Update app.py

app.py

@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 import os
 import argparse
 import logging
@@ -55,10 +56,10 @@ try:
     # Test if TensorFlow actually works
     tf.config.set_visible_devices([], 'GPU')  # Disable GPU to avoid conflicts
     CNN_AVAILABLE = True
-    print("✅ TensorFlow successfully loaded - CNN features enabled")
+    print("TensorFlow successfully loaded - CNN features enabled")
 except Exception as e:
     CNN_AVAILABLE = False
-    print(f"❌ TensorFlow not available - CNN features disabled: {str(e)[:100]}...")
+    print(f"TensorFlow not available - CNN features disabled: {str(e)[:100]}...")
 
 try:
     import cdsapi
@@ -1016,17 +1017,17 @@ def create_advanced_clustering_visualization(storm_features, typhoon_data, metho
             # Flatten column names for readability
             cluster_stats.columns = ['_'.join(col).strip() for col in cluster_stats.columns]
             
-            stats_text = "🌀 ADVANCED CLUSTER ANALYSIS RESULTS\n" + "="*50 + "\n\n"
+            stats_text = "ADVANCED CLUSTER ANALYSIS RESULTS\n" + "="*50 + "\n\n"
             
             for cluster in sorted(storm_features_viz['cluster'].unique()):
                 if cluster == -1:
-                    stats_text += f"🔸 NOISE POINTS: {cluster_stats.loc[-1, 'SID_count']} storms\n\n"
+                    stats_text += f"NOISE POINTS: {cluster_stats.loc[-1, 'SID_count']} storms\n\n"
                     continue
                 
                 cluster_row = cluster_stats.loc[cluster]
                 storm_count = int(cluster_row['SID_count'])
                 
-                stats_text += f"🌪️ CLUSTER {cluster}: {storm_count} storms\n"
+                stats_text += f"CLUSTER {cluster}: {storm_count} storms\n"
                 stats_text += f"   Intensity: {cluster_row['USA_WIND_max_mean']:.1f} ± {cluster_row['USA_WIND_max_std']:.1f} kt\n"
                 stats_text += f"   Pressure: {cluster_row['USA_PRES_min_mean']:.1f} ± {cluster_row['USA_PRES_min_std']:.1f} hPa\n"
                 stats_text += f"   Track Length: {cluster_row['track_length_mean']:.1f} ± {cluster_row['track_length_std']:.1f} points\n"
@@ -1035,15 +1036,15 @@ def create_advanced_clustering_visualization(storm_features, typhoon_data, metho
                 stats_text += f"   Avg Curvature: {cluster_row['avg_curvature_mean']:.3f} radians\n\n"
             
             # Add feature importance summary
-            stats_text += "📊 CLUSTERING FEATURES USED:\n"
+            stats_text += "CLUSTERING FEATURES USED:\n"
             stats_text += f"   • Storm intensity (max/mean/std wind & pressure)\n"
             stats_text += f"   • Track characteristics (length, curvature, distance)\n"
             stats_text += f"   • Genesis location (lat/lon)\n"
             stats_text += f"   • Geographic range (lat/lon span)\n"
             stats_text += f"   • Total features: {len(feature_cols)}\n\n"
             
-            stats_text += f"🎯 ALGORITHM: {method.upper()} + DBSCAN clustering\n"
-            stats_text += f"📈 CLUSTERS FOUND: {len([c for c in storm_features_viz['cluster'].unique() if c != -1])}\n"
+            stats_text += f"ALGORITHM: {method.upper()} + DBSCAN clustering\n"
+            stats_text += f"CLUSTERS FOUND: {len([c for c in storm_features_viz['cluster'].unique() if c != -1])}\n"
             
         except Exception as stats_error:
             stats_text = f"Error generating cluster statistics: {str(stats_error)}"
@@ -1633,11 +1634,11 @@ def generate_enhanced_track_video(year, typhoon_selection, standard):
         anim.save(temp_file.name, writer=writer, dpi=100)
         plt.close(fig)
         
-        print(f"✅ Video generated successfully: {temp_file.name}")
+        print(f"Video generated successfully: {temp_file.name}")
         return temp_file.name
         
     except Exception as e:
-        print(f"❌ Error generating video: {e}")
+        print(f"Error generating video: {e}")
         import traceback
         traceback.print_exc()
         return None
@@ -1721,36 +1722,36 @@ def create_interface():
             available_years = [str(year) for year in range(2000, 2026)]
 
         with gr.Blocks(title="Enhanced Typhoon Analysis Platform", theme=gr.themes.Soft()) as demo:
-            gr.Markdown("# 🌪️ Enhanced Typhoon Analysis Platform")
+            gr.Markdown("# Enhanced Typhoon Analysis Platform")
             gr.Markdown("Advanced ML clustering, CNN predictions, and comprehensive tropical cyclone analysis including Tropical Depressions")
             
-            with gr.Tab("📊 Overview"):
+            with gr.Tab("Overview"):
                 gr.Markdown(f"""
                 ## Welcome to the Enhanced Typhoon Analysis Dashboard
 
                 This dashboard provides comprehensive analysis of typhoon data in relation to ENSO phases with advanced machine learning capabilities.
 
-                ### 🆕 Enhanced Features:
-                - **📈 Advanced ML Clustering**: UMAP/t-SNE storm pattern analysis with route visualization
-                - **🤖 Optional CNN Predictions**: Deep learning intensity forecasting
-                - **🌀 Complete TD Support**: Now includes Tropical Depressions (< 34 kt)
-                - **📅 2025 Data Ready**: Real-time compatibility with current year data
-                - **🎬 Enhanced Animations**: High-quality storm track visualizations
+                ### Enhanced Features:
+                - **Advanced ML Clustering**: UMAP/t-SNE storm pattern analysis with route visualization
+                - **Optional CNN Predictions**: Deep learning intensity forecasting
+                - **Complete TD Support**: Now includes Tropical Depressions (< 34 kt)
+                - **2025 Data Ready**: Real-time compatibility with current year data
+                - **Enhanced Animations**: High-quality storm track visualizations
                 
-                ### 📁 Data Status:
+                ### Data Status:
                 - **ONI Data**: {len(oni_data)} years loaded
                 - **Typhoon Data**: {total_records} records loaded
                 - **Merged Data**: {len(merged_data)} typhoons with ONI values
                 - **Available Years**: {year_range_display}
                 
-                ### 🔧 Technical Capabilities:
-                - **UMAP Clustering**: {"✅ Available" if UMAP_AVAILABLE else "❌ Limited to t-SNE/PCA"}
-                - **AI Predictions**: {"✅ Deep Learning" if CNN_AVAILABLE else "✅ Physics-based"}
-                - **Enhanced Categorization**: ✅ Tropical Depression to Super Typhoon
-                - **Platform Compatibility**: ✅ Optimized for Hugging Face Spaces
+                ### Technical Capabilities:
+                - **UMAP Clustering**: {"Available" if UMAP_AVAILABLE else "Limited to t-SNE/PCA"}
+                - **AI Predictions**: {"Deep Learning" if CNN_AVAILABLE else "Physics-based"}
+                - **Enhanced Categorization**: Tropical Depression to Super Typhoon
+                - **Platform Compatibility**: Optimized for Hugging Face Spaces
                 """)
 
-            with gr.Tab("🔍 Advanced ML Clustering with Routes"):
+            with gr.Tab("Advanced ML Clustering with Routes"):
                 gr.Markdown("## Storm Pattern Analysis using UMAP/t-SNE with Route Visualization")
                 gr.Markdown("**This tab shows both the dimensional clustering analysis AND the actual storm tracks colored by cluster**")
                 
@@ -1766,7 +1767,7 @@ def create_interface():
                         info="Display actual storm tracks colored by cluster"
                     )
                 
-                analyze_clusters_btn = gr.Button("🎯 Analyze Storm Clusters & Routes", variant="primary")
+                analyze_clusters_btn = gr.Button("Analyze Storm Clusters & Routes", variant="primary")
                 
                 with gr.Row():
                     cluster_plot = gr.Plot(label="Storm Clustering with Route Visualization")
@@ -1792,14 +1793,14 @@ def create_interface():
                 )
                 
                 gr.Markdown("""
-                ### 🧠 Advanced Clustering Features:
-                - **🎯 Multi-dimensional Analysis**: Uses 15+ storm characteristics including intensity, track shape, genesis location
-                - **🗺️ Route Visualization**: Shows actual storm tracks colored by cluster membership
-                - **📊 DBSCAN Clustering**: Automatically finds natural groupings without predefined cluster count
-                - **📈 Comprehensive Stats**: Detailed cluster analysis including intensity, pressure, track length, curvature
-                - **🔄 Interactive**: Hover over points to see storm details, zoom and pan the route map
+                ### Advanced Clustering Features:
+                - **Multi-dimensional Analysis**: Uses 15+ storm characteristics including intensity, track shape, genesis location
+                - **Route Visualization**: Shows actual storm tracks colored by cluster membership
+                - **DBSCAN Clustering**: Automatically finds natural groupings without predefined cluster count
+                - **Comprehensive Stats**: Detailed cluster analysis including intensity, pressure, track length, curvature
+                - **Interactive**: Hover over points to see storm details, zoom and pan the route map
                 
-                ### 📖 How to Interpret:
+                ### How to Interpret:
                 - **Left Plot**: Each dot is a storm positioned by similarity (close = similar characteristics)
                 - **Right Plot**: Actual geographic storm tracks, colored by which cluster they belong to
                 - **Cluster Colors**: Each cluster gets a unique color to identify similar storm patterns
@@ -1810,11 +1811,11 @@ def create_interface():
                 gr.Markdown("## AI-Powered Storm Intensity Forecasting")
                 
                 if CNN_AVAILABLE:
-                    gr.Markdown("✅ **Deep Learning models available** - TensorFlow loaded successfully")
+                    gr.Markdown("**Deep Learning models available** - TensorFlow loaded successfully")
                     method_description = "Using Convolutional Neural Networks for advanced intensity prediction"
                 else:
-                    gr.Markdown("🔬 **Physics-based models available** - Using climatological relationships")
-                    gr.Markdown("💡 *Install TensorFlow for deep learning features: `pip install tensorflow-cpu`*")
+                    gr.Markdown("**Physics-based models available** - Using climatological relationships")
+                    gr.Markdown("*Install TensorFlow for deep learning features: pip install tensorflow-cpu*")
                     method_description = "Using established meteorological relationships and climatology"
                 
                 gr.Markdown(f"**Current Method**: {method_description}")
@@ -1825,7 +1826,7 @@ def create_interface():
                     cnn_month = gr.Slider(1, 12, label="Month", value=9, info="Month of year (1=Jan, 12=Dec)")
                     cnn_oni = gr.Number(label="ONI Value", value=0.0, info="Current ENSO index (-3 to 3)")
                 
-                predict_btn = gr.Button("🎯 Predict Storm Intensity", variant="primary")
+                predict_btn = gr.Button("Predict Storm Intensity", variant="primary")
                 
                 with gr.Row():
                     intensity_output = gr.Number(label="Predicted Max Wind (kt)")
@@ -1944,7 +1945,7 @@ def create_interface():
                         value='atlantic'
                     )
                 
-                generate_video_btn = gr.Button("🎬 Generate Enhanced Animation", variant="primary")
+                generate_video_btn = gr.Button("Generate Enhanced Animation", variant="primary")
                 video_output = gr.Video(label="Storm Track Animation")
                 
                 # Update storm options when year or basin changes
@@ -1963,17 +1964,17 @@ def create_interface():
                 )
                 
                 gr.Markdown("""
-                ### 🆕 Enhanced Animation Features:
-                - **🌀 Full TD Support**: Now displays Tropical Depressions (< 34 kt) in gray
-                - **📅 2025 Compatibility**: Complete support for current year data
-                - **🗺️ Enhanced Maps**: Better cartographic projections with terrain features
-                - **📏 Smart Scaling**: Storm symbols scale dynamically with intensity
-                - **📊 Real-time Info**: Live position, time, and meteorological data display
-                - **🎨 Professional Styling**: Publication-quality animations with proper legends
-                - **⚡ Optimized Export**: Fast rendering with web-compatible video formats
+                ### Enhanced Animation Features:
+                - **Full TD Support**: Now displays Tropical Depressions (< 34 kt) in gray
+                - **2025 Compatibility**: Complete support for current year data
+                - **Enhanced Maps**: Better cartographic projections with terrain features
+                - **Smart Scaling**: Storm symbols scale dynamically with intensity
+                - **Real-time Info**: Live position, time, and meteorological data display
+                - **Professional Styling**: Publication-quality animations with proper legends
+                - **Optimized Export**: Fast rendering with web-compatible video formats
                 """)
 
-            with gr.Tab("📊 Data Statistics & Insights"):
+            with gr.Tab("Data Statistics & Insights"):
                 gr.Markdown("## Comprehensive Dataset Analysis")
                 
                 # Create enhanced data summary
@@ -2079,27 +2080,27 @@ def create_interface():
                     td_percentage = 0
                 
                 gr.Markdown(f"""
-                ### 📈 Enhanced Dataset Summary:
+                ### Enhanced Dataset Summary:
                 - **Total Unique Storms**: {total_storms:,}
                 - **Total Track Records**: {total_records:,}  
                 - **Year Range**: {year_range} ({years_covered} years)
                 - **Basins Available**: {basins_available}
                 - **Average Storms/Year**: {avg_storms_per_year:.1f}
                 
-                ### 🌀 Storm Category Breakdown:
+                ### Storm Category Breakdown:
                 - **Tropical Depressions**: {td_storms:,} storms ({td_percentage:.1f}%)
                 - **Tropical Storms**: {ts_storms:,} storms
                 - **Typhoons (C1-C5)**: {typhoon_storms:,} storms
                 
-                ### 🆕 New Platform Capabilities:
-                - ✅ **Complete TD Analysis** - First platform to include comprehensive TD tracking
-                - ✅ **Advanced ML Clustering** - DBSCAN pattern recognition with route visualization  
-                - ✅ **Real-time Predictions** - Physics-based and optional CNN intensity forecasting
-                - ✅ **2025 Data Ready** - Full compatibility with current season data
-                - ✅ **Enhanced Animations** - Professional-quality storm track videos
-                - ✅ **Multi-basin Analysis** - Comprehensive Pacific and Atlantic coverage
+                ### New Platform Capabilities:
+                - **Complete TD Analysis** - First platform to include comprehensive TD tracking
+                - **Advanced ML Clustering** - DBSCAN pattern recognition with route visualization  
+                - **Real-time Predictions** - Physics-based and optional CNN intensity forecasting
+                - **2025 Data Ready** - Full compatibility with current season data
+                - **Enhanced Animations** - Professional-quality storm track videos
+                - **Multi-basin Analysis** - Comprehensive Pacific and Atlantic coverage
                 
-                ### 🔬 Research Applications:
+                ### Research Applications:
                 - Climate change impact studies
                 - Seasonal forecasting research
                 - Storm pattern classification
@@ -2118,10 +2119,10 @@ def create_interface():
 def create_minimal_fallback_interface():
     """Create a minimal fallback interface when main interface fails"""
     with gr.Blocks() as demo:
-        gr.Markdown("# 🌪️ Enhanced Typhoon Analysis Platform")
+        gr.Markdown("# Enhanced Typhoon Analysis Platform")
         gr.Markdown("**Notice**: Loading with minimal interface due to data issues.")
         
-        with gr.Tab("📊 Status"):
+        with gr.Tab("Status"):
             gr.Markdown("""
             ## Platform Status
             
@@ -2143,7 +2144,7 @@ def create_minimal_fallback_interface():
             4. Try restarting the application
             """)
         
-        with gr.Tab("🔧 Debug"):
+        with gr.Tab("Debug"):
             gr.Markdown("## Debug Information")
             
             def get_debug_info():
@@ -2177,7 +2178,7 @@ def create_minimal_fallback_interface():
 
 def test_color_conversion():
     """Test color conversion functions"""
-    print("🎨 Testing color conversion...")
+    print("Testing color conversion...")
     
     # Test all categories
     test_winds = [25, 40, 70, 85, 100, 120, 150]  # TD, TS, C1, C2, C3, C4, C5
@@ -2189,7 +2190,7 @@ def test_color_conversion():
         
         print(f"Wind: {wind:3d}kt → {category:20s} → Plotly: {plotly_color:15s} → Matplotlib: {matplotlib_color}")
     
-    print("✅ Color conversion test complete!")
+    print("Color conversion test complete!")
 
 def test_rgb_conversion():
     """Test RGB string to hex conversion"""
@@ -2200,12 +2201,12 @@ def test_rgb_conversion():
         'rgb(0, 0, 255)'
     ]
     
-    print("🔧 Testing RGB to hex conversion...")
+    print("Testing RGB to hex conversion...")
     for rgb_str in test_colors:
         hex_color = rgb_string_to_hex(rgb_str)
         print(f"{rgb_str:20s} → {hex_color}")
     
-    print("✅ RGB conversion test complete!")
+    print("RGB conversion test complete!")
 
 # Create and launch the interface
 demo = create_interface()