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import os
import tempfile
import gradio as gr
import re
import sys
import shutil
import importlib.util
import time
import random
def check_requirements():
"""Check if all required packages are installed and return status"""
requirements_status = []
packages = [
('gradio', 'gradio'),
('yt-dlp', 'yt_dlp'),
('openai-whisper', 'whisper'),
('torch', 'torch'),
('torchaudio', 'torchaudio'),
('numpy', 'numpy'),
('regex', 'regex'),
]
for package_name, import_name in packages:
try:
spec = importlib.util.find_spec(import_name)
if spec is None:
requirements_status.append(f"β {package_name}: Not found")
continue
module = importlib.import_module(import_name)
version = getattr(module, '__version__', 'Unknown version')
requirements_status.append(f"β
{package_name}: {version}")
except ImportError as e:
requirements_status.append(f"β {package_name}: Import error - {str(e)}")
except Exception as e:
requirements_status.append(f"β οΈ {package_name}: Found but error - {str(e)}")
# Add Python info
requirements_status.append(f"\nπ Python: {sys.version}")
requirements_status.append(f"π Python executable: {sys.executable}")
return "\n".join(requirements_status)
# Try to import required packages with error handling
try:
from yt_dlp import YoutubeDL
YT_DLP_AVAILABLE = True
except ImportError as e:
YT_DLP_AVAILABLE = False
print(f"yt-dlp import error: {e}")
# Try multiple whisper import methods
WHISPER_AVAILABLE = False
WHISPER_TYPE = None
try:
import whisper
WHISPER_AVAILABLE = True
WHISPER_TYPE = "openai-whisper"
print("Using OpenAI Whisper")
except ImportError as e:
print(f"OpenAI Whisper import error: {e}")
try:
from transformers import pipeline
WHISPER_AVAILABLE = True
WHISPER_TYPE = "transformers"
print("Using Transformers Whisper")
except ImportError as e2:
print(f"Transformers Whisper import error: {e2}")
print(f"Python version: {sys.version}")
print(f"Python executable: {sys.executable}")
print(f"yt-dlp available: {YT_DLP_AVAILABLE}")
print(f"whisper available: {WHISPER_AVAILABLE} (type: {WHISPER_TYPE})")
def get_video_info(url, cookies_file_path=None):
"""Get video information without downloading"""
if not YT_DLP_AVAILABLE:
raise Exception("yt-dlp is not available.")
ydl_opts = {
'quiet': True,
'no_warnings': True,
'extract_flat': False,
'skip_download': True,
}
if cookies_file_path and os.path.exists(cookies_file_path):
ydl_opts['cookiefile'] = cookies_file_path
with YoutubeDL(ydl_opts) as ydl:
try:
info = ydl.extract_info(url, download=False)
return {
'title': info.get('title', 'Unknown'),
'duration': info.get('duration', 0),
'availability': info.get('availability', 'unknown'),
'live_status': info.get('live_status', 'unknown'),
}
except Exception as e:
return {'error': str(e)}
def download_audio(url, cookies_file_path=None):
"""Download audio from YouTube URL with enhanced error handling"""
if not YT_DLP_AVAILABLE:
raise Exception("yt-dlp is not available. Please check the installation.")
try:
# First, try to get video info
video_info = get_video_info(url, cookies_file_path)
if 'error' in video_info:
raise Exception(f"Video info error: {video_info['error']}")
print(f"Video title: {video_info.get('title', 'Unknown')}")
print(f"Video duration: {video_info.get('duration', 0)} seconds")
print(f"Video availability: {video_info.get('availability', 'unknown')}")
# Create a temporary directory for downloads
temp_dir = tempfile.mkdtemp()
output_path = os.path.join(temp_dir, "audio")
# Enhanced options for better compatibility
ydl_opts = {
'format': 'bestaudio[ext=m4a]/bestaudio[ext=webm]/bestaudio[ext=mp4]/bestaudio/best',
'outtmpl': output_path + '.%(ext)s',
'quiet': False, # Enable logging for debugging
'no_warnings': False,
'extractor_retries': 5,
'fragment_retries': 5,
'retry_sleep_functions': {'http': lambda n: min(2 ** n, 60)},
'socket_timeout': 30,
'http_chunk_size': 10485760, # 10MB chunks
'writeinfojson': False,
'writesubtitles': False,
'writeautomaticsub': False,
'geo_bypass': True,
'geo_bypass_country': 'US',
'extract_flat': False,
'ignoreerrors': False,
}
# Enhanced cookies and headers handling
if cookies_file_path and os.path.exists(cookies_file_path):
ydl_opts['cookiefile'] = cookies_file_path
print(f"β
Using cookies file: {cookies_file_path}")
else:
print("β οΈ No cookies file - using enhanced headers")
# Always add enhanced headers
ydl_opts.update({
'user_agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
'referer': 'https://www.youtube.com/',
'headers': {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8',
'Accept-Language': 'en-US,en;q=0.9',
'Accept-Encoding': 'gzip, deflate, br',
'DNT': '1',
'Connection': 'keep-alive',
'Upgrade-Insecure-Requests': '1',
'Sec-Fetch-Dest': 'document',
'Sec-Fetch-Mode': 'navigate',
'Sec-Fetch-Site': 'none',
'Sec-Fetch-User': '?1',
'sec-ch-ua': '"Not_A Brand";v="8", "Chromium";v="120", "Google Chrome";v="120"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"Windows"',
}
})
# Add random delay to avoid rate limiting
time.sleep(random.uniform(1, 3))
with YoutubeDL(ydl_opts) as ydl:
print(f"Attempting to download audio from: {url}")
info_dict = ydl.extract_info(url, download=True)
# Find the downloaded file
for ext in ['.m4a', '.webm', '.mp4', '.mp3', '.aac', '.opus']:
potential_file = output_path + ext
if os.path.exists(potential_file):
print(f"β
Audio downloaded: {potential_file}")
return potential_file
# If no file found, list directory contents for debugging
print(f"Files in temp directory: {os.listdir(temp_dir)}")
raise FileNotFoundError("Downloaded audio file not found")
except Exception as e:
error_msg = str(e).lower()
# Provide specific error messages and solutions
if "video unavailable" in error_msg or "content isn't available" in error_msg:
raise Exception(f"""
β Video Access Error: The video is unavailable or restricted.
Possible reasons:
β’ Video is private, unlisted, or deleted
β’ Video is geo-blocked in your region
β’ Video has age restrictions
β’ Video requires sign-in to view
β’ Copyright restrictions
Solutions to try:
1. Verify the video URL is correct and accessible
2. Try a different public video
3. Check if the video works in your browser
4. If using a playlist URL, try the direct video URL instead
5. For age-restricted videos, ensure cookies are from a logged-in account
Original error: {str(e)}
""")
elif "403" in error_msg or "forbidden" in error_msg:
raise Exception(f"""
β Access Forbidden (403): YouTube blocked the request.
Solutions:
1. **Upload fresh cookies.txt file** (most important)
2. Get cookies from a logged-in YouTube account
3. Try again after a few minutes (rate limiting)
4. Use a different network/VPN if possible
How to get fresh cookies:
β’ Visit YouTube while logged in
β’ Use browser extension to export cookies
β’ Upload the newest cookies.txt file
Original error: {str(e)}
""")
elif "429" in error_msg or "rate limit" in error_msg:
raise Exception(f"""
β Rate Limited (429): Too many requests.
Solutions:
1. Wait 10-15 minutes before trying again
2. Upload fresh cookies.txt file
3. Try a different video
4. Use a different network if possible
Original error: {str(e)}
""")
else:
raise Exception(f"Failed to download audio: {str(e)}")
def transcribe_audio(file_path):
"""Transcribe audio file using Whisper"""
if not WHISPER_AVAILABLE:
raise Exception("OpenAI Whisper is not available. Please install it using: pip install openai-whisper")
try:
if WHISPER_TYPE == "openai-whisper":
# Use OpenAI Whisper with more robust settings
model = whisper.load_model("base") # Use base model for better accuracy
result = model.transcribe(
file_path,
language="en", # Specify English for better performance
task="transcribe",
verbose=False,
fp16=False, # Better compatibility
temperature=0.0, # More deterministic
)
return result["text"]
elif WHISPER_TYPE == "transformers":
# Use Transformers Whisper
from transformers import pipeline
transcriber = pipeline(
"automatic-speech-recognition",
model="openai/whisper-base",
device=-1 # Use CPU for better compatibility
)
result = transcriber(file_path, return_timestamps=False)
return result["text"]
else:
raise Exception("No compatible Whisper installation found")
except Exception as e:
raise Exception(f"Failed to transcribe audio: {str(e)}")
def extract_stock_info_enhanced(text):
"""Enhanced stock information extraction with better patterns"""
try:
stock_info = []
# Enhanced patterns for stock information
stock_patterns = {
'symbols': r'\b[A-Z]{2,5}\b(?=\s+(?:stock|shares|ticker|symbol|price|target|buy|sell))',
'prices': r'\$\d+(?:\.\d{1,2})?(?:\s*(?:per share|each|target|price))?',
'percentages': r'\d+(?:\.\d{1,2})?%',
'actions': r'\b(?:buy|sell|hold|long|short|bullish|bearish|target|stop loss|take profit|accumulate|distribute)\b',
'companies': r'\b[A-Z][a-zA-Z]+(?:\s+[A-Z][a-zA-Z]+){0,2}(?:\s+(?:Inc|Corp|Company|Ltd|LLC)\.?)?',
'market_terms': r'\b(?:earnings|revenue|profit|loss|growth|dividend|yield|PE ratio|market cap|volume)\b',
}
# Extract information
symbols = re.findall(stock_patterns['symbols'], text, re.IGNORECASE)
prices = re.findall(stock_patterns['prices'], text)
percentages = re.findall(stock_patterns['percentages'], text)
actions = re.findall(stock_patterns['actions'], text, re.IGNORECASE)
companies = re.findall(stock_patterns['companies'], text)
market_terms = re.findall(stock_patterns['market_terms'], text, re.IGNORECASE)
# Format results
result = "=== π EXTRACTED STOCK INFORMATION ===\n\n"
if symbols:
result += f"π€ **Stock Symbols Found**: {', '.join(set(symbols[:10]))}\n\n"
if companies:
filtered_companies = [c for c in set(companies) if len(c) > 3 and c.upper() not in ['THE', 'AND', 'FOR', 'WITH']]
if filtered_companies:
result += f"π’ **Companies Mentioned**: {', '.join(filtered_companies[:10])}\n\n"
if prices:
result += f"π° **Price Mentions**: {', '.join(set(prices[:10]))}\n\n"
if percentages:
result += f"π **Percentage Mentions**: {', '.join(set(percentages[:10]))}\n\n"
if actions:
result += f"π― **Trading Actions**: {', '.join(set(actions[:10]))}\n\n"
if market_terms:
result += f"π **Market Terms**: {', '.join(set(market_terms[:10]))}\n\n"
# Look for recommendation sentences
sentences = [s.strip() for s in text.split('.') if s.strip()]
recommendations = []
for sentence in sentences:
sentence_lower = sentence.lower()
if any(action in sentence_lower for action in ['buy', 'sell', 'target', 'recommend', 'suggest']):
if any(symbol in sentence for symbol in symbols[:5]) or any(price in sentence for price in prices[:3]):
recommendations.append(sentence)
if recommendations:
result += "π― **Potential Recommendations**:\n"
for i, rec in enumerate(recommendations[:5], 1):
result += f"{i}. {rec}\n"
result += "\n"
# Add summary
if not any([symbols, prices, actions, recommendations]):
result += "β οΈ **No clear stock recommendations found**\n\n"
result += "**Possible reasons:**\n"
result += "β’ Video doesn't contain stock/financial content\n"
result += "β’ Audio quality was poor for transcription\n"
result += "β’ Content is not in English\n"
result += "β’ General market discussion without specific recommendations\n"
else:
result += "β
**Analysis Complete** - Please verify all information independently!\n"
result += "\n" + "="*50 + "\n"
result += "β οΈ **DISCLAIMER**: This is automated extraction for educational purposes only.\n"
result += "Always conduct your own research before making investment decisions!\n"
result += "="*50
return result
except Exception as e:
return f"β Error extracting stock info: {str(e)}"
def cleanup_file(file_path):
"""Clean up temporary files"""
try:
if file_path and os.path.exists(file_path):
os.remove(file_path)
# Also try to remove the directory if it's empty
try:
os.rmdir(os.path.dirname(file_path))
except:
pass
except:
pass
def process_cookies_file(cookies_file):
"""Process uploaded cookies file and return the path"""
if cookies_file is None:
return None
try:
# Create a temporary file for cookies
temp_cookies_path = tempfile.mktemp(suffix='.txt')
# Copy the uploaded file
shutil.copy2(cookies_file, temp_cookies_path)
# Validate cookies file
with open(temp_cookies_path, 'r', encoding='utf-8') as f:
content = f.read()
if 'youtube.com' not in content.lower():
print("β οΈ Warning: cookies file might not contain YouTube cookies")
print(f"β
Cookies file processed: {temp_cookies_path}")
return temp_cookies_path
except Exception as e:
print(f"β Error processing cookies file: {e}")
return None
def validate_youtube_url(url):
"""Validate YouTube URL format"""
if not url or not url.strip():
return False, "Please provide a YouTube URL"
url = url.strip()
youtube_patterns = [
r'(?:https?://)?(?:www\.)?youtube\.com/watch\?v=[\w-]+',
r'(?:https?://)?(?:www\.)?youtu\.be/[\w-]+',
r'(?:https?://)?(?:www\.)?youtube\.com/embed/[\w-]+',
r'(?:https?://)?(?:m\.)?youtube\.com/watch\?v=[\w-]+',
]
for pattern in youtube_patterns:
if re.match(pattern, url):
return True, "Valid YouTube URL"
return False, "Invalid YouTube URL format"
def process_video(url, cookies_file, progress=gr.Progress()):
"""Main function to process YouTube video with detailed debugging"""
# Detailed debugging info
debug_info = []
debug_info.append(f"π Starting process at {time.strftime('%H:%M:%S')}")
debug_info.append(f"π‘ Python version: {sys.version.split()[0]}")
debug_info.append(f"π¦ yt-dlp available: {YT_DLP_AVAILABLE}")
debug_info.append(f"ποΈ Whisper available: {WHISPER_AVAILABLE} (type: {WHISPER_TYPE})")
# Check if required packages are available
if not YT_DLP_AVAILABLE:
error_msg = "β ERROR: yt-dlp is not installed properly.\n\n"
error_msg += "SOLUTION: Install yt-dlp using:\n"
error_msg += "pip install yt-dlp\n\n"
error_msg += "DEBUG INFO:\n" + "\n".join(debug_info)
return error_msg, "", "β Missing yt-dlp"
if not WHISPER_AVAILABLE:
error_msg = "β ERROR: OpenAI Whisper is not installed properly.\n\n"
error_msg += "SOLUTION: Install Whisper using:\n"
error_msg += "pip install openai-whisper\n"
error_msg += "OR\n"
error_msg += "pip install transformers torch torchaudio\n\n"
error_msg += "DEBUG INFO:\n" + "\n".join(debug_info)
return error_msg, "", "β Missing Whisper"
# Validate URL
is_valid, validation_msg = validate_youtube_url(url)
if not is_valid:
error_msg = f"β ERROR: {validation_msg}\n\n"
error_msg += f"PROVIDED URL: {url}\n\n"
error_msg += "VALID URL FORMATS:\n"
error_msg += "β’ https://www.youtube.com/watch?v=VIDEO_ID\n"
error_msg += "β’ https://youtu.be/VIDEO_ID\n"
error_msg += "β’ https://www.youtube.com/embed/VIDEO_ID\n\n"
error_msg += "DEBUG INFO:\n" + "\n".join(debug_info)
return error_msg, "", "β Invalid URL"
audio_path = None
cookies_temp_path = None
try:
progress(0.05, desc="π Validating URL...")
debug_info.append(f"β
URL validation passed: {url}")
# Process cookies file if provided
progress(0.1, desc="πͺ Processing cookies...")
cookies_temp_path = process_cookies_file(cookies_file)
if cookies_temp_path:
debug_info.append(f"β
Cookies processed: {cookies_temp_path}")
else:
debug_info.append("β οΈ No cookies provided - this may cause access errors")
status_msg = "β
Cookies loaded" if cookies_temp_path else "β οΈ No cookies (may encounter restrictions)"
# First, try to get video info for debugging
progress(0.15, desc="π Checking video accessibility...")
try:
video_info = get_video_info(url, cookies_temp_path)
if 'error' in video_info:
debug_info.append(f"β Video info error: {video_info['error']}")
raise Exception(f"Video accessibility check failed: {video_info['error']}")
else:
debug_info.append(f"β
Video info: {video_info}")
except Exception as e:
debug_info.append(f"β Video info check failed: {str(e)}")
# Continue anyway, but log the issue
# Download audio
progress(0.2, desc="π₯ Downloading audio...")
debug_info.append("π Starting audio download...")
audio_path = download_audio(url, cookies_temp_path)
debug_info.append(f"β
Audio downloaded: {audio_path}")
# Check if audio file exists and get size
if audio_path and os.path.exists(audio_path):
file_size = os.path.getsize(audio_path)
debug_info.append(f"π Audio file size: {file_size/1024/1024:.2f} MB")
else:
raise Exception("Audio file not found after download")
# Transcribe audio
progress(0.6, desc="ποΈ Transcribing audio...")
debug_info.append("π Starting transcription...")
transcript = transcribe_audio(audio_path)
debug_info.append(f"β
Transcription completed: {len(transcript)} characters")
if not transcript.strip():
error_msg = "β ERROR: No speech detected in the video\n\n"
error_msg += "POSSIBLE CAUSES:\n"
error_msg += "β’ Video has no audio track\n"
error_msg += "β’ Audio is too quiet or unclear\n"
error_msg += "β’ Video is not in English\n"
error_msg += "β’ Audio file is corrupted\n\n"
error_msg += "DEBUG INFO:\n" + "\n".join(debug_info)
return error_msg, "", "β No speech detected"
# Extract stock information
progress(0.9, desc="π Analyzing content...")
debug_info.append("π Starting stock analysis...")
stock_details = extract_stock_info_enhanced(transcript)
debug_info.append("β
Stock analysis completed")
progress(1.0, desc="β
Complete!")
# Add debug info to transcript
debug_section = "\n\n" + "="*50 + "\n"
debug_section += "π DEBUG INFORMATION\n"
debug_section += "="*50 + "\n"
debug_section += "\n".join(debug_info)
return transcript + debug_section, stock_details, "β
Processing completed successfully"
except Exception as e:
error_msg = f"β DETAILED ERROR INFORMATION:\n\n"
error_msg += f"ERROR MESSAGE: {str(e)}\n\n"
error_msg += f"ERROR TYPE: {type(e).__name__}\n\n"
# Add context based on where the error occurred
if "download" in str(e).lower():
error_msg += "π§ DOWNLOAD TROUBLESHOOTING:\n"
error_msg += "β’ Check if video URL is accessible in browser\n"
error_msg += "β’ Upload fresh cookies.txt file\n"
error_msg += "β’ Try a different video\n"
error_msg += "β’ Wait 10-15 minutes if rate limited\n\n"
elif "transcribe" in str(e).lower():
error_msg += "π§ TRANSCRIPTION TROUBLESHOOTING:\n"
error_msg += "β’ Check if audio file was downloaded properly\n"
error_msg += "β’ Ensure video has clear audio\n"
error_msg += "β’ Try a shorter video\n\n"
error_msg += "π PROCESSING STEPS COMPLETED:\n"
error_msg += "\n".join(debug_info)
return error_msg, "", f"β Error: {type(e).__name__}"
finally:
# Clean up temporary files
if audio_path:
debug_info.append(f"ποΈ Cleaning up: {audio_path}")
cleanup_file(audio_path)
if cookies_temp_path:
debug_info.append(f"ποΈ Cleaning up: {cookies_temp_path}")
cleanup_file(cookies_temp_path)
# Create Gradio interface optimized for Gradio Cloud
with gr.Blocks(
title="π YouTube Stock Extractor",
theme=gr.themes.Soft(),
css="""
.gradio-container {
max-width: 1200px;
margin: auto;
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
}
.status-box {
padding: 12px;
border-radius: 8px;
margin: 10px 0;
border: 1px solid #ddd;
}
.warning-box {
background-color: #fff3cd;
border-color: #ffeaa7;
color: #856404;
}
.success-box {
background-color: #d4edda;
border-color: #c3e6cb;
color: #155724;
}
.error-box {
background-color: #f8d7da;
border-color: #f5c6cb;
color: #721c24;
}
"""
) as demo:
gr.Markdown("""
# π YouTube Stock Recommendation Extractor
**Extract stock analysis and trading recommendations from YouTube videos using AI**
π§ **How it works:**
1. **Upload cookies.txt** (essential for avoiding restrictions)
2. **Paste YouTube URL** of financial content
3. **AI downloads** audio and transcribes using Whisper
4. **Extracts** stock symbols, prices, and recommendations
β οΈ **Important:** This tool is for educational purposes only. Always do your own research before investing!
""")
with gr.Row():
with gr.Column(scale=1):
# System check section
with gr.Group():
gr.Markdown("### π System Status")
check_req_btn = gr.Button(
"Check System Requirements",
variant="secondary",
size="sm"
)
requirements_output = gr.Textbox(
label="π System Requirements Status",
lines=8,
max_lines=15,
interactive=False,
visible=False
)
# Input section
with gr.Group():
gr.Markdown("### π₯ Input")
# Add a test button first
test_btn = gr.Button(
"π§ͺ Test System (Click First!)",
variant="secondary",
size="sm"
)
test_output = gr.Textbox(
label="π§ͺ System Test Results",
lines=5,
visible=False,
interactive=False
)
# Cookies upload with better instructions
cookies_input = gr.File(
label="πͺ Upload Cookies File (cookies.txt) - HIGHLY RECOMMENDED",
file_types=[".txt"],
file_count="single"
)
with gr.Accordion("π How to Get Cookies (Click to expand)", open=False):
gr.Markdown("""
**Why cookies are needed:** YouTube blocks most automated requests without proper authentication.
**Step-by-step instructions:**
1. **Install browser extension:**
- Chrome: "Get cookies.txt LOCALLY" or "cookies.txt"
- Firefox: "cookies.txt" or "Export Cookies"
2. **Get cookies:**
- Visit YouTube.com (log in if needed)
- Click the extension icon
- Select "Export for youtube.com"
- Download the cookies.txt file
3. **Upload here:** Use the file upload above
**β οΈ Without cookies, you'll get "403 Forbidden" or "Video unavailable" errors**
""")
url_input = gr.Textbox(
label="πΊ YouTube Video URL",
placeholder="https://www.youtube.com/watch?v=VIDEO_ID",
lines=2,
info="Paste the full YouTube video URL here"
)
process_btn = gr.Button(
"π Extract Stock Information",
variant="primary",
size="lg"
)
# Status display
status_output = gr.Textbox(
label="π Status",
lines=3,
interactive=False,
info="Current processing status"
)
# Output section
with gr.Row():
with gr.Column():
transcript_output = gr.Textbox(
label="π Full Transcript",
lines=20,
max_lines=25,
show_copy_button=True,
info="Complete transcription of the video audio"
)
with gr.Column():
stock_info_output = gr.Textbox(
label="π Extracted Stock Information",
lines=20,
max_lines=25,
show_copy_button=True,
info="Parsed stock symbols, prices, and recommendations"
)
# Example and troubleshooting section
with gr.Row():
with gr.Column():
gr.Markdown("### π Example URLs")
gr.Examples(
examples=[
["https://www.youtube.com/watch?v=dQw4w9WgXcQ"],
["https://youtu.be/dQw4w9WgXcQ"],
],
inputs=[url_input],
label="Click to try example URLs (replace with actual financial videos)"
)
# Troubleshooting section
with gr.Accordion("π§ Troubleshooting Guide", open=False):
gr.Markdown("""
### Common Issues and Solutions:
**β "Video unavailable" or "Content isn't available":**
- Video might be private, deleted, or geo-blocked
- Try a different public financial video
- Verify the URL works in your browser
- Check if video requires age verification
**β "403 Forbidden" error:**
- **Upload fresh cookies.txt file** (most common fix)
- Make sure cookies are from a logged-in YouTube account
- Try waiting 10-15 minutes (rate limiting)
**β "No speech detected":**
- Video might not have clear audio
- Try videos with clear narration
- Check if video is in English
**β "No stock information found":**
- Video might not contain financial content
- Try videos from financial YouTube channels
- Look for videos with stock analysis or recommendations
### Installation Commands:
```bash
# Install all requirements
pip install gradio yt-dlp openai-whisper torch torchaudio
# Alternative whisper installation
pip install transformers torch torchaudio
```
### Best Practices:
- Use videos from reputable financial channels
- Prefer videos under 20 minutes for faster processing
- Ensure clear audio quality
- Always verify extracted information independently
""")
# Event handlers
def show_requirements():
status = check_requirements()
return gr.update(value=status, visible=True)
def test_system():
"""Test system components and return detailed status"""
test_results = []
test_results.append("π§ͺ SYSTEM TEST RESULTS")
test_results.append("="*30)
# Test imports
test_results.append(f"β
yt-dlp: {'Available' if YT_DLP_AVAILABLE else 'NOT AVAILABLE'}")
test_results.append(f"β
Whisper: {'Available' if WHISPER_AVAILABLE else 'NOT AVAILABLE'} (Type: {WHISPER_TYPE})")
# Test yt-dlp functionality
if YT_DLP_AVAILABLE:
try:
from yt_dlp import YoutubeDL
test_ydl = YoutubeDL({'quiet': True})
test_results.append("β
yt-dlp: Can create YoutubeDL instance")
except Exception as e:
test_results.append(f"β yt-dlp: Error creating instance - {str(e)}")
# Test Whisper functionality
if WHISPER_AVAILABLE:
try:
if WHISPER_TYPE == "openai-whisper":
import whisper
test_results.append("β
Whisper: OpenAI Whisper can be imported")
elif WHISPER_TYPE == "transformers":
from transformers import pipeline
test_results.append("β
Whisper: Transformers Whisper can be imported")
except Exception as e:
test_results.append(f"β Whisper: Error testing - {str(e)}")
# Test file operations
try:
temp_file = tempfile.mktemp()
with open(temp_file, 'w') as f:
f.write("test")
os.remove(temp_file)
test_results.append("β
File operations: Working")
except Exception as e:
test_results.append(f"β File operations: Error - {str(e)}")
test_results.append("\nπ‘ If you see any β errors above, install missing packages:")
test_results.append("pip install yt-dlp openai-whisper torch torchaudio")
return gr.update(value="\n".join(test_results), visible=True)
check_req_btn.click(
fn=show_requirements,
outputs=[requirements_output]
)
test_btn.click(
fn=test_system,
outputs=[test_output]
)
process_btn.click(
fn=process_video,
inputs=[url_input, cookies_input],
outputs=[transcript_output, stock_info_output, status_output],
show_progress=True
)
# Footer
gr.Markdown("""
---
**π’ Disclaimer:** This tool is for educational and research purposes only.
The extracted information should not be considered as financial advice.
Always conduct your own research and consult with financial professionals before making investment decisions.
""")
# Launch configuration for Gradio Cloud
if __name__ == "__main__":
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False,
debug=False,
show_error=True,
quiet=False
) |