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import os
import tempfile
import gradio as gr
import re
import sys
import shutil
# 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:
import whisper
WHISPER_AVAILABLE = True
except ImportError as e:
WHISPER_AVAILABLE = False
print(f"whisper import error: {e}")
print(f"Python version: {sys.version}")
print(f"yt-dlp available: {YT_DLP_AVAILABLE}")
print(f"whisper available: {WHISPER_AVAILABLE}")
def download_audio(url, cookies_file_path=None):
"""Download audio from YouTube URL and return the file path"""
if not YT_DLP_AVAILABLE:
raise Exception("yt-dlp is not available. Please check the installation.")
try:
# Create a temporary directory for downloads
temp_dir = tempfile.mkdtemp()
output_path = os.path.join(temp_dir, "audio")
ydl_opts = {
'format': 'bestaudio[ext=m4a]/bestaudio/best',
'outtmpl': output_path + '.%(ext)s',
'quiet': True,
'no_warnings': True,
}
# Add cookies file if provided
if cookies_file_path and os.path.exists(cookies_file_path):
ydl_opts['cookiefile'] = cookies_file_path
print(f"Using cookies file: {cookies_file_path}")
with YoutubeDL(ydl_opts) as ydl:
info_dict = ydl.extract_info(url, download=True)
filename = ydl.prepare_filename(info_dict)
# Find the downloaded file
for ext in ['.m4a', '.webm', '.mp4', '.mp3']:
potential_file = output_path + ext
if os.path.exists(potential_file):
return potential_file
raise FileNotFoundError(f"Downloaded audio file not found")
except Exception as e:
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 check the installation.")
try:
# Use the smallest model to reduce memory usage
model = whisper.load_model("tiny")
result = model.transcribe(file_path)
return result["text"]
except Exception as e:
raise Exception(f"Failed to transcribe audio: {str(e)}")
def extract_stock_info_simple(text):
"""Extract stock information using simple pattern matching"""
try:
stock_info = []
# Simple patterns to look for stock-related information
stock_patterns = [
r'\b[A-Z]{1,5}\b(?:\s+stock|\s+shares|\s+symbol)', # Stock symbols
r'(?:buy|sell|target|price)\s+[A-Z]{1,5}',
r'\$\d+(?:\.\d{2})?', # Dollar amounts
r'\b(?:bullish|bearish|buy|sell|hold)\b',
]
# Look for company names and stock mentions
companies = re.findall(r'\b[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*(?:\s+(?:Inc|Corp|Company|Ltd)\.?)?', text)
symbols = re.findall(r'\b[A-Z]{2,5}\b', text)
prices = re.findall(r'\$\d+(?:\.\d{2})?', text)
actions = re.findall(r'\b(?:buy|sell|hold|bullish|bearish|target|stop\s+loss)\b', text, re.IGNORECASE)
# Format the extracted information
result = "=== EXTRACTED STOCK INFORMATION ===\n\n"
if companies:
result += f"π Mentioned Companies: {', '.join(set(companies[:10]))}\n\n"
if symbols:
result += f"π€ Potential Stock Symbols: {', '.join(set(symbols[:10]))}\n\n"
if prices:
result += f"π° Price Mentions: {', '.join(set(prices[:10]))}\n\n"
if actions:
result += f"π Trading Actions: {', '.join(set(actions[:10]))}\n\n"
# Look for specific recommendation patterns
recommendations = []
sentences = text.split('.')
for sentence in sentences:
if any(word in sentence.lower() for word in ['buy', 'sell', 'target', 'recommendation']):
if any(symbol in sentence for symbol in symbols[:5]):
recommendations.append(sentence.strip())
if recommendations:
result += "π― Potential Recommendations:\n"
for rec in recommendations[:5]:
result += f"β’ {rec}\n"
if not any([companies, symbols, prices, actions]):
result += "β οΈ No clear stock recommendations found in the transcript.\n"
result += "This might be because:\n"
result += "β’ The video doesn't contain stock recommendations\n"
result += "β’ The audio quality was poor\n"
result += "β’ The content is not in English\n"
return result
except Exception as e:
return f"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 to temp location
shutil.copy2(cookies_file.name, temp_cookies_path)
return temp_cookies_path
except Exception as e:
print(f"Error processing cookies file: {e}")
return None
def process_video(url, cookies_file, progress=gr.Progress()):
"""Main function to process YouTube video"""
# Check if required packages are available
if not YT_DLP_AVAILABLE:
return "Error: yt-dlp is not installed properly. Please check the requirements.", "", "β Error: Missing yt-dlp"
if not WHISPER_AVAILABLE:
return "Error: OpenAI Whisper is not installed properly. Please check the requirements.", "", "β Error: Missing Whisper"
if not url or not url.strip():
return "Please provide a valid YouTube URL", "", "β Error: Invalid URL"
audio_path = None
cookies_temp_path = None
try:
# Validate URL
if not any(domain in url.lower() for domain in ['youtube.com', 'youtu.be']):
return "Please provide a valid YouTube URL", "", "β Error: Invalid URL"
# Process cookies file if provided
progress(0.05, desc="Processing cookies...")
cookies_temp_path = process_cookies_file(cookies_file)
status_msg = "β
Cookies loaded" if cookies_temp_path else "β οΈ No cookies (may encounter bot detection)"
# Download audio
progress(0.2, desc="Downloading audio...")
audio_path = download_audio(url, cookies_temp_path)
# Transcribe audio
progress(0.6, desc="Transcribing audio...")
transcript = transcribe_audio(audio_path)
if not transcript.strip():
return "No speech detected in the video", "", "β No speech detected"
# Extract stock information
progress(0.9, desc="Extracting stock information...")
stock_details = extract_stock_info_simple(transcript)
progress(1.0, desc="Complete!")
return transcript, stock_details, "β
Processing completed successfully"
except Exception as e:
error_msg = f"Error processing video: {str(e)}"
return error_msg, "", f"β Error: {str(e)}"
finally:
# Clean up temporary files
cleanup_file(audio_path)
cleanup_file(cookies_temp_path)
# Create Gradio interface
with gr.Blocks(
title="Stock Recommendation Extractor",
theme=gr.themes.Soft(),
css="""
.gradio-container {
max-width: 1400px;
margin: auto;
}
.status-box {
padding: 10px;
border-radius: 5px;
margin: 10px 0;
}
"""
) as demo:
gr.Markdown("""
# π Stock Recommendation Extractor from YouTube
Extract stock recommendations and trading information from YouTube videos using AI transcription.
**How it works:**
1. Upload your cookies.txt file (optional but recommended to avoid bot detection)
2. Paste YouTube video URL
3. Downloads audio from YouTube video
4. Transcribes using OpenAI Whisper
5. Extracts stock-related information
**β οΈ Disclaimer:** This is for educational purposes only. Always do your own research!
""")
with gr.Row():
with gr.Column(scale=1):
# Cookies file upload
cookies_input = gr.File(
label="πͺ Upload Cookies File (cookies.txt)",
file_types=[".txt"],
file_count="single"
)
gr.Markdown("""
**How to get cookies.txt:**
1. Install browser extension like "Get cookies.txt LOCALLY"
2. Visit YouTube in your browser (logged in)
3. Export cookies for youtube.com
4. Upload the downloaded cookies.txt file here
""")
url_input = gr.Textbox(
label="πΊ YouTube URL",
placeholder="https://www.youtube.com/watch?v=...",
lines=2
)
process_btn = gr.Button(
"π Extract Stock Information",
variant="primary",
size="lg"
)
# Status display
status_output = gr.Textbox(
label="π Status",
lines=1,
interactive=False
)
gr.Markdown("""
### π‘ Tips:
- Upload cookies.txt to avoid bot detection
- Works best with financial YouTube channels
- Ensure video has clear audio
- English content works best
""")
with gr.Row():
with gr.Column():
transcript_output = gr.Textbox(
label="π Full Transcript",
lines=15,
max_lines=20,
show_copy_button=True
)
with gr.Column():
stock_info_output = gr.Textbox(
label="π Extracted Stock Information",
lines=15,
max_lines=20,
show_copy_button=True
)
# Event handlers
process_btn.click(
fn=process_video,
inputs=[url_input, cookies_input],
outputs=[transcript_output, stock_info_output, status_output],
show_progress=True
)
# Example section
gr.Markdown("### π Example URLs (Replace with actual financial videos)")
gr.Examples(
examples=[
["https://www.youtube.com/watch?v=dQw4w9WgXcQ"],
],
inputs=[url_input],
label="Click to try example"
)
gr.Markdown("""
### π§ Troubleshooting:
- **Bot Detection Error**: Upload your cookies.txt file
- **No Audio Found**: Check if video has audio track
- **Transcription Failed**: Video might be too long or audio quality poor
- **No Stock Info**: Video might not contain financial content
""")
if __name__ == "__main__":
demo.launch() |