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import streamlit as st
import anthropic
import openai
import base64
import cv2
import glob
import json
import math
import os
import pytz
import random
import re
import requests
import textract
import time
import zipfile
import plotly.graph_objects as go
import streamlit.components.v1 as components
from datetime import datetime
from audio_recorder_streamlit import audio_recorder
from bs4 import BeautifulSoup
from collections import defaultdict, deque, Counter
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 asyncio
import edge_tts
from streamlit_marquee import streamlit_marquee
from concurrent.futures import ThreadPoolExecutor
from functools import partial
from typing import Dict, List, Optional, Tuple, Union
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# 1. CORE CONFIGURATION & SETUP
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
st.set_page_config(
page_title="π²TalkingAIResearcherπ",
page_icon="π²π",
layout="wide",
initial_sidebar_state="auto",
menu_items={
'Get Help': 'https://huggingface.co/awacke1',
'Report a bug': 'https://huggingface.co/spaces/awacke1',
'About': "π²TalkingAIResearcherπ"
}
)
load_dotenv()
# Available English voices for Edge TTS
EDGE_TTS_VOICES = [
"en-US-AriaNeural",
"en-US-GuyNeural",
"en-US-JennyNeural",
"en-GB-SoniaNeural",
"en-GB-RyanNeural",
"en-AU-NatashaNeural",
"en-AU-WilliamNeural",
"en-CA-ClaraNeural",
"en-CA-LiamNeural"
]
# Session state initialization with default values
DEFAULT_SESSION_STATE = {
'marquee_settings': {
"background": "#1E1E1E",
"color": "#FFFFFF",
"font-size": "14px",
"animationDuration": "20s",
"width": "100%",
"lineHeight": "35px"
},
'tts_voice': EDGE_TTS_VOICES[0],
'audio_format': 'mp3',
'transcript_history': [],
'chat_history': [],
'openai_model': "gpt-4o-2024-05-13",
'messages': [],
'last_voice_input': "",
'editing_file': None,
'edit_new_name': "",
'edit_new_content': "",
'viewing_prefix': None,
'should_rerun': False,
'old_val': None,
'last_query': "",
'marquee_content': "π Welcome to TalkingAIResearcher | π€ Your Research Assistant",
'enable_audio': False,
'enable_download': False,
'enable_claude': True,
'audio_cache': {},
'paper_cache': {},
'download_link_cache': {},
'performance_metrics': defaultdict(list),
'operation_timings': defaultdict(float)
}
# Initialize session state
for key, value in DEFAULT_SESSION_STATE.items():
if key not in st.session_state:
st.session_state[key] = value
# API Keys and Configuration
openai_api_key = os.getenv('OPENAI_API_KEY', "")
anthropic_key = os.getenv('ANTHROPIC_API_KEY_3', "")
xai_key = os.getenv('xai', "")
if 'OPENAI_API_KEY' in st.secrets:
openai_api_key = st.secrets['OPENAI_API_KEY']
if 'ANTHROPIC_API_KEY' in st.secrets:
anthropic_key = st.secrets["ANTHROPIC_API_KEY"]
openai.api_key = openai_api_key
openai_client = OpenAI(api_key=openai.api_key, organization=os.getenv('OPENAI_ORG_ID'))
HF_KEY = os.getenv('HF_KEY')
API_URL = os.getenv('API_URL')
# File type emojis for display
FILE_EMOJIS = {
"md": "π",
"mp3": "π΅",
"wav": "π",
"pdf": "π",
"txt": "π",
"json": "π",
"csv": "π"
}
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# 2. PERFORMANCE MONITORING & TIMING
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
class PerformanceTimer:
"""Context manager for timing operations with automatic logging."""
def __init__(self, operation_name: str):
self.operation_name = operation_name
self.start_time = None
def __enter__(self):
self.start_time = time.time()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
if not exc_type: # Only log if no exception occurred
duration = time.time() - self.start_time
st.session_state['operation_timings'][self.operation_name] = duration
st.session_state['performance_metrics'][self.operation_name].append(duration)
def log_performance_metrics():
"""Display performance metrics in the sidebar."""
st.sidebar.markdown("### β±οΈ Performance Metrics")
metrics = st.session_state['operation_timings']
if metrics:
total_time = sum(metrics.values())
st.sidebar.write(f"**Total Processing Time:** {total_time:.2f}s")
# Create timing breakdown
for operation, duration in metrics.items():
percentage = (duration / total_time) * 100
st.sidebar.write(f"**{operation}:** {duration:.2f}s ({percentage:.1f}%)")
# Show timing history chart
if st.session_state['performance_metrics']:
history_data = []
for op, times in st.session_state['performance_metrics'].items():
if times: # Only show if we have timing data
avg_time = sum(times) / len(times)
history_data.append({"Operation": op, "Avg Time (s)": avg_time})
if history_data: # Create chart if we have data
st.sidebar.markdown("### π Timing History")
chart_data = pd.DataFrame(history_data)
st.sidebar.bar_chart(chart_data.set_index("Operation"))
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# 3. OPTIMIZED AUDIO GENERATION
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def clean_for_speech(text: str) -> str:
"""Clean up text for TTS output with enhanced cleaning."""
with PerformanceTimer("text_cleaning"):
# Remove markdown formatting
text = re.sub(r'#+ ', '', text) # Remove headers
text = re.sub(r'\[([^\]]+)\]\([^\)]+\)', r'\1', text) # Clean links
text = re.sub(r'[*_~`]', '', text) # Remove emphasis markers
# Remove code blocks
text = re.sub(r'```[\s\S]*?```', '', text)
text = re.sub(r'`[^`]*`', '', text)
# Clean up whitespace
text = re.sub(r'\s+', ' ', text)
text = text.replace("\n", " ")
text = text.replace("</s>", " ")
# Remove URLs
text = re.sub(r'https?://\S+', '', text)
text = re.sub(r'\(https?://[^\)]+\)', '', text)
# Final cleanup
text = text.strip()
return text
async def async_edge_tts_generate(
text: str,
voice: str,
rate: int = 0,
pitch: int = 0,
file_format: str = "mp3"
) -> Tuple[Optional[str], float]:
"""Asynchronous TTS generation with performance tracking and caching."""
with PerformanceTimer("tts_generation") as timer:
# Clean and validate text
text = clean_for_speech(text)
if not text.strip():
return None, 0
# Check cache
cache_key = f"{text[:100]}_{voice}_{rate}_{pitch}_{file_format}"
if cache_key in st.session_state['audio_cache']:
return st.session_state['audio_cache'][cache_key], 0
try:
# Generate audio
rate_str = f"{rate:+d}%"
pitch_str = f"{pitch:+d}Hz"
communicate = edge_tts.Communicate(text, voice, rate=rate_str, pitch=pitch_str)
# Generate unique filename
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"audio_{timestamp}_{random.randint(1000, 9999)}.{file_format}"
# Save audio file
await communicate.save(filename)
# Cache result
st.session_state['audio_cache'][cache_key] = filename
return filename, time.time() - timer.start_time
except Exception as e:
st.error(f"Error generating audio: {str(e)}")
return None, 0
async def async_save_qa_with_audio(
question: str,
answer: str,
voice: Optional[str] = None
) -> Tuple[str, Optional[str], float, float]:
"""Asynchronously save Q&A to markdown and generate audio with timing."""
voice = voice or st.session_state['tts_voice']
with PerformanceTimer("qa_save") as timer:
# Save markdown
md_start = time.time()
combined_text = f"# Question\n{question}\n\n# Answer\n{answer}"
md_file = create_file(question, answer, "md")
md_time = time.time() - md_start
# Generate audio if enabled
audio_file = None
audio_time = 0
if st.session_state['enable_audio']:
audio_text = f"{question}\n\nAnswer: {answer}"
audio_file, audio_time = await async_edge_tts_generate(
audio_text,
voice=voice,
file_format=st.session_state['audio_format']
)
return md_file, audio_file, md_time, audio_time
def create_download_link_with_cache(
file_path: str,
file_type: str = "mp3"
) -> str:
"""Create download link with caching and error handling."""
with PerformanceTimer("download_link_generation"):
# Check cache first
cache_key = f"dl_{file_path}"
if cache_key in st.session_state['download_link_cache']:
return st.session_state['download_link_cache'][cache_key]
try:
with open(file_path, "rb") as f:
b64 = base64.b64encode(f.read()).decode()
# Generate appropriate link based on file type
filename = os.path.basename(file_path)
if file_type == "mp3":
link = f'<a href="data:audio/mpeg;base64,{b64}" download="{filename}">π΅ Download {filename}</a>'
elif file_type == "wav":
link = f'<a href="data:audio/wav;base64,{b64}" download="{filename}">π Download {filename}</a>'
elif file_type == "md":
link = f'<a href="data:text/markdown;base64,{b64}" download="{filename}">π Download {filename}</a>'
else:
link = f'<a href="data:application/octet-stream;base64,{b64}" download="{filename}">β¬οΈ Download {filename}</a>'
# Cache and return
st.session_state['download_link_cache'][cache_key] = link
return link
except Exception as e:
st.error(f"Error creating download link: {str(e)}")
return ""
# ---
def display_voice_tab():
"""Display voice input tab with TTS settings."""
st.subheader("π€ Voice Input")
# Voice Settings Section
st.markdown("### π€ Voice Settings")
captionFemale='Top: πΈ **Aria** β πΆ **Jenny** β πΊ **Sonia** β π **Natasha** β π· **Clara**'
captionMale='Bottom: π **Guy** β π οΈ **Ryan** β π» **William** β π **Liam**'
st.sidebar.image('Group Picture - Voices.png', caption=captionFemale + ' - ' + captionMale)
st.sidebar.markdown("""
# ποΈ Voice Character Agent Selector π
1. Female:
- πΈ **Aria** β Female: π The voice of elegance and creativity, perfect for soothing storytelling or inspiring ideas.
- πΆ **Jenny** β Female: π Sweet and friendly, sheβs the go-to for warm, conversational tones.
- πΊ **Sonia** β Female: π Bold and confident, ideal for commanding attention and delivering with flair.
- π **Natasha** β Female: β¨ Enigmatic and sophisticated, Natasha is great for a touch of mystery and charm.
- π· **Clara** β Female: π Cheerful and gentle, perfect for nurturing, empathetic conversations.
---
2. Male:
- π **Guy** β Male: π© Sophisticated and versatile, a natural fit for clear and authoritative delivery.
- π οΈ **Ryan** β Male: π€ Down-to-earth and approachable, ideal for friendly and casual exchanges.
- π» **William** β Male: π Classic and refined, perfect for a scholarly or thoughtful tone.
- π **Liam** β Male: β‘ Energetic and upbeat, great for dynamic, engaging interactions.
""")
selected_voice = st.selectbox(
"Select TTS Voice:",
options=EDGE_TTS_VOICES,
index=EDGE_TTS_VOICES.index(st.session_state['tts_voice'])
)
# Audio Format Selection
st.markdown("### π Audio Format")
selected_format = st.radio(
"Choose Audio Format:",
options=["MP3", "WAV"],
index=0
)
# Update session state if settings change
if selected_voice != st.session_state['tts_voice']:
st.session_state['tts_voice'] = selected_voice
st.rerun()
if selected_format.lower() != st.session_state['audio_format']:
st.session_state['audio_format'] = selected_format.lower()
st.rerun()
# Text Input Area
user_text = st.text_area("π¬ Message:", height=100)
user_text = user_text.strip().replace('\n', ' ')
# Send Button
if st.button("π¨ Send"):
process_voice_input(user_text)
# Chat History
st.subheader("π Chat History")
for c in st.session_state.chat_history:
st.write("**You:**", c["user"])
st.write("**Response:**", c["claude"])
def display_arxiv_tab():
"""Display ArXiv search tab with options."""
st.subheader("π Query ArXiv")
q = st.text_input("π Query:", key="arxiv_query")
# Options Section
st.markdown("### π Options")
col1, col2 = st.columns(2)
with col1:
vocal_summary = st.checkbox("π Short Audio", value=True,
key="option_vocal_summary")
extended_refs = st.checkbox("π Long Refs", value=False,
key="option_extended_refs")
with col2:
titles_summary = st.checkbox("π Titles Only", value=True,
key="option_titles_summary")
full_audio = st.checkbox("π Full Audio", value=False,
key="option_full_audio")
full_transcript = st.checkbox("π§Ύ Full Transcript", value=False,
key="option_full_transcript")
if q and st.button("π Run Search"):
st.session_state.last_query = q
result, timings = perform_ai_lookup(
q,
vocal_summary=vocal_summary,
extended_refs=extended_refs,
titles_summary=titles_summary,
full_audio=full_audio
)
if full_transcript:
create_file(q, result, "md")
def display_media_tab():
"""Display media gallery tab with audio, images, and video."""
st.header("πΈ Media Gallery")
# Create tabs for different media types
tabs = st.tabs(["π΅ Audio", "πΌ Images", "π₯ Video"])
# Audio Files Tab
with tabs[0]:
st.subheader("π΅ Audio Files")
audio_files = glob.glob("*.mp3") + glob.glob("*.wav")
if audio_files:
for audio_file in audio_files:
with st.expander(os.path.basename(audio_file)):
st.audio(audio_file)
ext = os.path.splitext(audio_file)[1].replace('.', '')
dl_link = get_download_link(audio_file, file_type=ext)
st.markdown(dl_link, unsafe_allow_html=True)
else:
st.write("No audio files found.")
# Images Tab
with tabs[1]:
st.subheader("πΌ Image Files")
image_files = glob.glob("*.png") + glob.glob("*.jpg") + glob.glob("*.jpeg")
if image_files:
cols = st.slider("Columns:", 1, 5, 3, key="cols_images")
image_cols = st.columns(cols)
for i, img_file in enumerate(image_files):
with image_cols[i % cols]:
try:
img = Image.open(img_file)
st.image(img, use_column_width=True)
except Exception as e:
st.error(f"Error loading image {img_file}: {str(e)}")
else:
st.write("No images found.")
# Video Tab
with tabs[2]:
st.subheader("π₯ Video Files")
video_files = glob.glob("*.mp4") + glob.glob("*.mov") + glob.glob("*.avi")
if video_files:
for video_file in video_files:
with st.expander(os.path.basename(video_file)):
st.video(video_file)
else:
st.write("No videos found.")
def display_editor_tab():
"""Display text editor tab with file management."""
st.subheader("π Text Editor")
# File Management Section
st.markdown("### π File Management")
# File Selection
md_files = glob.glob("*.md")
selected_file = st.selectbox(
"Select file to edit:",
["New File"] + md_files,
key="file_selector"
)
# Edit Area
if selected_file == "New File":
new_filename = st.text_input("New filename (without extension):")
file_content = st.text_area("Content:", height=300)
if st.button("πΎ Save File"):
if new_filename:
try:
with open(f"{new_filename}.md", 'w', encoding='utf-8') as f:
f.write(file_content)
st.success(f"File {new_filename}.md saved successfully!")
st.session_state.should_rerun = True
except Exception as e:
st.error(f"Error saving file: {str(e)}")
else:
st.warning("Please enter a filename.")
else:
try:
# Load existing file content
with open(selected_file, 'r', encoding='utf-8') as f:
file_content = f.read()
# Edit existing file
edited_content = st.text_area(
"Edit content:",
value=file_content,
height=300
)
col1, col2 = st.columns(2)
with col1:
if st.button("πΎ Save Changes"):
try:
with open(selected_file, 'w', encoding='utf-8') as f:
f.write(edited_content)
st.success("Changes saved successfully!")
except Exception as e:
st.error(f"Error saving changes: {str(e)}")
with col2:
if st.button("π Delete File"):
try:
os.remove(selected_file)
st.success(f"File {selected_file} deleted successfully!")
st.session_state.should_rerun = True
except Exception as e:
st.error(f"Error deleting file: {str(e)}")
except Exception as e:
st.error(f"Error loading file {selected_file}: {str(e)}")
def display_settings_tab():
"""Display application settings tab."""
st.subheader("βοΈ Settings")
# General Settings
st.markdown("### π§ General Settings")
# Theme Selection
theme = st.selectbox(
"Color Theme:",
["Dark", "Light", "Custom"],
index=0
)
if theme == "Custom":
st.color_picker("Primary Color:", "#1E1E1E")
st.color_picker("Secondary Color:", "#2D2D2D")
# Performance Settings
st.markdown("### β‘ Performance Settings")
# Cache Settings
cache_size = st.slider(
"Maximum Cache Size (MB):",
0, 1000, 100
)
if st.button("Clear Cache"):
st.session_state['audio_cache'] = {}
st.session_state['paper_cache'] = {}
st.session_state['download_link_cache'] = {}
st.success("Cache cleared successfully!")
# API Settings
st.markdown("### π API Settings")
# Show/hide API keys
show_keys = st.checkbox("Show API Keys")
if show_keys:
st.text_input("OpenAI API Key:", value=openai_api_key)
st.text_input("Anthropic API Key:", value=anthropic_key)
# Save Settings
if st.button("πΎ Save Settings"):
st.success("Settings saved successfully!")
st.session_state.should_rerun = True
def get_download_link(file: str, file_type: str = "zip") -> str:
"""
Convert a file to base64 and return an HTML link for download.
Supports multiple file types with appropriate MIME types.
"""
try:
with open(file, "rb") as f:
b64 = base64.b64encode(f.read()).decode()
# Get filename for display
filename = os.path.basename(file)
# Define MIME types and emoji icons for different file types
mime_types = {
"zip": ("application/zip", "π"),
"mp3": ("audio/mpeg", "π΅"),
"wav": ("audio/wav", "π"),
"md": ("text/markdown", "π"),
"pdf": ("application/pdf", "π"),
"txt": ("text/plain", "π"),
"json": ("application/json", "π"),
"csv": ("text/csv", "π"),
"png": ("image/png", "πΌ"),
"jpg": ("image/jpeg", "πΌ"),
"jpeg": ("image/jpeg", "πΌ")
}
# Get MIME type and emoji for file
mime_type, emoji = mime_types.get(
file_type.lower(),
("application/octet-stream", "β¬οΈ")
)
# Create download link with appropriate MIME type
link = f'<a href="data:{mime_type};base64,{b64}" download="{filename}">{emoji} Download {filename}</a>'
return link
except FileNotFoundError:
return f"<p style='color: red'>β File not found: {file}</p>"
except Exception as e:
return f"<p style='color: red'>β Error creating download link: {str(e)}</p>"
def play_and_download_audio(file_path: str, file_type: str = "mp3"):
"""
Display audio player and download link for audio file.
Includes error handling and file validation.
"""
if not file_path:
st.warning("No audio file provided.")
return
if not os.path.exists(file_path):
st.error(f"Audio file not found: {file_path}")
return
try:
# Display audio player
st.audio(file_path)
# Create and display download link
dl_link = get_download_link(file_path, file_type=file_type)
st.markdown(dl_link, unsafe_allow_html=True)
except Exception as e:
st.error(f"Error playing audio: {str(e)}")
def get_file_info(file_path: str) -> dict:
"""
Get detailed information about a file.
Returns dictionary with size, modification time, and other metadata.
"""
try:
stats = os.stat(file_path)
# Get basic file information
info = {
'name': os.path.basename(file_path),
'path': file_path,
'size': stats.st_size,
'modified': datetime.fromtimestamp(stats.st_mtime),
'created': datetime.fromtimestamp(stats.st_ctime),
'type': os.path.splitext(file_path)[1].lower().strip('.'),
}
# Add formatted size
if info['size'] < 1024:
info['size_fmt'] = f"{info['size']} B"
elif info['size'] < 1024 * 1024:
info['size_fmt'] = f"{info['size']/1024:.1f} KB"
else:
info['size_fmt'] = f"{info['size']/(1024*1024):.1f} MB"
# Add formatted dates
info['modified_fmt'] = info['modified'].strftime("%Y-%m-%d %H:%M:%S")
info['created_fmt'] = info['created'].strftime("%Y-%m-%d %H:%M:%S")
return info
except Exception as e:
st.error(f"Error getting file info: {str(e)}")
return None
def sanitize_filename(filename: str) -> str:
"""
Clean and sanitize a filename to ensure it's safe for filesystem.
Removes/replaces unsafe characters and enforces length limits.
"""
# Remove or replace unsafe characters
filename = re.sub(r'[<>:"/\\|?*]', '_', filename)
# Remove leading/trailing spaces and dots
filename = filename.strip('. ')
# Limit length (reserving space for extension)
max_length = 255
name, ext = os.path.splitext(filename)
if len(filename) > max_length:
return name[:(max_length-len(ext))] + ext
return filename
def create_file_with_metadata(filename: str, content: str, metadata: dict = None):
"""
Create a file with optional metadata header.
Useful for storing additional information with files.
"""
try:
# Sanitize filename
safe_filename = sanitize_filename(filename)
# Ensure directory exists
os.makedirs(os.path.dirname(safe_filename) or '.', exist_ok=True)
# Prepare content with metadata
if metadata:
metadata_str = json.dumps(metadata, indent=2)
full_content = f"""---
{metadata_str}
---
{content}"""
else:
full_content = content
# Write file
with open(safe_filename, 'w', encoding='utf-8') as f:
f.write(full_content)
return safe_filename
except Exception as e:
st.error(f"Error creating file: {str(e)}")
return None
def read_file_with_metadata(filename: str) -> tuple:
"""
Read a file and extract any metadata header.
Returns tuple of (content, metadata).
"""
try:
with open(filename, 'r', encoding='utf-8') as f:
content = f.read()
# Check for metadata section
if content.startswith('---\n'):
# Find end of metadata section
end_meta = content.find('\n---\n', 4)
if end_meta != -1:
try:
metadata = json.loads(content[4:end_meta])
content = content[end_meta+5:]
return content, metadata
except json.JSONDecodeError:
pass
return content, None
except Exception as e:
st.error(f"Error reading file: {str(e)}")
return None, None
def archive_files(file_paths: list, archive_name: str = None) -> str:
"""
Create a zip archive containing the specified files.
Returns path to created archive.
"""
try:
# Generate archive name if not provided
if not archive_name:
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
archive_name = f"archive_{timestamp}.zip"
# Create zip file
with zipfile.ZipFile(archive_name, 'w', zipfile.ZIP_DEFLATED) as zf:
for file_path in file_paths:
if os.path.exists(file_path):
zf.write(file_path, os.path.basename(file_path))
return archive_name
except Exception as e:
st.error(f"Error creating archive: {str(e)}")
return None
def list_files_by_type(directory: str = ".",
extensions: list = None,
recursive: bool = False) -> dict:
"""
List files in directory filtered by extension.
Returns dict grouping files by type.
"""
try:
if extensions is None:
extensions = ['md', 'mp3', 'wav', 'pdf', 'txt', 'json', 'csv']
files = {}
pattern = "**/*" if recursive else "*"
for ext in extensions:
glob_pattern = f"{pattern}.{ext}"
matches = glob.glob(os.path.join(directory, glob_pattern),
recursive=recursive)
if matches:
files[ext] = matches
return files
except Exception as e:
st.error(f"Error listing files: {str(e)}")
return {}
def get_central_time() -> datetime:
"""Get current time in US Central timezone."""
central = pytz.timezone('US/Central')
return datetime.now(central)
def format_timestamp_prefix() -> str:
"""Generate timestamp prefix in format MM_dd_yy_hh_mm_AM/PM."""
ct = get_central_time()
return ct.strftime("%m_%d_%y_%I_%M_%p")
def get_formatted_time(dt: datetime = None,
timezone: str = 'US/Central',
include_timezone: bool = True,
include_seconds: bool = False) -> str:
"""
Format a datetime object with specified options.
If no datetime is provided, uses current time.
"""
if dt is None:
tz = pytz.timezone(timezone)
dt = datetime.now(tz)
elif dt.tzinfo is None:
tz = pytz.timezone(timezone)
dt = tz.localize(dt)
format_string = "%Y-%m-%d %I:%M"
if include_seconds:
format_string += ":%S"
format_string += " %p"
if include_timezone:
format_string += " %Z"
return dt.strftime(format_string)
def parse_timestamp(timestamp_str: str,
timezone: str = 'US/Central') -> Optional[datetime]:
"""
Parse a timestamp string in various formats.
Returns timezone-aware datetime object.
"""
try:
# Try different format patterns
patterns = [
"%m_%d_%y_%I_%M_%p", # Standard app format
"%Y-%m-%d %I:%M %p", # Common 12-hour format
"%Y-%m-%d %H:%M", # 24-hour format
"%m/%d/%y %I:%M %p", # US date format
"%d/%m/%y %I:%M %p" # European date format
]
dt = None
for pattern in patterns:
try:
dt = datetime.strptime(timestamp_str, pattern)
break
except ValueError:
continue
if dt is None:
raise ValueError(f"Could not parse timestamp: {timestamp_str}")
# Add timezone if not present
if dt.tzinfo is None:
tz = pytz.timezone(timezone)
dt = tz.localize(dt)
return dt
except Exception as e:
st.error(f"Error parsing timestamp: {str(e)}")
return None
def get_time_ago(dt: datetime) -> str:
"""
Convert datetime to human-readable "time ago" format.
E.g., "2 hours ago", "3 days ago", etc.
"""
try:
now = datetime.now(dt.tzinfo)
diff = now - dt
seconds = diff.total_seconds()
if seconds < 60:
return "just now"
elif seconds < 3600:
minutes = int(seconds / 60)
return f"{minutes} minute{'s' if minutes != 1 else ''} ago"
elif seconds < 86400:
hours = int(seconds / 3600)
return f"{hours} hour{'s' if hours != 1 else ''} ago"
elif seconds < 604800:
days = int(seconds / 86400)
return f"{days} day{'s' if days != 1 else ''} ago"
elif seconds < 2592000:
weeks = int(seconds / 604800)
return f"{weeks} week{'s' if weeks != 1 else ''} ago"
elif seconds < 31536000:
months = int(seconds / 2592000)
return f"{months} month{'s' if months != 1 else ''} ago"
else:
years = int(seconds / 31536000)
return f"{years} year{'s' if years != 1 else ''} ago"
except Exception as e:
st.error(f"Error calculating time ago: {str(e)}")
return "unknown time ago"
def format_duration(seconds: float) -> str:
"""
Format a duration in seconds to human-readable string.
E.g., "2m 30s", "1h 15m", etc.
"""
try:
if seconds < 0:
return "invalid duration"
# Handle special cases
if seconds < 1:
return f"{seconds * 1000:.0f}ms"
if seconds < 60:
return f"{seconds:.1f}s"
# Calculate hours, minutes, seconds
hours = int(seconds // 3600)
minutes = int((seconds % 3600) // 60)
secs = seconds % 60
# Build duration string
parts = []
if hours > 0:
parts.append(f"{hours}h")
if minutes > 0:
parts.append(f"{minutes}m")
if secs > 0 and hours == 0: # Only show seconds if less than an hour
parts.append(f"{secs:.1f}s")
return " ".join(parts)
except Exception as e:
st.error(f"Error formatting duration: {str(e)}")
return "unknown duration"
async def create_paper_audio_files(papers: List[Dict], input_question: str):
"""Generate audio files for papers asynchronously with improved naming."""
with PerformanceTimer("paper_audio_generation"):
tasks = []
for paper in papers:
try:
# Prepare text for audio generation
audio_text = f"{paper['title']} by {paper['authors']}. {paper['summary']}"
audio_text = clean_for_speech(audio_text)
# Create sanitized title for filename
safe_title = paper['title'].lower()
safe_title = re.sub(r'[^\w\s-]', '', safe_title) # Remove special chars
safe_title = re.sub(r'\s+', '_', safe_title) # Replace spaces with underscores
safe_title = safe_title[:100] # Limit length
# Generate timestamp
timestamp = format_timestamp_prefix()
# Create filename with timestamp and title
filename = f"{timestamp}_{safe_title}.{st.session_state['audio_format']}"
# Create task for audio generation
async def generate_audio(text, filename):
rate_str = "0%"
pitch_str = "0Hz"
communicate = edge_tts.Communicate(text, st.session_state['tts_voice'])
await communicate.save(filename)
return filename
task = generate_audio(audio_text, filename)
tasks.append((paper, task, filename))
except Exception as e:
st.warning(f"Error preparing audio for paper {paper['title']}: {str(e)}")
continue
# Process all audio generation tasks concurrently
for paper, task, filename in tasks:
try:
audio_file = await task
if audio_file:
paper['full_audio'] = audio_file
if st.session_state['enable_download']:
paper['download_base64'] = create_download_link_with_cache(
audio_file,
st.session_state['audio_format']
)
except Exception as e:
st.warning(f"Error generating audio for paper {paper['title']}: {str(e)}")
paper['full_audio'] = None
paper['download_base64'] = ''
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# 4. PAPER PROCESSING & DISPLAY
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def parse_arxiv_refs(ref_text: str) -> List[Dict[str, str]]:
"""Parse arxiv references with improved error handling."""
if not ref_text:
return []
with PerformanceTimer("parse_refs"):
results = []
current_paper = {}
lines = ref_text.split('\n')
for i, line in enumerate(lines):
try:
if line.count('|') == 2:
# Found a new paper line
if current_paper:
results.append(current_paper)
if len(results) >= 20: # Limit to 20 papers
break
# Parse header parts
header_parts = line.strip('* ').split('|')
date = header_parts[0].strip()
title = header_parts[1].strip()
url_match = re.search(r'(https://arxiv.org/\S+)', line)
url = url_match.group(1) if url_match else f"paper_{len(results)}"
current_paper = {
'date': date,
'title': title,
'url': url,
'authors': '',
'summary': '',
'full_audio': None,
'download_base64': '',
}
elif current_paper:
# Add content to current paper
line = line.strip('* ')
if not current_paper['authors']:
current_paper['authors'] = line
else:
if current_paper['summary']:
current_paper['summary'] += ' ' + line
else:
current_paper['summary'] = line
except Exception as e:
st.warning(f"Error parsing line {i}: {str(e)}")
continue
# Add final paper if exists
if current_paper:
results.append(current_paper)
return results[:20] # Ensure we don't exceed 20 papers
async def create_paper_audio_files(papers: List[Dict], input_question: str):
"""Generate audio files for papers asynchronously with progress tracking."""
with PerformanceTimer("paper_audio_generation"):
tasks = []
for paper in papers:
try:
# Prepare text for audio generation
audio_text = f"{paper['title']} by {paper['authors']}. {paper['summary']}"
audio_text = clean_for_speech(audio_text)
# Create task for audio generation
task = async_edge_tts_generate(
audio_text,
voice=st.session_state['tts_voice'],
file_format=st.session_state['audio_format']
)
tasks.append((paper, task))
except Exception as e:
st.warning(f"Error preparing audio for paper {paper['title']}: {str(e)}")
continue
# Process all audio generation tasks concurrently
for paper, task in tasks:
try:
audio_file, gen_time = await task
if audio_file:
paper['full_audio'] = audio_file
if st.session_state['enable_download']:
paper['download_base64'] = create_download_link_with_cache(
audio_file,
st.session_state['audio_format']
)
except Exception as e:
st.warning(f"Error generating audio for paper {paper['title']}: {str(e)}")
paper['full_audio'] = None
paper['download_base64'] = ''
def initialize_marquee_settings():
"""Initialize default marquee settings if not present in session state."""
if 'marquee_settings' not in st.session_state:
st.session_state['marquee_settings'] = {
"background": "#1E1E1E",
"color": "#FFFFFF",
"font-size": "14px",
"animationDuration": "20s",
"width": "100%",
"lineHeight": "35px"
}
def get_marquee_settings():
"""Get current marquee settings, initializing if needed."""
initialize_marquee_settings()
return st.session_state['marquee_settings']
def update_marquee_settings_ui():
"""Add color pickers & sliders for marquee configuration in sidebar."""
st.sidebar.markdown("### π― Marquee Settings")
# Create two columns for settings
cols = st.sidebar.columns(2)
# Column 1: Color settings
with cols[0]:
# Background color picker
bg_color = st.color_picker(
"π¨ Background",
st.session_state['marquee_settings']["background"],
key="bg_color_picker"
)
# Text color picker
text_color = st.color_picker(
"βοΈ Text Color",
st.session_state['marquee_settings']["color"],
key="text_color_picker"
)
# Column 2: Size and speed settings
with cols[1]:
# Font size slider
font_size = st.slider(
"π Font Size",
10, 24, 14,
key="font_size_slider"
)
# Animation duration slider
duration = st.slider(
"β±οΈ Animation Speed",
1, 20, 20,
key="duration_slider"
)
# Update session state with new settings
st.session_state['marquee_settings'].update({
"background": bg_color,
"color": text_color,
"font-size": f"{font_size}px",
"animationDuration": f"{duration}s"
})
def display_marquee(text: str, settings: dict, key_suffix: str = ""):
"""Show marquee text with specified style settings."""
# Truncate long text to prevent performance issues
truncated_text = text[:280] + "..." if len(text) > 280 else text
# Display the marquee
streamlit_marquee(
content=truncated_text,
**settings,
key=f"marquee_{key_suffix}"
)
# Add spacing after marquee
st.write("")
def create_paper_links_md(papers: list) -> str:
"""Creates a minimal markdown file linking to each paper's arxiv URL."""
lines = ["# Paper Links\n"]
for i, p in enumerate(papers, start=1):
lines.append(f"{i}. **{p['title']}** β [Arxiv]({p['url']})")
return "\n".join(lines)
def apply_custom_styling():
"""Apply custom CSS styling to the app."""
st.markdown("""
<style>
.main {
background: linear-gradient(to right, #1a1a1a, #2d2d2d);
color: #fff;
}
.stMarkdown {
font-family: 'Helvetica Neue', sans-serif;
}
.stButton>button {
margin-right: 0.5rem;
}
.streamlit-marquee {
margin: 1rem 0;
border-radius: 4px;
}
.st-emotion-cache-1y4p8pa {
padding: 1rem;
}
</style>
""", unsafe_allow_html=True)
def display_performance_metrics(timings: dict):
"""Display performance metrics with visualizations."""
st.sidebar.markdown("### β±οΈ Performance Metrics")
# Calculate total time
total_time = sum(timings.values())
st.sidebar.write(f"**Total Processing Time:** {total_time:.2f}s")
# Show breakdown of operations
st.sidebar.markdown("#### Operation Breakdown")
for operation, duration in timings.items():
percentage = (duration / total_time) * 100 if total_time > 0 else 0
st.sidebar.write(f"**{operation}:** {duration:.2f}s ({percentage:.1f}%)")
# Create a progress bar for visual representation
st.sidebar.progress(percentage / 100)
def display_papers(papers: List[Dict], marquee_settings: Dict):
"""Display paper information with enhanced visualization."""
with PerformanceTimer("paper_display"):
st.write("## π Research Papers")
# Create tabs for different views
tab1, tab2 = st.tabs(["π List View", "π Grid View"])
with tab1:
for i, paper in enumerate(papers, start=1):
# Create marquee for paper title
marquee_text = f"π {paper['title']} | π€ {paper['authors'][:120]}"
display_marquee(marquee_text, marquee_settings, key_suffix=f"paper_{i}")
# Paper details expander
with st.expander(f"{i}. π {paper['title']}", expanded=True):
# Create PDF link
pdf_url = paper['url'].replace('/abs/', '/pdf/')
# Display paper information
st.markdown(f"""
**Date:** {paper['date']}
**Title:** {paper['title']}
**Links:** π [Abstract]({paper['url']}) | π [PDF]({pdf_url})
""")
st.markdown(f"**Authors:** {paper['authors']}")
st.markdown(f"**Summary:** {paper['summary']}")
# Audio player and download if available
if paper.get('full_audio'):
st.write("π§ Paper Audio Summary")
st.audio(paper['full_audio'])
if paper['download_base64']:
st.markdown(paper['download_base64'], unsafe_allow_html=True)
with tab2:
# Create a grid layout of papers
cols = st.columns(3)
for i, paper in enumerate(papers):
with cols[i % 3]:
st.markdown(f"""
### π {paper['title'][:50]}...
**Date:** {paper['date']}
[Abstract]({paper['url']}) | [PDF]({paper['url'].replace('/abs/', '/pdf/')})
""")
if paper.get('full_audio'):
st.audio(paper['full_audio'])
def display_papers_in_sidebar(papers: List[Dict]):
"""Display paper listing in sidebar with lazy loading."""
with PerformanceTimer("sidebar_display"):
st.sidebar.title("π Papers Overview")
# Add filter options
filter_date = st.sidebar.date_input("Filter by date:", None)
search_term = st.sidebar.text_input("Search papers:", "")
# Filter papers based on criteria
filtered_papers = papers
if filter_date:
filtered_papers = [p for p in filtered_papers
if filter_date.strftime("%Y-%m-%d") in p['date']]
if search_term:
search_lower = search_term.lower()
filtered_papers = [p for p in filtered_papers
if search_lower in p['title'].lower()
or search_lower in p['authors'].lower()]
# Display filtered papers
for i, paper in enumerate(filtered_papers, start=1):
paper_key = f"paper_{paper['url']}"
if paper_key not in st.session_state:
st.session_state[paper_key] = False
with st.sidebar.expander(f"{i}. {paper['title'][:50]}...", expanded=False):
# Paper metadata
st.markdown(f"**Date:** {paper['date']}")
# Links
pdf_url = paper['url'].replace('/abs/', '/pdf/')
st.markdown(f"π [Abstract]({paper['url']}) | π [PDF]({pdf_url})")
# Preview of authors and summary
st.markdown(f"**Authors:** {paper['authors'][:100]}...")
if paper['summary']:
st.markdown(f"**Summary:** {paper['summary'][:200]}...")
# Audio controls
if paper['full_audio']:
if st.button("π΅ Load Audio", key=f"btn_{paper_key}"):
st.session_state[paper_key] = True
if st.session_state[paper_key]:
st.audio(paper['full_audio'])
if paper['download_base64']:
st.markdown(paper['download_base64'], unsafe_allow_html=True)
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# 5. FILE MANAGEMENT & HISTORY
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def create_file(prompt: str, response: str, file_type: str = "md") -> str:
"""Create a file with proper naming and error handling."""
with PerformanceTimer("file_creation"):
try:
# Generate filename
filename = generate_filename(prompt.strip(), response.strip(), file_type)
# Ensure directory exists
os.makedirs("generated_files", exist_ok=True)
filepath = os.path.join("generated_files", filename)
# Write content
with open(filepath, 'w', encoding='utf-8') as f:
if file_type == "md":
f.write(f"# Query\n{prompt}\n\n# Response\n{response}")
else:
f.write(f"{prompt}\n\n{response}")
return filepath
except Exception as e:
st.error(f"Error creating file: {str(e)}")
return ""
def get_high_info_terms(text: str, top_n: int = 10) -> List[str]:
"""Extract most informative terms from text."""
# Common English stop words to filter out
stop_words = set([
'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to',
'for', 'of', 'with', 'by', 'from', 'up', 'about', 'into', 'over',
'after', 'the', 'this', 'that', 'these', 'those', 'what', 'which'
])
# Extract words and bi-grams
words = re.findall(r'\b\w+(?:-\w+)*\b', text.lower())
bi_grams = [' '.join(pair) for pair in zip(words, words[1:])]
# Combine and filter terms
combined = words + bi_grams
filtered = [term for term in combined
if term not in stop_words
and len(term.split()) <= 2
and len(term) > 3]
# Count and return top terms
counter = Counter(filtered)
return [term for term, freq in counter.most_common(top_n)]
def clean_text_for_filename(text: str) -> str:
"""Clean text for use in filenames."""
# Remove special characters
text = text.lower()
text = re.sub(r'[^\w\s-]', '', text)
# Remove common unhelpful words
stop_words = set([
'the', 'and', 'for', 'with', 'this', 'that', 'what', 'which',
'where', 'when', 'why', 'how', 'who', 'whom', 'whose', 'ai',
'library', 'function', 'method', 'class', 'object', 'variable'
])
words = text.split()
filtered = [w for w in words if len(w) > 3 and w not in stop_words]
return '_'.join(filtered)[:200]
def generate_filename(prompt: str, response: str, file_type: str = "md",
max_length: int = 200) -> str:
"""Generate descriptive filename from content."""
# Get timestamp prefix
prefix = format_timestamp_prefix() + "_"
# Extract informative terms
combined_text = (prompt + " " + response)[:500]
info_terms = get_high_info_terms(combined_text, top_n=5)
# Get content snippet
snippet = (prompt[:40] + " " + response[:40]).strip()
snippet_cleaned = clean_text_for_filename(snippet)
# Combine and deduplicate parts
name_parts = info_terms + [snippet_cleaned]
seen = set()
unique_parts = []
for part in name_parts:
if part not in seen:
seen.add(part)
unique_parts.append(part)
# Create final filename
full_name = '_'.join(unique_parts).strip('_')
leftover_chars = max_length - len(prefix) - len(file_type) - 1
if len(full_name) > leftover_chars:
full_name = full_name[:leftover_chars]
return f"{prefix}{full_name}.{file_type}"
def create_zip_of_files(md_files: List[str], mp3_files: List[str],
wav_files: List[str], input_question: str) -> Optional[str]:
"""Create zip archive of files with optimization."""
with PerformanceTimer("zip_creation"):
# Filter out readme and empty files
md_files = [f for f in md_files
if os.path.basename(f).lower() != 'readme.md'
and os.path.getsize(f) > 0]
all_files = md_files + mp3_files + wav_files
if not all_files:
return None
try:
# Generate zip name
all_content = []
for f in all_files:
if f.endswith('.md'):
with open(f, 'r', encoding='utf-8') as file:
all_content.append(file.read())
elif f.endswith(('.mp3', '.wav')):
basename = os.path.splitext(os.path.basename(f))[0]
all_content.append(basename.replace('_', ' '))
all_content.append(input_question)
combined_content = " ".join(all_content)
info_terms = get_high_info_terms(combined_content, top_n=10)
timestamp = format_timestamp_prefix()
name_text = '-'.join(term for term in info_terms[:5])
zip_name = f"archive_{timestamp}_{name_text[:50]}.zip"
# Create zip file
with zipfile.ZipFile(zip_name, 'w', zipfile.ZIP_DEFLATED) as z:
for f in all_files:
z.write(f, os.path.basename(f))
return zip_name
except Exception as e:
st.error(f"Error creating zip archive: {str(e)}")
return None
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# 6. OPTIMIZED AI LOOKUP & PROCESSING
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def perform_ai_lookup(q: str, vocal_summary: bool = True,
extended_refs: bool = False,
titles_summary: bool = True,
full_audio: bool = False) -> Tuple[str, Dict[str, float]]:
"""Main AI lookup routine with performance optimization."""
with PerformanceTimer("total_lookup") as total_timer:
timings = {}
# Add operation controls if not present
if 'operation_controls' not in st.session_state:
st.sidebar.markdown("### π§ Operation Controls")
st.session_state['enable_claude'] = st.sidebar.checkbox(
"Enable Claude Search",
value=st.session_state['enable_claude']
)
st.session_state['enable_audio'] = st.sidebar.checkbox(
"Generate Audio",
value=st.session_state['enable_audio']
)
st.session_state['enable_download'] = st.sidebar.checkbox(
"Create Download Links",
value=st.session_state['enable_download']
)
st.session_state['operation_controls'] = True
result = ""
# 1. Claude API (if enabled)
if st.session_state['enable_claude']:
with PerformanceTimer("claude_api") as claude_timer:
try:
client = anthropic.Anthropic(api_key=anthropic_key)
response = client.messages.create(
model="claude-3-sonnet-20240229",
max_tokens=1000,
messages=[{"role": "user", "content": q}]
)
st.write("Claude's reply π§ :")
st.markdown(response.content[0].text)
result = response.content[0].text
timings['claude_api'] = time.time() - claude_timer.start_time
except Exception as e:
st.error(f"Error with Claude API: {str(e)}")
result = "Error occurred during Claude API call"
timings['claude_api'] = 0
# 2. Async save and audio generation
async def process_results():
with PerformanceTimer("results_processing") as proc_timer:
md_file, audio_file, md_time, audio_time = await async_save_qa_with_audio(
q, result
)
timings['markdown_save'] = md_time
timings['audio_generation'] = audio_time
if audio_file and st.session_state['enable_audio']:
st.subheader("π Main Response Audio")
st.audio(audio_file)
if st.session_state['enable_download']:
st.markdown(
create_download_link_with_cache(
audio_file,
st.session_state['audio_format']
),
unsafe_allow_html=True
)
# Run async operations
asyncio.run(process_results())
# 3. Arxiv RAG with performance tracking
if st.session_state['enable_claude']:
with PerformanceTimer("arxiv_rag") as rag_timer:
try:
st.write('Running Arxiv RAG with Claude inputs.')
client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
refs = client.predict(
q,
10,
"Semantic Search",
"mistralai/Mixtral-8x7B-Instruct-v0.1",
api_name="/update_with_rag_md"
)[0]
timings['arxiv_rag'] = time.time() - rag_timer.start_time
# Process papers asynchronously
papers = parse_arxiv_refs(refs)
if papers:
with PerformanceTimer("paper_processing") as paper_timer:
async def process_papers():
# Create minimal links page
paper_links = create_paper_links_md(papers)
links_file = create_file(q, paper_links, "md")
st.markdown(paper_links)
# Generate audio and display papers
await create_paper_audio_files(papers, q)
display_papers(papers, get_marquee_settings())
display_papers_in_sidebar(papers)
asyncio.run(process_papers())
timings['paper_processing'] = time.time() - paper_timer.start_time
else:
st.warning("No papers found in the response.")
except Exception as e:
st.error(f"Error during Arxiv RAG: {str(e)}")
timings['arxiv_rag'] = 0
return result, timings
def process_voice_input(text: str):
"""Process voice input with enhanced error handling and feedback."""
if not text:
st.warning("Please provide some input text.")
return
with PerformanceTimer("voice_processing"):
try:
st.subheader("π Search Results")
result, timings = perform_ai_lookup(
text,
vocal_summary=True,
extended_refs=False,
titles_summary=True,
full_audio=True
)
# Save results
md_file, audio_file = save_qa_with_audio(text, result)
# Display results
st.subheader("π Generated Files")
col1, col2 = st.columns(2)
with col1:
st.write(f"π Markdown: {os.path.basename(md_file)}")
st.markdown(get_download_link(md_file, "md"), unsafe_allow_html=True)
with col2:
if audio_file:
st.write(f"π΅ Audio: {os.path.basename(audio_file)}")
play_and_download_audio(
audio_file,
st.session_state['audio_format']
)
except Exception as e:
st.error(f"Error processing voice input: {str(e)}")
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# 7. SIDEBAR AND FILE HISTORY
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def display_file_history_in_sidebar():
"""Display file history with enhanced organization and filtering."""
with PerformanceTimer("file_history"):
st.sidebar.markdown("---")
st.sidebar.markdown("### π File History")
# Gather all files
md_files = glob.glob("*.md")
mp3_files = glob.glob("*.mp3")
wav_files = glob.glob("*.wav")
all_files = md_files + mp3_files + wav_files
if not all_files:
st.sidebar.write("No files found.")
return
# Add file management controls
col1, col2 = st.sidebar.columns(2)
with col1:
if st.button("π Delete All"):
try:
for f in all_files:
os.remove(f)
st.session_state.should_rerun = True
st.success("All files deleted successfully.")
except Exception as e:
st.error(f"Error deleting files: {str(e)}")
with col2:
if st.button("β¬οΈ Zip All"):
zip_name = create_zip_of_files(
md_files,
mp3_files,
wav_files,
st.session_state.get('last_query', '')
)
if zip_name:
st.sidebar.markdown(
get_download_link(zip_name, "zip"),
unsafe_allow_html=True
)
# Add file filtering options
st.sidebar.markdown("### π Filter Files")
file_search = st.sidebar.text_input("Search files:", "")
file_type_filter = st.sidebar.multiselect(
"File types:",
["Markdown", "Audio"],
default=["Markdown", "Audio"]
)
# Sort files by modification time
all_files.sort(key=os.path.getmtime, reverse=True)
# Filter files based on search and type
filtered_files = []
for f in all_files:
if file_search.lower() in f.lower():
ext = os.path.splitext(f)[1].lower()
if (("Markdown" in file_type_filter and ext == ".md") or
("Audio" in file_type_filter and ext in [".mp3", ".wav"])):
filtered_files.append(f)
# Display filtered files
for f in filtered_files:
fname = os.path.basename(f)
ext = os.path.splitext(fname)[1].lower().strip('.')
emoji = FILE_EMOJIS.get(ext, 'π¦')
# Get file metadata
mod_time = datetime.fromtimestamp(os.path.getmtime(f))
time_str = mod_time.strftime("%Y-%m-%d %H:%M:%S")
file_size = os.path.getsize(f) / 1024 # Size in KB
with st.sidebar.expander(f"{emoji} {fname}"):
st.write(f"**Modified:** {time_str}")
st.write(f"**Size:** {file_size:.1f} KB")
if ext == "md":
try:
with open(f, "r", encoding="utf-8") as file_in:
snippet = file_in.read(200).replace("\n", " ")
if len(snippet) == 200:
snippet += "..."
st.write(snippet)
st.markdown(
get_download_link(f, file_type="md"),
unsafe_allow_html=True
)
except Exception as e:
st.error(f"Error reading markdown file: {str(e)}")
elif ext in ["mp3", "wav"]:
st.audio(f)
st.markdown(
get_download_link(f, file_type=ext),
unsafe_allow_html=True
)
else:
st.markdown(get_download_link(f), unsafe_allow_html=True)
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# 8. MAIN APPLICATION
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def main():
"""Main application entry point with enhanced UI and error handling."""
try:
# 1. Setup marquee UI in sidebar
update_marquee_settings_ui()
marquee_settings = get_marquee_settings()
# 2. Display welcome marquee
display_marquee(
st.session_state['marquee_content'],
{**marquee_settings, "font-size": "28px", "lineHeight": "50px"},
key_suffix="welcome"
)
# 3. Main action tabs
tab_main = st.radio(
"Action:",
["π€ Voice", "πΈ Media", "π ArXiv", "π Editor"],
horizontal=True
)
# Custom component usage
mycomponent = components.declare_component(
"mycomponent",
path="mycomponent"
)
val = mycomponent(my_input_value="Hello")
if val:
# Process input value
val_stripped = val.replace('\\n', ' ')
edited_input = st.text_area(
"βοΈ Edit Input:",
value=val_stripped,
height=100
)
# Model selection and options
run_option = st.selectbox("Model:", ["Arxiv"])
col1, col2 = st.columns(2)
with col1:
#autorun = st.checkbox("β AutoRun", value=True)
autorun = st.checkbox("β AutoRun", value=False)
with col2:
full_audio = st.checkbox("π FullAudio", value=False)
# Check for input changes
input_changed = (val != st.session_state.old_val)
if autorun and input_changed:
st.session_state.old_val = val
st.session_state.last_query = edited_input
result, timings = perform_ai_lookup(
edited_input,
vocal_summary=True,
extended_refs=False,
titles_summary=True,
full_audio=full_audio
)
# Display performance metrics
display_performance_metrics(timings)
else:
if st.button("βΆ Run"):
st.session_state.old_val = val
st.session_state.last_query = edited_input
result, timings = perform_ai_lookup(
edited_input,
vocal_summary=True,
extended_refs=False,
titles_summary=True,
full_audio=full_audio
)
# Display performance metrics
display_performance_metrics(timings)
# Tab-specific content
if tab_main == "π ArXiv":
display_arxiv_tab()
elif tab_main == "π€ Voice":
display_voice_tab()
elif tab_main == "πΈ Media":
display_media_tab()
elif tab_main == "π Editor":
display_editor_tab()
# Display file history
display_file_history_in_sidebar()
# Apply styling
apply_custom_styling()
# Check for rerun
if st.session_state.should_rerun:
st.session_state.should_rerun = False
st.rerun()
except Exception as e:
st.error(f"An error occurred in the main application: {str(e)}")
st.info("Please try refreshing the page or contact support if the issue persists.")
if __name__ == "__main__":
main() |