Add comprehensive logging for debugging Hugging Face deployment

🔍 Enhanced Logging Features:
- Added detailed startup logging with timestamps and component initialization
- Comprehensive sentiment analysis logging with timing information
- Step-by-step progress tracking for each stock analysis
- Error handling with detailed tracebacks and error types
- Performance timing for Reddit/Google News searches
- Sample content logging for debugging data quality

📊 Startup Logging:
- Python version and working directory info
- Alpaca client initialization status
- Sentiment analyzer (VADER/TextBlob) initialization
- Dashboard creation and event handler setup

🧠 Sentiment Analysis Logging:
- Individual stock analysis progress with timing
- News source breakdown (Reddit posts vs Google News articles)
- Sample titles from each source for verification
- Prediction results with confidence levels
- Detailed error handling for failed analyses

⚡ Performance Tracking:
- Time spent on Reddit searches vs Google News
- Total analysis time per stock
- Overall table refresh completion status

This will help identify exactly where the deployment gets stuck during the "Preparing Space" phase.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

app.py

@@ -5,6 +5,7 @@ Beautiful Vercel-style dashboard with VM data integration
 """
 
 import os
+import sys
 import pandas as pd
 import gradio as gr
 import plotly.graph_objects as go
@@ -27,17 +28,58 @@ API_KEY = os.getenv('ALPACA_API_KEY', 'PK2FD9B2S86LHR7ZBHG1')
 SECRET_KEY = os.getenv('ALPACA_SECRET_KEY', 'QPmGPDgbPArvHv6cldBXc7uWddapYcIAnBhtkuBW')
 VM_API_URL = os.getenv('VM_API_URL', 'http://34.56.193.18:8090')  # Set this in Hugging Face
 
-# Configure logging
-logging.basicConfig(level=logging.INFO)
+# Configure detailed logging for debugging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.StreamHandler(),
+    ]
+)
 logger = logging.getLogger(__name__)
 
+# Log startup information
+logger.info("🚀 Starting Premium Trading Dashboard...")
+logger.info(f"Python version: {sys.version}")
+logger.info(f"Working directory: {os.getcwd()}")
+
 # Initialize Alpaca clients
-trading_client = TradingClient(api_key=API_KEY, secret_key=SECRET_KEY)
-data_client = StockHistoricalDataClient(API_KEY, SECRET_KEY)
+logger.info("🔌 Initializing Alpaca trading client...")
+try:
+    trading_client = TradingClient(api_key=API_KEY, secret_key=SECRET_KEY)
+    logger.info("✅ Alpaca trading client initialized successfully")
+except Exception as e:
+    logger.error(f"❌ Failed to initialize Alpaca trading client: {e}")
+    raise
+
+logger.info("📊 Initializing Alpaca data client...")
+try:
+    data_client = StockHistoricalDataClient(API_KEY, SECRET_KEY)
+    logger.info("✅ Alpaca data client initialized successfully")
+except Exception as e:
+    logger.error(f"❌ Failed to initialize Alpaca data client: {e}")
+    raise
 
 # Initialize sentiment analyzers
-vader = SentimentIntensityAnalyzer()
+logger.info("🧠 Initializing sentiment analysis engines...")
+try:
+    vader = SentimentIntensityAnalyzer()
+    logger.info("✅ VADER sentiment analyzer initialized")
+except Exception as e:
+    logger.error(f"❌ Failed to initialize VADER: {e}")
+    raise
+
+try:
+    from textblob import TextBlob
+    # Test TextBlob
+    test_blob = TextBlob("test")
+    logger.info("✅ TextBlob sentiment analyzer initialized")
+except Exception as e:
+    logger.error(f"❌ Failed to initialize TextBlob: {e}")
+    raise
+
 headers = {'User-Agent': 'TradingHistoryBacktester/1.0'}
+logger.info("✅ HTTP headers configured")
 
 # Modern color scheme
 COLORS = {
@@ -353,10 +395,20 @@ def get_order_history():
 
 def refresh_investment_performance_table():
     """Refresh investment performance table with P&L and sentiment analysis for all trading symbols"""
+    logger.info("📊 Starting investment performance table refresh...")
+    
     # Get IPO data and orders
+    logger.info("🔌 Fetching IPO data from VM...")
     ipos = fetch_from_vm('ipos?limit=100', [])
+    logger.info(f"📈 Retrieved {len(ipos)} IPO records from VM")
+    
+    logger.info("📋 Fetching order history from Alpaca...")
     orders = get_order_history()
+    logger.info(f"📝 Retrieved {len(orders)} orders from Alpaca")
+    
+    logger.info("💼 Fetching current positions from Alpaca...")
     positions = get_current_positions()
+    logger.info(f"🏦 Retrieved {len(positions)} current positions")
     
     # Create proper empty DataFrame with correct column names  
     columns = ['Symbol', 'Status', 'IPO Price', 'Buy Price', 'Sell Price', 'Investment', 'P&L ($)', 'P&L (%)', 'Sentiment', 'Predicted', 'Date']
@@ -465,14 +517,21 @@ def refresh_investment_performance_table():
                 pl_percent_str = f"{pl_arrow} <span style='color: {pl_color}; font-weight: 600;'>{abs(pl_percent):.2f}%</span>"
                 
                 # ADD SENTIMENT ANALYSIS FOR EACH STOCK
-                logger.info(f"Running sentiment analysis for {symbol}...")
+                logger.info(f"🧠 Starting sentiment analysis for {symbol}...")
+                start_time = time.time()
                 try:
                     # Get pre-investment news (quick version)
+                    logger.info(f"📰 Gathering pre-investment news for {symbol}...")
                     news_items = get_pre_investment_news(symbol, investment_time, hours_before=12)
+                    logger.info(f"📑 Found {len(news_items)} total news items for {symbol}")
                     
                     # Analyze sentiment
+                    logger.info(f"🔍 Analyzing sentiment for {symbol}...")
                     avg_sentiment, predicted_change, prediction_label, source_breakdown = analyze_pre_investment_sentiment(news_items)
                     
+                    analysis_time = time.time() - start_time
+                    logger.info(f"⚡ Sentiment analysis for {symbol} completed in {analysis_time:.1f}s")
+                    
                     # Format sentiment display
                     if prediction_label == "bullish":
                         sentiment_display = f"<span style='color: #00d647; font-weight: 600;'>🚀 {prediction_label.title()}</span>"
@@ -491,12 +550,27 @@ def refresh_investment_performance_table():
                     
                     reddit_count = len(source_breakdown.get('Reddit', []))
                     news_count = len(source_breakdown.get('Google News', []))
-                    logger.info(f"{symbol} sentiment: {prediction_label} ({predicted_change:+.1f}%) - Reddit: {reddit_count}, News: {news_count}")
+                    logger.info(f"🎯 {symbol} RESULTS: {prediction_label.upper()} ({predicted_change:+.1f}%) | Reddit: {reddit_count} posts | News: {news_count} articles")
+                    
+                    # Log sample titles for debugging
+                    if reddit_count > 0:
+                        sample_reddit = source_breakdown['Reddit'][0]['title'][:50]
+                        logger.info(f"📱 Sample Reddit: {sample_reddit}...")
+                    if news_count > 0:
+                        sample_news = source_breakdown['Google News'][0]['title'][:50]
+                        logger.info(f"📰 Sample News: {sample_news}...")
                     
                 except Exception as e:
-                    logger.warning(f"Sentiment analysis failed for {symbol}: {e}")
+                    analysis_time = time.time() - start_time
+                    logger.error(f"❌ Sentiment analysis failed for {symbol} after {analysis_time:.1f}s: {str(e)}")
+                    logger.error(f"🔍 Error type: {type(e).__name__}")
+                    import traceback
+                    logger.error(f"📋 Traceback: {traceback.format_exc()[:200]}...")
                     sentiment_display = "<span style='color: #8b949e;'>❓ Error</span>"
                     predicted_display = "<span style='color: #8b949e;'>N/A</span>"
+                    
+                    # Continue with next stock instead of failing completely
+                    pass
                 
                 invested_data.append({
                     'Symbol': symbol,
@@ -516,8 +590,10 @@ def refresh_investment_performance_table():
     
     # Sort by date (most recent first)
     invested_data.sort(key=lambda x: x['_sort_date'], reverse=True)
+    logger.info(f"📋 Processed {len(invested_data)} investments with sentiment analysis")
     
     df = pd.DataFrame(invested_data)
+    logger.info(f"✅ Investment performance table refresh completed - {len(df)} rows")
     return df
 
 def refresh_investment_performance_html():
@@ -1313,19 +1389,29 @@ def get_pre_investment_news(symbol, investment_time, hours_before=12):
     cutoff_time = investment_time - timedelta(minutes=30)  # 30 min buffer
     search_start = investment_time - timedelta(hours=hours_before)
     
-    logger.info(f"Getting news for {symbol} between {search_start.strftime('%Y-%m-%d %H:%M')} and {cutoff_time.strftime('%Y-%m-%d %H:%M')}")
+    logger.info(f"🔍 NEWS SEARCH for {symbol}:")
+    logger.info(f"   📅 Time window: {search_start.strftime('%Y-%m-%d %H:%M')} → {cutoff_time.strftime('%Y-%m-%d %H:%M')}")
+    logger.info(f"   ⏰ Search duration: {hours_before} hours before investment")
     
     all_news = []
     
     # Get Reddit posts
+    logger.info(f"🧵 Starting Reddit search for {symbol}...")
+    reddit_start = time.time()
     reddit_posts = get_reddit_pre_investment(symbol, search_start, cutoff_time)
+    reddit_time = time.time() - reddit_start
+    logger.info(f"✅ Reddit search completed in {reddit_time:.1f}s - found {len(reddit_posts)} posts")
     all_news.extend(reddit_posts)
     
     # Get Google News
+    logger.info(f"📰 Starting Google News search for {symbol}...")
+    news_start = time.time()
     google_news = get_google_news_pre_investment(symbol, search_start, cutoff_time)
+    news_time = time.time() - news_start
+    logger.info(f"✅ Google News search completed in {news_time:.1f}s - found {len(google_news)} articles")
     all_news.extend(google_news)
     
-    logger.info(f"Total news sources found: {len(all_news)}")
+    logger.info(f"📊 TOTAL NEWS GATHERED for {symbol}: {len(all_news)} items ({len(reddit_posts)} Reddit + {len(google_news)} News)")
     return all_news
 
 def get_reddit_pre_investment(symbol, start_time, cutoff_time):
@@ -1910,11 +1996,15 @@ custom_css = """
 """
 
 def create_dashboard():
-    with gr.Blocks(
-        title="🚀 Premium Trading Dashboard", 
-        theme=gr.themes.Soft(primary_hue="blue"),
-        css=custom_css
-    ) as demo:
+    logger.info("🎨 Creating Gradio dashboard interface...")
+    
+    try:
+        with gr.Blocks(
+            title="🚀 Premium Trading Dashboard", 
+            theme=gr.themes.Soft(primary_hue="blue"),
+            css=custom_css
+        ) as demo:
+            logger.info("🖼️ Dashboard blocks created successfully")
         
         # Header
         gr.HTML("""
@@ -2096,6 +2186,8 @@ def create_dashboard():
             </div>
         """)
         
+        logger.info("🔗 Setting up event handlers...")
+        
         # Event Handlers
         
         # Portfolio tab
@@ -2322,10 +2414,27 @@ def create_dashboard():
         demo.load(fn=refresh_ipo_discoveries_table, outputs=[ipo_table])
         demo.load(fn=refresh_investment_performance_html, outputs=[investment_performance_table])
     
-    return demo
+        logger.info("✅ All event handlers configured successfully")
+        return demo
+    
+    except Exception as e:
+        logger.error(f"❌ Failed to create dashboard: {e}")
+        raise
 
 # Create and launch
-demo = create_dashboard()
+logger.info("🏗️ Building dashboard...")
+try:
+    demo = create_dashboard()
+    logger.info("✅ Dashboard created successfully!")
+except Exception as e:
+    logger.error(f"❌ Dashboard creation failed: {e}")
+    raise
 
 if __name__ == "__main__":
-    demo.launch()
+    logger.info("🚀 Launching dashboard server...")
+    try:
+        demo.launch()
+        logger.info("✅ Dashboard launched successfully!")
+    except Exception as e:
+        logger.error(f"❌ Dashboard launch failed: {e}")
+        raise