;
};
recognition.onend = () => {
if (!stopButton.disabled) {
try {
recognition.start();
console.log('Restarted recognition');
} catch (e) {
console.error('Failed to restart recognition:', e);
status.textContent = 'Error restarting: ' + e.message;
startButton.disabled = false;
stopButton.disabled = true;
}
}
};
recognition.onerror = (event) => {
console.error('Recognition error:', event.error);
status.textContent = 'Error: ' + event.error;
if (event.error === 'not-allowed' || event.error === 'service-not-allowed') {
startButton.disabled = false;
stopButton.disabled = true;
}
};
}
</script>
</body>
</html>
"""
# 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'<a href="data:file/txt;base64,{b64}" download="{os.path.basename(file)}">Download {os.path.basename(file)}📂</a>'
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)
st.write(file)
markdown_target.markdown(st.session_state.file_content) # view 🌐
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'''
<video width="{width}" controls autoplay muted loop>
<source src="data:video/mp4;base64,{media_data}" type="video/mp4">
Your browser does not support the video tag.
</video>
'''
else: # audio
return f'''
<audio controls style="width: {width};">
<source src="data:audio/mpeg;base64,{media_data}" type="audio/mpeg">
Your browser does not support the audio element.
</audio>
'''
@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", "📸 Media Gallery", "🔍 Search ArXiv", "📝 File Editor"],
horizontal=True)
# Model Selection
model_choice = st.sidebar.radio(
"Choose AI Model:",
[ "GPT+Claude+Arxiv", "GPT-4o", "Claude-3"]
)
# 🏆################ Component Magic ###############🏆
mycomponent = components.declare_component("mycomponent", path="mycomponent") # load from __init__.py and index.html in mycomponent folder
from mycomponent import mycomponent
value = mycomponent(my_input_value="hello there")
st.write("Received", value) # value is speech recognition full text result with \n dividing
if (value is not None):
user_input = value
if model_choice == "GPT-4o":
gpt_response = process_with_gpt(user_input)
elif model_choice == "Claude-3":
claude_response = process_with_claude(user_input)
else: # All Three AIs!
col1, col2, col3 = st.columns(3)
with col2:
st.subheader("Claude-3.5 Sonnet:")
try:
claude_response = process_with_claude(user_input)
except:
st.write('Claude 3.5 Sonnet out of tokens.')
with col1:
st.subheader("GPT-4o Omni:")
try:
gpt_response = process_with_gpt(user_input)
except:
st.write('GPT 4o out of tokens')
with col3:
st.subheader("Arxiv and Mistral Research:")
with st.spinner("Searching ArXiv..."):
try:
results = perform_ai_lookup(user_input)
st.markdown(results)
except:
st.write("Arxiv Mistral too busy - try again.")
# 🏆################ Component Magic ###############🏆
if tab_main == "🎤 Voice Input":
st.subheader("Voice Recognition")
# Initialize session state for the transcript
if 'voice_transcript' not in st.session_state:
st.session_state.voice_transcript = ""
# Display speech recognition component and capture returned value
#transcript = st.components.v1.html(speech_recognition_html, height=400)
# Update session state if there's new data
#if transcript is not None and transcript != "":
# st.session_state.voice_transcript = transcript
# Display the transcript in a Streamlit text area
# st.markdown("### Processed Voice Input:")
# st.text_area("Voice Transcript", st.session_state.voice_transcript, height=100)
# Chat Interface
user_input = st.text_area("Message:", height=100)
if st.button("Send 📨"):
if user_input:
if model_choice == "GPT-4o":
gpt_response = process_with_gpt(user_input)
elif model_choice == "Claude-3":
claude_response = process_with_claude(user_input)
else: # Both
col1, col2, col3 = st.columns(3)
with col2:
st.subheader("Claude-3.5 Sonnet:")
try:
claude_response = process_with_claude(user_input)
except:
st.write('Claude 3.5 Sonnet out of tokens.')
with col1:
st.subheader("GPT-4o Omni:")
try:
gpt_response = process_with_gpt(user_input)
except:
st.write('GPT 4o out of tokens')
with col3:
st.subheader("Arxiv and Mistral Research:")
with st.spinner("Searching ArXiv..."):
#results = search_arxiv(user_input)
results = perform_ai_lookup(user_input)
st.markdown(results)
# Display Chat History
st.subheader("Chat History 📜")
tab1, tab2 = st.tabs(["Claude History", "GPT-4o History"])
with tab1:
for chat in st.session_state.chat_history:
st.text_area("You:", chat["user"], height=100)
st.text_area("Claude:", chat["claude"], height=200)
st.markdown(chat["claude"])
with tab2:
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
elif tab_main == "📸 Media Gallery":
create_media_gallery()
elif tab_main == "🔍 Search ArXiv":
query = st.text_input("Enter your research query:")
if query:
with st.spinner("Searching ArXiv..."):
results = search_arxiv(query)
st.markdown(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()