Update app.py

app.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 import os
 import argparse
 import logging
@@ -116,7 +115,7 @@ CACHE_EXPIRY_DAYS = 1
 # Enhanced color mapping with TD support (for Plotly)
 enhanced_color_map = {
     'Unknown': 'rgb(200, 200, 200)',
-    'Tropical Depression': 'rgb(128, 128, 128)',  # NEW: Gray for TD
+    'Tropical Depression': 'rgb(128, 128, 128)',  # Gray for TD
     'Tropical Storm': 'rgb(0, 0, 255)',
     'C1 Typhoon': 'rgb(0, 255, 255)',
     'C2 Typhoon': 'rgb(0, 255, 0)',
@@ -1028,20 +1027,20 @@ def create_advanced_clustering_visualization(storm_features, typhoon_data, metho
                 storm_count = int(cluster_row['SID_count'])
                 
                 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"
+                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"
                 stats_text += f"   Genesis Region: {cluster_row['genesis_lat']:.1f}°N, {cluster_row['genesis_lon']:.1f}°E\n"
                 stats_text += f"   Avg Distance: {cluster_row['total_distance_mean']:.2f} degrees\n"
                 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 += 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 += "   - Storm intensity (max/mean/std wind & pressure)\n"
+            stats_text += "   - Track characteristics (length, curvature, distance)\n"
+            stats_text += "   - Genesis location (lat/lon)\n"
+            stats_text += "   - 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"
@@ -1726,30 +1725,31 @@ def create_interface():
             gr.Markdown("Advanced ML clustering, CNN predictions, and comprehensive tropical cyclone analysis including Tropical Depressions")
             
             with gr.Tab("Overview"):
-                gr.Markdown(f"""
+                overview_text = 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
+                - 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:
-                - **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}
+                - 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
-                """)
+                - 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
+                """
+                gr.Markdown(overview_text)
 
             with gr.Tab("Advanced ML Clustering with Routes"):
                 gr.Markdown("## Storm Pattern Analysis using UMAP/t-SNE with Route Visualization")
@@ -1792,30 +1792,31 @@ def create_interface():
                     outputs=[cluster_plot, cluster_stats]
                 )
                 
-                gr.Markdown("""
+                cluster_info_text = """
                 ### 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
+                - 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:
-                - **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
-                - **Noise Points**: Gray points represent storms that don't fit clear patterns
-                """)
+                - 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
+                - Noise Points: Gray points represent storms that don't fit clear patterns
+                """
+                gr.Markdown(cluster_info_text)
 
-            with gr.Tab("🤖 Intensity Prediction"):
+            with gr.Tab("Intensity Prediction"):
                 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}")
@@ -1838,22 +1839,22 @@ def create_interface():
                     outputs=[intensity_output, confidence_output]
                 )
                 
-                gr.Markdown("""
-                gr.Markdown("""
-                ### 🧠 Prediction Features:
-                - **Environmental Analysis**: Considers ENSO, latitude, seasonality
-                - **Real-time Capable**: Predictions in milliseconds
-                - **Confidence Scoring**: Uncertainty quantification included
-                - **Robust Fallbacks**: Works with or without deep learning libraries
+                prediction_info_text = """
+                ### Prediction Features:
+                - Environmental Analysis: Considers ENSO, latitude, seasonality
+                - Real-time Capable: Predictions in milliseconds
+                - Confidence Scoring: Uncertainty quantification included
+                - Robust Fallbacks: Works with or without deep learning libraries
                 
-                ### 📖 Interpretation Guide:
-                - **25-33 kt**: Tropical Depression (TD)
-                - **34-63 kt**: Tropical Storm (TS) 
-                - **64+ kt**: Typhoon categories (C1-C5)
-                - **100+ kt**: Major typhoon (C3+)
-                """)
-
-            with gr.Tab("📍 Track Visualization"):
+                ### Interpretation Guide:
+                - 25-33 kt: Tropical Depression (TD)
+                - 34-63 kt: Tropical Storm (TS) 
+                - 64+ kt: Typhoon categories (C1-C5)
+                - 100+ kt: Major typhoon (C3+)
+                """
+                gr.Markdown(prediction_info_text)
+
+            with gr.Tab("Track Visualization"):
                 with gr.Row():
                     start_year = gr.Number(label="Start Year", value=2020)
                     start_month = gr.Dropdown(label="Start Month", choices=list(range(1, 13)), value=1)
@@ -1870,7 +1871,7 @@ def create_interface():
                     outputs=[tracks_plot, typhoon_count]
                 )
             
-            with gr.Tab("💨 Wind Analysis"):
+            with gr.Tab("Wind Analysis"):
                 with gr.Row():
                     wind_start_year = gr.Number(label="Start Year", value=2020)
                     wind_start_month = gr.Dropdown(label="Start Month", choices=list(range(1, 13)), value=1)
@@ -1887,7 +1888,7 @@ def create_interface():
                     outputs=[wind_scatter, wind_regression_results]
                 )
             
-            with gr.Tab("🌀 Pressure Analysis"):
+            with gr.Tab("Pressure Analysis"):
                 with gr.Row():
                     pressure_start_year = gr.Number(label="Start Year", value=2020)
                     pressure_start_month = gr.Dropdown(label="Start Month", choices=list(range(1, 13)), value=1)
@@ -1904,7 +1905,7 @@ def create_interface():
                     outputs=[pressure_scatter, pressure_regression_results]
                 )
             
-            with gr.Tab("🌏 Longitude Analysis"):
+            with gr.Tab("Longitude Analysis"):
                 with gr.Row():
                     lon_start_year = gr.Number(label="Start Year", value=2020)
                     lon_start_month = gr.Dropdown(label="Start Month", choices=list(range(1, 13)), value=1)
@@ -1922,7 +1923,7 @@ def create_interface():
                     outputs=[regression_plot, slopes_text, lon_regression_results]
                 )
             
-            with gr.Tab("🎬 Enhanced Track Animation"):
+            with gr.Tab("Enhanced Track Animation"):
                 gr.Markdown("## High-Quality Storm Track Visualization (All Categories Including TD)")
                 
                 with gr.Row():
@@ -1963,16 +1964,17 @@ def create_interface():
                     outputs=[video_output]
                 )
                 
-                gr.Markdown("""
+                animation_info_text = """
                 ### 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
-                """)
+                - 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
+                """
+                gr.Markdown(animation_info_text)
 
             with gr.Tab("Data Statistics & Insights"):
                 gr.Markdown("## Comprehensive Dataset Analysis")
@@ -2036,7 +2038,7 @@ def create_interface():
                 except Exception as e:
                     gr.Markdown(f"Visualization error: {str(e)}")
                 
-                # Enhanced statistics
+                # Enhanced statistics - FIXED formatting
                 total_storms = len(typhoon_data['SID'].unique()) if 'SID' in typhoon_data.columns else 0
                 total_records = len(typhoon_data)
                 
@@ -2079,26 +2081,27 @@ def create_interface():
                     td_storms = ts_storms = typhoon_storms = 0
                     td_percentage = 0
                 
-                gr.Markdown(f"""
+                # Create statistics text safely
+                stats_text = f"""
                 ### 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}
+                - 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:
-                - **Tropical Depressions**: {td_storms:,} storms ({td_percentage:.1f}%)
-                - **Tropical Storms**: {ts_storms:,} storms
-                - **Typhoons (C1-C5)**: {typhoon_storms:,} storms
+                - 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
+                - 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:
                 - Climate change impact studies
@@ -2106,7 +2109,8 @@ def create_interface():
                 - Storm pattern classification
                 - ENSO-typhoon relationship analysis
                 - Intensity prediction model development
-                """)
+                """
+                gr.Markdown(stats_text)
 
         return demo
     except Exception as e:
@@ -2188,7 +2192,7 @@ def test_color_conversion():
         plotly_color = enhanced_color_map.get(category, 'rgb(128,128,128)')
         matplotlib_color = get_matplotlib_color(category)
         
-        print(f"Wind: {wind:3d}kt → {category:20s} → Plotly: {plotly_color:15s} → Matplotlib: {matplotlib_color}")
+        print(f"Wind: {wind:3d}kt -> {category:20s} -> Plotly: {plotly_color:15s} -> Matplotlib: {matplotlib_color}")
     
     print("Color conversion test complete!")
 
@@ -2204,7 +2208,7 @@ def test_rgb_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(f"{rgb_str:20s} -> {hex_color}")
     
     print("RGB conversion test complete!")