app.py

import os
import argparse
import logging
import pickle
import threading
import time
import warnings
from datetime import datetime, timedelta
from collections import defaultdict
import csv 
import json

# Suppress warnings for cleaner output
warnings.filterwarnings('ignore', category=FutureWarning)
warnings.filterwarnings('ignore', category=UserWarning, module='umap')
warnings.filterwarnings('ignore', category=UserWarning, module='sklearn')

import gradio as gr
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots

from sklearn.manifold import TSNE
from sklearn.cluster import DBSCAN, KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error, r2_score
from scipy.interpolate import interp1d, RBFInterpolator
import statsmodels.api as sm
import requests
import tempfile
import shutil
import xarray as xr

# NEW: Advanced ML imports
try:
    import umap.umap_ as umap
    UMAP_AVAILABLE = True
except ImportError:
    UMAP_AVAILABLE = False
    print("UMAP not available - clustering features limited")

# Optional CNN imports with robust error handling
CNN_AVAILABLE = False
try:
    # Set environment variables before importing TensorFlow
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # Suppress TensorFlow warnings
    import tensorflow as tf
    from tensorflow.keras import layers, models
    # 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")
except Exception as e:
    CNN_AVAILABLE = False
    print(f"TensorFlow not available - CNN features disabled: {str(e)[:100]}...")

try:
    import cdsapi
    CDSAPI_AVAILABLE = True
except ImportError:
    CDSAPI_AVAILABLE = False

import tropycal.tracks as tracks

# -----------------------------
# Configuration and Setup
# -----------------------------
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)

# Remove argument parser to simplify startup
DATA_PATH = '/tmp/typhoon_data' if 'SPACE_ID' in os.environ else tempfile.gettempdir()

# Ensure directory exists and is writable
try:
    os.makedirs(DATA_PATH, exist_ok=True)
    # Test write permissions
    test_file = os.path.join(DATA_PATH, 'test_write.txt')
    with open(test_file, 'w') as f:
        f.write('test')
    os.remove(test_file)
    logging.info(f"Data directory is writable: {DATA_PATH}")
except Exception as e:
    logging.warning(f"Data directory not writable, using temp dir: {e}")
    DATA_PATH = tempfile.mkdtemp()
    logging.info(f"Using temporary directory: {DATA_PATH}")

# Update file paths
ONI_DATA_PATH = os.path.join(DATA_PATH, 'oni_data.csv')
TYPHOON_DATA_PATH = os.path.join(DATA_PATH, 'processed_typhoon_data.csv')
MERGED_DATA_CSV = os.path.join(DATA_PATH, 'merged_typhoon_era5_data.csv')

# IBTrACS settings
BASIN_FILES = {
    'EP': 'ibtracs.EP.list.v04r01.csv',
    'NA': 'ibtracs.NA.list.v04r01.csv',
    'WP': 'ibtracs.WP.list.v04r01.csv'
}
IBTRACS_BASE_URL = 'https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r01/access/csv/'
LOCAL_IBTRACS_PATH = os.path.join(DATA_PATH, 'ibtracs.WP.list.v04r01.csv')
CACHE_FILE = os.path.join(DATA_PATH, 'ibtracs_cache.pkl')
CACHE_EXPIRY_DAYS = 1

# -----------------------------
# ENHANCED: Color Maps and Standards with TD Support
# -----------------------------
# Enhanced color mapping with TD support (for Plotly)
enhanced_color_map = {
    'Unknown': 'rgb(200, 200, 200)',
    '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)',
    'C3 Strong Typhoon': 'rgb(255, 255, 0)',
    'C4 Very Strong Typhoon': 'rgb(255, 165, 0)',
    'C5 Super Typhoon': 'rgb(255, 0, 0)'
}

# Matplotlib-compatible color mapping (hex colors)
matplotlib_color_map = {
    'Unknown': '#C8C8C8',
    'Tropical Depression': '#808080',  # Gray for TD
    'Tropical Storm': '#0000FF',       # Blue
    'C1 Typhoon': '#00FFFF',          # Cyan
    'C2 Typhoon': '#00FF00',          # Green
    'C3 Strong Typhoon': '#FFFF00',   # Yellow
    'C4 Very Strong Typhoon': '#FFA500', # Orange
    'C5 Super Typhoon': '#FF0000'     # Red
}

# Taiwan color mapping
taiwan_color_map = {
    'Tropical Depression': '#808080',  # Gray
    'Mild Typhoon': '#FFFF00',         # Yellow
    'Medium Typhoon': '#FFA500',       # Orange
    'Strong Typhoon': '#FF0000'        # Red
}

def rgb_string_to_hex(rgb_string):
    """Convert 'rgb(r,g,b)' string to hex color for matplotlib"""
    try:
        # Extract numbers from 'rgb(r,g,b)' format
        import re
        numbers = re.findall(r'\d+', rgb_string)
        if len(numbers) == 3:
            r, g, b = map(int, numbers)
            return f'#{r:02x}{g:02x}{b:02x}'
        else:
            return '#808080'  # Default gray
    except:
        return '#808080'  # Default gray

def get_matplotlib_color(category):
    """Get matplotlib-compatible color for a storm category"""
    return matplotlib_color_map.get(category, '#808080')

def get_taiwan_color(category):
    """Get Taiwan standard color for a storm category"""
    return taiwan_color_map.get(category, '#808080')

# Cluster colors for route visualization
CLUSTER_COLORS = [
    '#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEAA7',
    '#DDA0DD', '#98D8C8', '#F7DC6F', '#BB8FCE', '#85C1E9',
    '#F8C471', '#82E0AA', '#F1948A', '#85C1E9', '#D2B4DE'
]

# Route prediction colors
ROUTE_COLORS = [
    '#FF0066', '#00FF66', '#6600FF', '#FF6600', '#0066FF',
    '#FF00CC', '#00FFCC', '#CC00FF', '#CCFF00', '#00CCFF'
]

# Original color map for backward compatibility
color_map = {
    'C5 Super Typhoon': 'rgb(255, 0, 0)',
    'C4 Very Strong Typhoon': 'rgb(255, 165, 0)',
    'C3 Strong Typhoon': 'rgb(255, 255, 0)',
    'C2 Typhoon': 'rgb(0, 255, 0)',
    'C1 Typhoon': 'rgb(0, 255, 255)',
    'Tropical Storm': 'rgb(0, 0, 255)',
    'Tropical Depression': 'rgb(128, 128, 128)'
}

atlantic_standard = {
    'C5 Super Typhoon': {'wind_speed': 137, 'color': 'Red', 'hex': '#FF0000'},
    'C4 Very Strong Typhoon': {'wind_speed': 113, 'color': 'Orange', 'hex': '#FFA500'},
    'C3 Strong Typhoon': {'wind_speed': 96, 'color': 'Yellow', 'hex': '#FFFF00'},
    'C2 Typhoon': {'wind_speed': 83, 'color': 'Green', 'hex': '#00FF00'},
    'C1 Typhoon': {'wind_speed': 64, 'color': 'Cyan', 'hex': '#00FFFF'},
    'Tropical Storm': {'wind_speed': 34, 'color': 'Blue', 'hex': '#0000FF'},
    'Tropical Depression': {'wind_speed': 0, 'color': 'Gray', 'hex': '#808080'}
}

taiwan_standard = {
    'Strong Typhoon': {'wind_speed': 51.0, 'color': 'Red', 'hex': '#FF0000'},
    'Medium Typhoon': {'wind_speed': 33.7, 'color': 'Orange', 'hex': '#FFA500'},
    'Mild Typhoon': {'wind_speed': 17.2, 'color': 'Yellow', 'hex': '#FFFF00'},
    'Tropical Depression': {'wind_speed': 0, 'color': 'Gray', 'hex': '#808080'}
}

# -----------------------------
# Utility Functions for HF Spaces
# -----------------------------

def safe_file_write(file_path, data_frame, backup_dir=None):
    """Safely write DataFrame to CSV with backup and error handling"""
    try:
        # Create directory if it doesn't exist
        os.makedirs(os.path.dirname(file_path), exist_ok=True)
        
        # Try to write to a temporary file first
        temp_path = file_path + '.tmp'
        data_frame.to_csv(temp_path, index=False)
        
        # If successful, rename to final file
        os.rename(temp_path, file_path)
        logging.info(f"Successfully saved {len(data_frame)} records to {file_path}")
        return True
        
    except PermissionError as e:
        logging.warning(f"Permission denied writing to {file_path}: {e}")
        if backup_dir:
            try:
                backup_path = os.path.join(backup_dir, os.path.basename(file_path))
                data_frame.to_csv(backup_path, index=False)
                logging.info(f"Saved to backup location: {backup_path}")
                return True
            except Exception as backup_e:
                logging.error(f"Failed to save to backup location: {backup_e}")
        return False
        
    except Exception as e:
        logging.error(f"Error saving file {file_path}: {e}")
        # Clean up temp file if it exists
        temp_path = file_path + '.tmp'
        if os.path.exists(temp_path):
            try:
                os.remove(temp_path)
            except:
                pass
        return False

def get_fallback_data_dir():
    """Get a fallback data directory that's guaranteed to be writable"""
    fallback_dirs = [
        tempfile.gettempdir(),
        '/tmp',
        os.path.expanduser('~'),
        os.getcwd()
    ]
    
    for directory in fallback_dirs:
        try:
            test_dir = os.path.join(directory, 'typhoon_fallback')
            os.makedirs(test_dir, exist_ok=True)
            test_file = os.path.join(test_dir, 'test.txt')
            with open(test_file, 'w') as f:
                f.write('test')
            os.remove(test_file)
            return test_dir
        except:
            continue
    
    # If all else fails, use current directory
    return os.getcwd()

# -----------------------------
# ONI and Typhoon Data Functions
# -----------------------------

def download_oni_file(url, filename):
    """Download ONI file with retry logic"""
    max_retries = 3
    for attempt in range(max_retries):
        try:
            response = requests.get(url, timeout=30)
            response.raise_for_status()
            with open(filename, 'wb') as f:
                f.write(response.content)
            return True
        except Exception as e:
            logging.warning(f"Attempt {attempt + 1} failed to download ONI: {e}")
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)  # Exponential backoff
            else:
                logging.error(f"Failed to download ONI after {max_retries} attempts")
                return False

def convert_oni_ascii_to_csv(input_file, output_file):
    """Convert ONI ASCII format to CSV"""
    data = defaultdict(lambda: [''] * 12)
    season_to_month = {'DJF':12, 'JFM':1, 'FMA':2, 'MAM':3, 'AMJ':4, 'MJJ':5,
                       'JJA':6, 'JAS':7, 'ASO':8, 'SON':9, 'OND':10, 'NDJ':11}
    
    try:
        with open(input_file, 'r') as f:
            lines = f.readlines()[1:]  # Skip header
            for line in lines:
                parts = line.split()
                if len(parts) >= 4:
                    season, year, anom = parts[0], parts[1], parts[-1]
                    if season in season_to_month:
                        month = season_to_month[season]
                        if season == 'DJF':
                            year = str(int(year)-1)
                        data[year][month-1] = anom
        
        # Write to CSV with safe write
        df = pd.DataFrame(data).T.reset_index()
        df.columns = ['Year','Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
        df = df.sort_values('Year').reset_index(drop=True)
        
        return safe_file_write(output_file, df, get_fallback_data_dir())
        
    except Exception as e:
        logging.error(f"Error converting ONI file: {e}")
        return False

def update_oni_data():
    """Update ONI data with error handling"""
    url = "https://www.cpc.ncep.noaa.gov/data/indices/oni.ascii.txt"
    temp_file = os.path.join(DATA_PATH, "temp_oni.ascii.txt")
    input_file = os.path.join(DATA_PATH, "oni.ascii.txt")
    output_file = ONI_DATA_PATH
    
    try:
        if download_oni_file(url, temp_file):
            if not os.path.exists(input_file) or not os.path.exists(output_file):
                os.rename(temp_file, input_file)
                convert_oni_ascii_to_csv(input_file, output_file)
            else:
                os.remove(temp_file)
        else:
            # Create fallback ONI data if download fails
            logging.warning("Creating fallback ONI data")
            create_fallback_oni_data(output_file)
    except Exception as e:
        logging.error(f"Error updating ONI data: {e}")
        create_fallback_oni_data(output_file)

def create_fallback_oni_data(output_file):
    """Create minimal ONI data for testing"""
    years = range(2000, 2026)  # Extended to include 2025
    months = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
    
    # Create synthetic ONI data
    data = []
    for year in years:
        row = [year]
        for month in months:
            # Generate some realistic ONI values
            value = np.random.normal(0, 1) * 0.5
            row.append(f"{value:.2f}")
        data.append(row)
    
    df = pd.DataFrame(data, columns=['Year'] + months)
    safe_file_write(output_file, df, get_fallback_data_dir())

# -----------------------------
# FIXED: IBTrACS Data Loading
# -----------------------------

def download_ibtracs_file(basin, force_download=False):
    """Download specific basin file from IBTrACS"""
    filename = BASIN_FILES[basin]
    local_path = os.path.join(DATA_PATH, filename)
    url = IBTRACS_BASE_URL + filename
    
    # Check if file exists and is recent (less than 7 days old)
    if os.path.exists(local_path) and not force_download:
        file_age = time.time() - os.path.getmtime(local_path)
        if file_age < 7 * 24 * 3600:  # 7 days
            logging.info(f"Using cached {basin} basin file")
            return local_path
    
    try:
        logging.info(f"Downloading {basin} basin file from {url}")
        response = requests.get(url, timeout=60)
        response.raise_for_status()
        
        # Ensure directory exists
        os.makedirs(os.path.dirname(local_path), exist_ok=True)
        
        with open(local_path, 'wb') as f:
            f.write(response.content)
        logging.info(f"Successfully downloaded {basin} basin file")
        return local_path
    except Exception as e:
        logging.error(f"Failed to download {basin} basin file: {e}")
        return None

def examine_ibtracs_structure(file_path):
    """Examine the actual structure of an IBTrACS CSV file"""
    try:
        with open(file_path, 'r') as f:
            lines = f.readlines()
        
        # Show first 5 lines
        logging.info("First 5 lines of IBTrACS file:")
        for i, line in enumerate(lines[:5]):
            logging.info(f"Line {i}: {line.strip()}")
        
        # The first line contains the actual column headers
        # No need to skip rows for IBTrACS v04r01
        df = pd.read_csv(file_path, nrows=5)
        logging.info(f"Columns from first row: {list(df.columns)}")
        
        return list(df.columns)
    except Exception as e:
        logging.error(f"Error examining IBTrACS structure: {e}")
        return None

def load_ibtracs_csv_directly(basin='WP'):
    """Load IBTrACS data directly from CSV - FIXED VERSION"""
    filename = BASIN_FILES[basin]
    local_path = os.path.join(DATA_PATH, filename)
    
    # Download if not exists
    if not os.path.exists(local_path):
        downloaded_path = download_ibtracs_file(basin)
        if not downloaded_path:
            return None
    
    try:
        # First, examine the structure
        actual_columns = examine_ibtracs_structure(local_path)
        if not actual_columns:
            logging.error("Could not examine IBTrACS file structure")
            return None
        
        # Read IBTrACS CSV - DON'T skip any rows for v04r01
        # The first row contains proper column headers
        logging.info(f"Reading IBTrACS CSV file: {local_path}")
        df = pd.read_csv(local_path, low_memory=False)  # Don't skip any rows
        
        logging.info(f"Original columns: {list(df.columns)}")
        logging.info(f"Data shape before cleaning: {df.shape}")
        
        # Check which essential columns exist
        required_cols = ['SID', 'ISO_TIME', 'LAT', 'LON']
        available_required = [col for col in required_cols if col in df.columns]
        
        if len(available_required) < 2:
            logging.error(f"Missing critical columns. Available: {list(df.columns)}")
            return None
        
        # Clean and standardize the data with format specification
        if 'ISO_TIME' in df.columns:
            df['ISO_TIME'] = pd.to_datetime(df['ISO_TIME'], format='%Y-%m-%d %H:%M:%S', errors='coerce')
        
        # Clean numeric columns
        numeric_columns = ['LAT', 'LON', 'WMO_WIND', 'WMO_PRES', 'USA_WIND', 'USA_PRES']
        for col in numeric_columns:
            if col in df.columns:
                df[col] = pd.to_numeric(df[col], errors='coerce')
        
        # Filter out invalid/missing critical data
        valid_rows = df['LAT'].notna() & df['LON'].notna()
        df = df[valid_rows]
        
        # Ensure LAT/LON are in reasonable ranges
        df = df[(df['LAT'] >= -90) & (df['LAT'] <= 90)]
        df = df[(df['LON'] >= -180) & (df['LON'] <= 180)]
        
        # Add basin info if missing
        if 'BASIN' not in df.columns:
            df['BASIN'] = basin
        
        # Add default columns if missing
        if 'NAME' not in df.columns:
            df['NAME'] = 'UNNAMED'
        
        if 'SEASON' not in df.columns and 'ISO_TIME' in df.columns:
            df['SEASON'] = df['ISO_TIME'].dt.year
        
        logging.info(f"Successfully loaded {len(df)} records from {basin} basin")
        return df
        
    except Exception as e:
        logging.error(f"Error reading IBTrACS CSV file: {e}")
        return None

def load_ibtracs_data_fixed():
    """Fixed version of IBTrACS data loading"""
    ibtracs_data = {}
    
    # Try to load each basin, but prioritize WP for this application
    load_order = ['WP', 'EP', 'NA']
    
    for basin in load_order:
        try:
            logging.info(f"Loading {basin} basin data...")
            df = load_ibtracs_csv_directly(basin)
            
            if df is not None and not df.empty:
                ibtracs_data[basin] = df
                logging.info(f"Successfully loaded {basin} basin with {len(df)} records")
            else:
                logging.warning(f"No data loaded for basin {basin}")
                ibtracs_data[basin] = None
                
        except Exception as e:
            logging.error(f"Failed to load basin {basin}: {e}")
            ibtracs_data[basin] = None
    
    return ibtracs_data

def load_data_fixed(oni_path, typhoon_path):
    """Fixed version of load_data function"""
    # Load ONI data
    oni_data = pd.DataFrame({'Year': [], 'Jan': [], 'Feb': [], 'Mar': [], 'Apr': [], 
                           'May': [], 'Jun': [], 'Jul': [], 'Aug': [], 'Sep': [], 
                           'Oct': [], 'Nov': [], 'Dec': []})
    
    if not os.path.exists(oni_path):
        logging.warning(f"ONI data file not found: {oni_path}")
        update_oni_data()
    
    try:
        oni_data = pd.read_csv(oni_path)
        logging.info(f"Successfully loaded ONI data with {len(oni_data)} years")
    except Exception as e:
        logging.error(f"Error loading ONI data: {e}")
        update_oni_data()
        try:
            oni_data = pd.read_csv(oni_path)
        except Exception as e:
            logging.error(f"Still can't load ONI data: {e}")
    
    # Load typhoon data - NEW APPROACH
    typhoon_data = None
    
    # First, try to load from existing processed file
    if os.path.exists(typhoon_path):
        try:
            typhoon_data = pd.read_csv(typhoon_path, low_memory=False)
            # Ensure basic columns exist and are valid
            required_cols = ['LAT', 'LON']
            if all(col in typhoon_data.columns for col in required_cols):
                if 'ISO_TIME' in typhoon_data.columns:
                    typhoon_data['ISO_TIME'] = pd.to_datetime(typhoon_data['ISO_TIME'], errors='coerce')
                logging.info(f"Loaded processed typhoon data with {len(typhoon_data)} records")
            else:
                logging.warning("Processed typhoon data missing required columns, will reload from IBTrACS")
                typhoon_data = None
        except Exception as e:
            logging.error(f"Error loading processed typhoon data: {e}")
            typhoon_data = None
    
    # If no valid processed data, load from IBTrACS
    if typhoon_data is None or typhoon_data.empty:
        logging.info("Loading typhoon data from IBTrACS...")
        ibtracs_data = load_ibtracs_data_fixed()
        
        # Combine all available basin data, prioritizing WP
        combined_dfs = []
        for basin in ['WP', 'EP', 'NA']:
            if basin in ibtracs_data and ibtracs_data[basin] is not None:
                df = ibtracs_data[basin].copy()
                df['BASIN'] = basin
                combined_dfs.append(df)
        
        if combined_dfs:
            typhoon_data = pd.concat(combined_dfs, ignore_index=True)
            # Ensure SID has proper format
            if 'SID' not in typhoon_data.columns and 'BASIN' in typhoon_data.columns:
                # Create SID from basin and other identifiers if missing
                if 'SEASON' in typhoon_data.columns:
                    typhoon_data['SID'] = (typhoon_data['BASIN'].astype(str) + 
                                         typhoon_data.index.astype(str).str.zfill(2) + 
                                         typhoon_data['SEASON'].astype(str))
                else:
                    typhoon_data['SID'] = (typhoon_data['BASIN'].astype(str) + 
                                         typhoon_data.index.astype(str).str.zfill(2) + 
                                         '2000')
            
            # Save the processed data for future use
            safe_file_write(typhoon_path, typhoon_data, get_fallback_data_dir())
            logging.info(f"Combined IBTrACS data: {len(typhoon_data)} total records")
        else:
            logging.error("Failed to load any IBTrACS basin data")
            # Create minimal fallback data
            typhoon_data = create_fallback_typhoon_data()
    
    # Final validation of typhoon data
    if typhoon_data is not None:
        # Ensure required columns exist with fallback values
        required_columns = {
            'SID': 'UNKNOWN',
            'ISO_TIME': pd.Timestamp('2000-01-01'),
            'LAT': 0.0,
            'LON': 0.0,
            'USA_WIND': np.nan,
            'USA_PRES': np.nan,
            'NAME': 'UNNAMED',
            'SEASON': 2000
        }
        
        for col, default_val in required_columns.items():
            if col not in typhoon_data.columns:
                typhoon_data[col] = default_val
                logging.warning(f"Added missing column {col} with default value")
        
        # Ensure data types
        if 'ISO_TIME' in typhoon_data.columns:
            typhoon_data['ISO_TIME'] = pd.to_datetime(typhoon_data['ISO_TIME'], errors='coerce')
        typhoon_data['LAT'] = pd.to_numeric(typhoon_data['LAT'], errors='coerce')
        typhoon_data['LON'] = pd.to_numeric(typhoon_data['LON'], errors='coerce')
        typhoon_data['USA_WIND'] = pd.to_numeric(typhoon_data['USA_WIND'], errors='coerce')
        typhoon_data['USA_PRES'] = pd.to_numeric(typhoon_data['USA_PRES'], errors='coerce')
        
        # Remove rows with invalid coordinates
        typhoon_data = typhoon_data.dropna(subset=['LAT', 'LON'])
        
        logging.info(f"Final typhoon data: {len(typhoon_data)} records after validation")
    
    return oni_data, typhoon_data

def create_fallback_typhoon_data():
    """Create minimal fallback typhoon data - FIXED VERSION"""
    # Use proper pandas date_range instead of numpy
    dates = pd.date_range(start='2000-01-01', end='2025-12-31', freq='D')  # Extended to 2025
    storm_dates = dates[np.random.choice(len(dates), size=100, replace=False)]
    
    data = []
    for i, date in enumerate(storm_dates):
        # Create realistic WP storm tracks
        base_lat = np.random.uniform(10, 30)
        base_lon = np.random.uniform(130, 160)
        
        # Generate 20-50 data points per storm
        track_length = np.random.randint(20, 51)
        sid = f"WP{i+1:02d}{date.year}"
        
        for j in range(track_length):
            lat = base_lat + j * 0.2 + np.random.normal(0, 0.1)
            lon = base_lon + j * 0.3 + np.random.normal(0, 0.1)
            wind = max(25, 70 + np.random.normal(0, 20))
            pres = max(950, 1000 - wind + np.random.normal(0, 5))
            
            data.append({
                'SID': sid,
                'ISO_TIME': date + pd.Timedelta(hours=j*6),  # Use pd.Timedelta instead
                'NAME': f'FALLBACK_{i+1}',
                'SEASON': date.year,
                'LAT': lat,
                'LON': lon,
                'USA_WIND': wind,
                'USA_PRES': pres,
                'BASIN': 'WP'
            })
    
    df = pd.DataFrame(data)
    logging.info(f"Created fallback typhoon data with {len(df)} records")
    return df

def process_oni_data(oni_data):
    """Process ONI data into long format"""
    oni_long = oni_data.melt(id_vars=['Year'], var_name='Month', value_name='ONI')
    month_map = {'Jan':'01','Feb':'02','Mar':'03','Apr':'04','May':'05','Jun':'06',
                 'Jul':'07','Aug':'08','Sep':'09','Oct':'10','Nov':'11','Dec':'12'}
    oni_long['Month'] = oni_long['Month'].map(month_map)
    oni_long['Date'] = pd.to_datetime(oni_long['Year'].astype(str)+'-'+oni_long['Month']+'-01')
    oni_long['ONI'] = pd.to_numeric(oni_long['ONI'], errors='coerce')
    return oni_long

def process_typhoon_data(typhoon_data):
    """Process typhoon data"""
    if 'ISO_TIME' in typhoon_data.columns:
        typhoon_data['ISO_TIME'] = pd.to_datetime(typhoon_data['ISO_TIME'], errors='coerce')
    typhoon_data['USA_WIND'] = pd.to_numeric(typhoon_data['USA_WIND'], errors='coerce')
    typhoon_data['USA_PRES'] = pd.to_numeric(typhoon_data['USA_PRES'], errors='coerce')
    typhoon_data['LON'] = pd.to_numeric(typhoon_data['LON'], errors='coerce')
    
    logging.info(f"Unique basins in typhoon_data: {typhoon_data['SID'].str[:2].unique()}")
    
    typhoon_max = typhoon_data.groupby('SID').agg({
        'USA_WIND':'max','USA_PRES':'min','ISO_TIME':'first','SEASON':'first','NAME':'first',
        'LAT':'first','LON':'first'
    }).reset_index()
    
    if 'ISO_TIME' in typhoon_max.columns:
        typhoon_max['Month'] = typhoon_max['ISO_TIME'].dt.strftime('%m')
        typhoon_max['Year'] = typhoon_max['ISO_TIME'].dt.year
    else:
        # Fallback if no ISO_TIME
        typhoon_max['Month'] = '01'
        typhoon_max['Year'] = typhoon_max['SEASON']
    
    typhoon_max['Category'] = typhoon_max['USA_WIND'].apply(categorize_typhoon_enhanced)
    return typhoon_max

def merge_data(oni_long, typhoon_max):
    """Merge ONI and typhoon data"""
    return pd.merge(typhoon_max, oni_long, on=['Year','Month'])

# -----------------------------
# ENHANCED: Categorization Functions
# -----------------------------

def categorize_typhoon_enhanced(wind_speed):
    """Enhanced categorization that properly includes Tropical Depressions"""
    if pd.isna(wind_speed):
        return 'Unknown'
    
    # Convert to knots if in m/s (some datasets use m/s)
    if wind_speed < 10:  # Likely in m/s, convert to knots
        wind_speed = wind_speed * 1.94384
    
    # FIXED thresholds to include TD
    if wind_speed < 34:  # Below 34 knots = Tropical Depression
        return 'Tropical Depression'
    elif wind_speed < 64:  # 34-63 knots = Tropical Storm
        return 'Tropical Storm'
    elif wind_speed < 83:  # 64-82 knots = Category 1 Typhoon
        return 'C1 Typhoon'
    elif wind_speed < 96:  # 83-95 knots = Category 2 Typhoon
        return 'C2 Typhoon'
    elif wind_speed < 113:  # 96-112 knots = Category 3 Strong Typhoon
        return 'C3 Strong Typhoon'
    elif wind_speed < 137:  # 113-136 knots = Category 4 Very Strong Typhoon
        return 'C4 Very Strong Typhoon'
    else:  # 137+ knots = Category 5 Super Typhoon
        return 'C5 Super Typhoon'

def categorize_typhoon_taiwan(wind_speed):
    """Taiwan categorization system"""
    if pd.isna(wind_speed):
        return 'Tropical Depression'
    
    # Convert to m/s if in knots
    if wind_speed > 50:  # Likely in knots, convert to m/s
        wind_speed = wind_speed * 0.514444
    
    if wind_speed >= 51.0:
        return 'Strong Typhoon'
    elif wind_speed >= 33.7:
        return 'Medium Typhoon'
    elif wind_speed >= 17.2:
        return 'Mild Typhoon'
    else:
        return 'Tropical Depression'

# Original function for backward compatibility
def categorize_typhoon(wind_speed):
    """Original categorize typhoon function for backward compatibility"""
    return categorize_typhoon_enhanced(wind_speed)

def classify_enso_phases(oni_value):
    """Classify ENSO phases based on ONI value"""
    if isinstance(oni_value, pd.Series):
        oni_value = oni_value.iloc[0]
    if pd.isna(oni_value):
        return 'Neutral'
    if oni_value >= 0.5:
        return 'El Nino'
    elif oni_value <= -0.5:
        return 'La Nina'
    else:
        return 'Neutral'

# -----------------------------
# FIXED: ADVANCED ML FEATURES WITH ROBUST ERROR HANDLING
# -----------------------------

def extract_storm_features(typhoon_data):
    """Extract comprehensive features for clustering analysis - FIXED VERSION"""
    try:
        if typhoon_data is None or typhoon_data.empty:
            logging.error("No typhoon data provided for feature extraction")
            return None
        
        # Basic features - ensure columns exist
        basic_features = []
        for sid in typhoon_data['SID'].unique():
            storm_data = typhoon_data[typhoon_data['SID'] == sid].copy()
            
            if len(storm_data) == 0:
                continue
            
            # Initialize feature dict with safe defaults
            features = {'SID': sid}
            
            # Wind statistics
            if 'USA_WIND' in storm_data.columns:
                wind_values = pd.to_numeric(storm_data['USA_WIND'], errors='coerce').dropna()
                if len(wind_values) > 0:
                    features['USA_WIND_max'] = wind_values.max()
                    features['USA_WIND_mean'] = wind_values.mean()
                    features['USA_WIND_std'] = wind_values.std() if len(wind_values) > 1 else 0
                else:
                    features['USA_WIND_max'] = 30
                    features['USA_WIND_mean'] = 30
                    features['USA_WIND_std'] = 0
            else:
                features['USA_WIND_max'] = 30
                features['USA_WIND_mean'] = 30
                features['USA_WIND_std'] = 0
                
            # Pressure statistics
            if 'USA_PRES' in storm_data.columns:
                pres_values = pd.to_numeric(storm_data['USA_PRES'], errors='coerce').dropna()
                if len(pres_values) > 0:
                    features['USA_PRES_min'] = pres_values.min()
                    features['USA_PRES_mean'] = pres_values.mean()
                    features['USA_PRES_std'] = pres_values.std() if len(pres_values) > 1 else 0
                else:
                    features['USA_PRES_min'] = 1000
                    features['USA_PRES_mean'] = 1000
                    features['USA_PRES_std'] = 0
            else:
                features['USA_PRES_min'] = 1000
                features['USA_PRES_mean'] = 1000
                features['USA_PRES_std'] = 0
            
            # Location statistics
            if 'LAT' in storm_data.columns and 'LON' in storm_data.columns:
                lat_values = pd.to_numeric(storm_data['LAT'], errors='coerce').dropna()
                lon_values = pd.to_numeric(storm_data['LON'], errors='coerce').dropna()
                
                if len(lat_values) > 0 and len(lon_values) > 0:
                    features['LAT_mean'] = lat_values.mean()
                    features['LAT_std'] = lat_values.std() if len(lat_values) > 1 else 0
                    features['LAT_max'] = lat_values.max()
                    features['LAT_min'] = lat_values.min()
                    features['LON_mean'] = lon_values.mean()
                    features['LON_std'] = lon_values.std() if len(lon_values) > 1 else 0
                    features['LON_max'] = lon_values.max()
                    features['LON_min'] = lon_values.min()
                    
                    # Genesis location (first valid position)
                    features['genesis_lat'] = lat_values.iloc[0]
                    features['genesis_lon'] = lon_values.iloc[0]
                    features['genesis_intensity'] = features['USA_WIND_mean']  # Use mean as fallback
                    
                    # Track characteristics
                    features['lat_range'] = lat_values.max() - lat_values.min()
                    features['lon_range'] = lon_values.max() - lon_values.min()
                    
                    # Calculate track distance
                    if len(lat_values) > 1:
                        distances = []
                        for i in range(1, len(lat_values)):
                            dlat = lat_values.iloc[i] - lat_values.iloc[i-1]
                            dlon = lon_values.iloc[i] - lon_values.iloc[i-1]
                            distances.append(np.sqrt(dlat**2 + dlon**2))
                        features['total_distance'] = sum(distances)
                        features['avg_speed'] = np.mean(distances) if distances else 0
                    else:
                        features['total_distance'] = 0
                        features['avg_speed'] = 0
                        
                    # Track curvature
                    if len(lat_values) > 2:
                        bearing_changes = []
                        for i in range(1, len(lat_values)-1):
                            dlat1 = lat_values.iloc[i] - lat_values.iloc[i-1]
                            dlon1 = lon_values.iloc[i] - lon_values.iloc[i-1]
                            dlat2 = lat_values.iloc[i+1] - lat_values.iloc[i]
                            dlon2 = lon_values.iloc[i+1] - lon_values.iloc[i]
                            
                            angle1 = np.arctan2(dlat1, dlon1)
                            angle2 = np.arctan2(dlat2, dlon2)
                            change = abs(angle2 - angle1)
                            bearing_changes.append(change)
                        
                        features['avg_curvature'] = np.mean(bearing_changes) if bearing_changes else 0
                    else:
                        features['avg_curvature'] = 0
                else:
                    # Default location values
                    features.update({
                        'LAT_mean': 20, 'LAT_std': 0, 'LAT_max': 20, 'LAT_min': 20,
                        'LON_mean': 140, 'LON_std': 0, 'LON_max': 140, 'LON_min': 140,
                        'genesis_lat': 20, 'genesis_lon': 140, 'genesis_intensity': 30,
                        'lat_range': 0, 'lon_range': 0, 'total_distance': 0,
                        'avg_speed': 0, 'avg_curvature': 0
                    })
            else:
                # Default location values if columns missing
                features.update({
                    'LAT_mean': 20, 'LAT_std': 0, 'LAT_max': 20, 'LAT_min': 20,
                    'LON_mean': 140, 'LON_std': 0, 'LON_max': 140, 'LON_min': 140,
                    'genesis_lat': 20, 'genesis_lon': 140, 'genesis_intensity': 30,
                    'lat_range': 0, 'lon_range': 0, 'total_distance': 0,
                    'avg_speed': 0, 'avg_curvature': 0
                })
            
            # Track length
            features['track_length'] = len(storm_data)
            
            # Add seasonal information
            if 'SEASON' in storm_data.columns:
                features['season'] = storm_data['SEASON'].iloc[0]
            else:
                features['season'] = 2000
                
            # Add basin information
            if 'BASIN' in storm_data.columns:
                features['basin'] = storm_data['BASIN'].iloc[0]
            elif 'SID' in storm_data.columns:
                features['basin'] = sid[:2] if len(sid) >= 2 else 'WP'
            else:
                features['basin'] = 'WP'
            
            basic_features.append(features)
        
        if not basic_features:
            logging.error("No valid storm features could be extracted")
            return None
            
        # Convert to DataFrame
        storm_features = pd.DataFrame(basic_features)
        
        # Ensure all numeric columns are properly typed
        numeric_columns = [col for col in storm_features.columns if col not in ['SID', 'basin']]
        for col in numeric_columns:
            storm_features[col] = pd.to_numeric(storm_features[col], errors='coerce').fillna(0)
        
        logging.info(f"Successfully extracted features for {len(storm_features)} storms")
        logging.info(f"Feature columns: {list(storm_features.columns)}")
        
        return storm_features
        
    except Exception as e:
        logging.error(f"Error in extract_storm_features: {e}")
        import traceback
        traceback.print_exc()
        return None

def perform_dimensionality_reduction(storm_features, method='umap', n_components=2):
    """Perform UMAP or t-SNE dimensionality reduction - FIXED VERSION"""
    try:
        if storm_features is None or storm_features.empty:
            raise ValueError("No storm features provided")
        
        # Select numeric features for clustering - FIXED
        feature_cols = []
        for col in storm_features.columns:
            if col not in ['SID', 'basin'] and storm_features[col].dtype in ['float64', 'int64']:
                # Check if column has valid data
                valid_data = storm_features[col].dropna()
                if len(valid_data) > 0 and valid_data.std() > 0:  # Only include columns with variance
                    feature_cols.append(col)
        
        if len(feature_cols) == 0:
            raise ValueError("No valid numeric features found for clustering")
        
        logging.info(f"Using {len(feature_cols)} features for clustering: {feature_cols}")
        
        X = storm_features[feature_cols].fillna(0)
        
        # Check if we have enough samples
        if len(X) < 2:
            raise ValueError("Need at least 2 storms for clustering")
        
        # Standardize features
        scaler = StandardScaler()
        X_scaled = scaler.fit_transform(X)
        
        # Perform dimensionality reduction
        if method.lower() == 'umap' and UMAP_AVAILABLE and len(X_scaled) >= 4:
            # UMAP parameters optimized for typhoon data - fixed warnings
            n_neighbors = min(15, len(X_scaled) - 1)
            reducer = umap.UMAP(
                n_components=n_components,
                n_neighbors=n_neighbors,
                min_dist=0.1,
                metric='euclidean',
                random_state=42,
                n_jobs=1  # Explicitly set to avoid warning
            )
        elif method.lower() == 'tsne' and len(X_scaled) >= 4:
            # t-SNE parameters
            perplexity = min(30, len(X_scaled) // 4)
            perplexity = max(1, perplexity)  # Ensure perplexity is at least 1
            reducer = TSNE(
                n_components=n_components,
                perplexity=perplexity,
                learning_rate=200,
                n_iter=1000,
                random_state=42
            )
        else:
            # Fallback to PCA
            reducer = PCA(n_components=n_components, random_state=42)
        
        # Fit and transform
        embedding = reducer.fit_transform(X_scaled)
        
        logging.info(f"Dimensionality reduction successful: {X_scaled.shape} -> {embedding.shape}")
        
        return embedding, feature_cols, scaler
        
    except Exception as e:
        logging.error(f"Error in perform_dimensionality_reduction: {e}")
        raise

def cluster_storms_data(embedding, method='dbscan', eps=0.5, min_samples=3):
    """Cluster storms based on their embedding - FIXED NAME VERSION"""
    try:
        if len(embedding) < 2:
            return np.array([0] * len(embedding))  # Single cluster for insufficient data
        
        if method.lower() == 'dbscan':
            # Adjust min_samples based on data size
            min_samples = min(min_samples, max(2, len(embedding) // 5))
            clusterer = DBSCAN(eps=eps, min_samples=min_samples)
        elif method.lower() == 'kmeans':
            # Adjust n_clusters based on data size
            n_clusters = min(5, max(2, len(embedding) // 3))
            clusterer = KMeans(n_clusters=n_clusters, random_state=42)
        else:
            raise ValueError("Method must be 'dbscan' or 'kmeans'")
        
        clusters = clusterer.fit_predict(embedding)
        
        logging.info(f"Clustering complete: {len(np.unique(clusters))} clusters found")
        
        return clusters
        
    except Exception as e:
        logging.error(f"Error in cluster_storms_data: {e}")
        # Return single cluster as fallback
        return np.array([0] * len(embedding))

def create_separate_clustering_plots(storm_features, typhoon_data, method='umap'):
    """Create separate plots for clustering analysis - ENHANCED CLARITY VERSION"""
    try:
        # Validate inputs
        if storm_features is None or storm_features.empty:
            raise ValueError("No storm features available for clustering")
            
        if typhoon_data is None or typhoon_data.empty:
            raise ValueError("No typhoon data available for route visualization")
        
        logging.info(f"Starting clustering visualization with {len(storm_features)} storms")
        
        # Perform dimensionality reduction
        embedding, feature_cols, scaler = perform_dimensionality_reduction(storm_features, method)
        
        # Perform clustering
        cluster_labels = cluster_storms_data(embedding, 'dbscan')
        
        # Add clustering results to storm features
        storm_features_viz = storm_features.copy()
        storm_features_viz['cluster'] = cluster_labels
        storm_features_viz['dim1'] = embedding[:, 0]
        storm_features_viz['dim2'] = embedding[:, 1]
        
        # Merge with typhoon data for additional info - SAFE MERGE
        try:
            storm_info = typhoon_data.groupby('SID').first()[['NAME', 'SEASON']].reset_index()
            storm_features_viz = storm_features_viz.merge(storm_info, on='SID', how='left')
            # Fill missing values
            storm_features_viz['NAME'] = storm_features_viz['NAME'].fillna('UNNAMED')
            storm_features_viz['SEASON'] = storm_features_viz['SEASON'].fillna(2000)
        except Exception as merge_error:
            logging.warning(f"Could not merge storm info: {merge_error}")
            storm_features_viz['NAME'] = 'UNNAMED'
            storm_features_viz['SEASON'] = 2000
        
        # Get unique clusters and assign distinct colors
        unique_clusters = sorted([c for c in storm_features_viz['cluster'].unique() if c != -1])
        noise_count = len(storm_features_viz[storm_features_viz['cluster'] == -1])
        
        # 1. Enhanced clustering scatter plot with clear cluster identification
        fig_cluster = go.Figure()
        
        # Add noise points first
        if noise_count > 0:
            noise_data = storm_features_viz[storm_features_viz['cluster'] == -1]
            fig_cluster.add_trace(
                go.Scatter(
                    x=noise_data['dim1'],
                    y=noise_data['dim2'],
                    mode='markers',
                    marker=dict(color='lightgray', size=8, opacity=0.5, symbol='x'),
                    name=f'Noise ({noise_count} storms)',
                    hovertemplate=(
                        '<b>%{customdata[0]}</b><br>'
                        'Season: %{customdata[1]}<br>'
                        'Cluster: Noise<br>'
                        f'{method.upper()} Dim 1: %{{x:.2f}}<br>'
                        f'{method.upper()} Dim 2: %{{y:.2f}}<br>'
                        '<extra></extra>'
                    ),
                    customdata=np.column_stack((
                        noise_data['NAME'].fillna('UNNAMED'),
                        noise_data['SEASON'].fillna(2000)
                    ))
                )
            )
        
        # Add clusters with distinct colors and shapes
        cluster_symbols = ['circle', 'square', 'diamond', 'triangle-up', 'triangle-down', 
                          'pentagon', 'hexagon', 'star', 'cross', 'circle-open']
        
        for i, cluster in enumerate(unique_clusters):
            cluster_data = storm_features_viz[storm_features_viz['cluster'] == cluster]
            color = CLUSTER_COLORS[i % len(CLUSTER_COLORS)]
            symbol = cluster_symbols[i % len(cluster_symbols)]
            
            fig_cluster.add_trace(
                go.Scatter(
                    x=cluster_data['dim1'],
                    y=cluster_data['dim2'],
                    mode='markers',
                    marker=dict(color=color, size=10, symbol=symbol, line=dict(width=1, color='white')),
                    name=f'Cluster {cluster} ({len(cluster_data)} storms)',
                    hovertemplate=(
                        '<b>%{customdata[0]}</b><br>'
                        'Season: %{customdata[1]}<br>'
                        f'Cluster: {cluster}<br>'
                        f'{method.upper()} Dim 1: %{{x:.2f}}<br>'
                        f'{method.upper()} Dim 2: %{{y:.2f}}<br>'
                        'Intensity: %{customdata[2]:.0f} kt<br>'
                        '<extra></extra>'
                    ),
                    customdata=np.column_stack((
                        cluster_data['NAME'].fillna('UNNAMED'),
                        cluster_data['SEASON'].fillna(2000),
                        cluster_data['USA_WIND_max'].fillna(0)
                    ))
                )
            )
        
        fig_cluster.update_layout(
            title=f'Storm Clustering Analysis using {method.upper()}<br><sub>Each symbol/color represents a distinct storm pattern group</sub>',
            xaxis_title=f'{method.upper()} Dimension 1',
            yaxis_title=f'{method.upper()} Dimension 2',
            height=600,
            showlegend=True
        )
        
        # 2. ENHANCED route map with cluster legends and clearer representation
        fig_routes = go.Figure()
        
        # Create a comprehensive legend showing cluster characteristics
        cluster_info_text = []
        
        for i, cluster in enumerate(unique_clusters):
            cluster_storm_ids = storm_features_viz[storm_features_viz['cluster'] == cluster]['SID'].tolist()
            color = CLUSTER_COLORS[i % len(CLUSTER_COLORS)]
            
            # Get cluster statistics for legend
            cluster_data = storm_features_viz[storm_features_viz['cluster'] == cluster]
            avg_intensity = cluster_data['USA_WIND_max'].mean() if 'USA_WIND_max' in cluster_data.columns else 0
            avg_pressure = cluster_data['USA_PRES_min'].mean() if 'USA_PRES_min' in cluster_data.columns else 1000
            
            cluster_info_text.append(
                f"Cluster {cluster}: {len(cluster_storm_ids)} storms, "
                f"Avg: {avg_intensity:.0f}kt/{avg_pressure:.0f}hPa"
            )
            
            # Add multiple storms per cluster with clear identification
            storms_added = 0
            for j, sid in enumerate(cluster_storm_ids[:8]):  # Show up to 8 storms per cluster
                try:
                    storm_track = typhoon_data[typhoon_data['SID'] == sid].sort_values('ISO_TIME')
                    if len(storm_track) > 1:
                        # Ensure valid coordinates
                        valid_coords = storm_track['LAT'].notna() & storm_track['LON'].notna()
                        storm_track = storm_track[valid_coords]
                        
                        if len(storm_track) > 1:
                            storm_name = storm_track['NAME'].iloc[0] if pd.notna(storm_track['NAME'].iloc[0]) else 'UNNAMED'
                            storm_season = storm_track['SEASON'].iloc[0] if 'SEASON' in storm_track.columns else 'Unknown'
                            
                            # Vary line style for different storms in same cluster
                            line_styles = ['solid', 'dash', 'dot', 'dashdot']
                            line_style = line_styles[j % len(line_styles)]
                            line_width = 3 if j == 0 else 2  # First storm thicker
                            
                            fig_routes.add_trace(
                                go.Scattergeo(
                                    lon=storm_track['LON'],
                                    lat=storm_track['LAT'],
                                    mode='lines+markers',
                                    line=dict(color=color, width=line_width, dash=line_style),
                                    marker=dict(color=color, size=3),
                                    name=f'C{cluster}: {storm_name} ({storm_season})',
                                    showlegend=True,
                                    legendgroup=f'cluster_{cluster}',
                                    hovertemplate=(
                                        f'<b>Cluster {cluster}: {storm_name}</b><br>'
                                        'Lat: %{lat:.1f}°<br>'
                                        'Lon: %{lon:.1f}°<br>'
                                        f'Season: {storm_season}<br>'
                                        f'Pattern Group: {cluster}<br>'
                                        '<extra></extra>'
                                    )
                                )
                            )
                            storms_added += 1
                except Exception as track_error:
                    logging.warning(f"Error adding track for storm {sid}: {track_error}")
                    continue
            
            # Add cluster centroid marker
            if len(cluster_storm_ids) > 0:
                # Calculate average genesis location for cluster
                cluster_storm_data = storm_features_viz[storm_features_viz['cluster'] == cluster]
                if 'genesis_lat' in cluster_storm_data.columns and 'genesis_lon' in cluster_storm_data.columns:
                    avg_lat = cluster_storm_data['genesis_lat'].mean()
                    avg_lon = cluster_storm_data['genesis_lon'].mean()
                    
                    fig_routes.add_trace(
                        go.Scattergeo(
                            lon=[avg_lon],
                            lat=[avg_lat],
                            mode='markers',
                            marker=dict(
                                color=color, 
                                size=20, 
                                symbol='star',
                                line=dict(width=2, color='white')
                            ),
                            name=f'C{cluster} Center',
                            showlegend=True,
                            legendgroup=f'cluster_{cluster}',
                            hovertemplate=(
                                f'<b>Cluster {cluster} Genesis Center</b><br>'
                                f'Avg Position: {avg_lat:.1f}°N, {avg_lon:.1f}°E<br>'
                                f'Storms: {len(cluster_storm_ids)}<br>'
                                f'Avg Intensity: {avg_intensity:.0f} kt<br>'
                                '<extra></extra>'
                            )
                        )
                    )
        
        # Update route map layout with enhanced information and LARGER SIZE
        fig_routes.update_layout(
            title=f"Storm Routes by {method.upper()} Clusters<br><sub>Different line styles = different storms in same cluster | Stars = cluster centers</sub>",
            geo=dict(
                projection_type="natural earth",
                showland=True,
                landcolor="LightGray",
                showocean=True,
                oceancolor="LightBlue",
                showcoastlines=True,
                coastlinecolor="Gray",
                center=dict(lat=20, lon=140),
                projection_scale=2.5  # Larger map
            ),
            height=800,  # Much larger height
            width=1200,  # Wider map
            showlegend=True
        )
        
        # Add cluster info annotation
        cluster_summary = "<br>".join(cluster_info_text)
        fig_routes.add_annotation(
            text=f"<b>Cluster Summary:</b><br>{cluster_summary}",
            xref="paper", yref="paper",
            x=0.02, y=0.98,
            showarrow=False,
            align="left",
            bgcolor="rgba(255,255,255,0.8)",
            bordercolor="gray",
            borderwidth=1
        )
        
        # 3. Enhanced pressure evolution plot with cluster identification
        fig_pressure = go.Figure()
        
        for i, cluster in enumerate(unique_clusters):
            cluster_storm_ids = storm_features_viz[storm_features_viz['cluster'] == cluster]['SID'].tolist()
            color = CLUSTER_COLORS[i % len(CLUSTER_COLORS)]
            
            cluster_pressures = []
            for j, sid in enumerate(cluster_storm_ids[:5]):  # Limit to 5 storms per cluster
                try:
                    storm_track = typhoon_data[typhoon_data['SID'] == sid].sort_values('ISO_TIME')
                    if len(storm_track) > 1 and 'USA_PRES' in storm_track.columns:
                        pressure_values = pd.to_numeric(storm_track['USA_PRES'], errors='coerce').dropna()
                        if len(pressure_values) > 0:
                            storm_name = storm_track['NAME'].iloc[0] if pd.notna(storm_track['NAME'].iloc[0]) else 'UNNAMED'
                            time_hours = range(len(pressure_values))
                            
                            # Normalize time to show relative progression
                            normalized_time = np.linspace(0, 100, len(pressure_values))
                            
                            fig_pressure.add_trace(
                                go.Scatter(
                                    x=normalized_time,
                                    y=pressure_values,
                                    mode='lines',
                                    line=dict(color=color, width=2, dash='solid' if j == 0 else 'dash'),
                                    name=f'C{cluster}: {storm_name}' if j == 0 else None,
                                    showlegend=(j == 0),
                                    legendgroup=f'pressure_cluster_{cluster}',
                                    hovertemplate=(
                                        f'<b>Cluster {cluster}: {storm_name}</b><br>'
                                        'Progress: %{x:.0f}%<br>'
                                        'Pressure: %{y:.0f} hPa<br>'
                                        '<extra></extra>'
                                    ),
                                    opacity=0.8 if j == 0 else 0.5
                                )
                            )
                            cluster_pressures.extend(pressure_values)
                except Exception as e:
                    continue
            
            # Add cluster average line
            if cluster_pressures:
                avg_pressure = np.mean(cluster_pressures)
                fig_pressure.add_hline(
                    y=avg_pressure,
                    line_dash="dot",
                    line_color=color,
                    annotation_text=f"C{cluster} Avg: {avg_pressure:.0f}",
                    annotation_position="right"
                )
        
        fig_pressure.update_layout(
            title=f"Pressure Evolution by {method.upper()} Clusters<br><sub>Normalized timeline (0-100%) | Dotted lines = cluster averages</sub>",
            xaxis_title="Storm Progress (%)",
            yaxis_title="Pressure (hPa)",
            height=500
        )
        
        # 4. Enhanced wind evolution plot
        fig_wind = go.Figure()
        
        for i, cluster in enumerate(unique_clusters):
            cluster_storm_ids = storm_features_viz[storm_features_viz['cluster'] == cluster]['SID'].tolist()
            color = CLUSTER_COLORS[i % len(CLUSTER_COLORS)]
            
            cluster_winds = []
            for j, sid in enumerate(cluster_storm_ids[:5]):  # Limit to 5 storms per cluster
                try:
                    storm_track = typhoon_data[typhoon_data['SID'] == sid].sort_values('ISO_TIME')
                    if len(storm_track) > 1 and 'USA_WIND' in storm_track.columns:
                        wind_values = pd.to_numeric(storm_track['USA_WIND'], errors='coerce').dropna()
                        if len(wind_values) > 0:
                            storm_name = storm_track['NAME'].iloc[0] if pd.notna(storm_track['NAME'].iloc[0]) else 'UNNAMED'
                            
                            # Normalize time to show relative progression
                            normalized_time = np.linspace(0, 100, len(wind_values))
                            
                            fig_wind.add_trace(
                                go.Scatter(
                                    x=normalized_time,
                                    y=wind_values,
                                    mode='lines',
                                    line=dict(color=color, width=2, dash='solid' if j == 0 else 'dash'),
                                    name=f'C{cluster}: {storm_name}' if j == 0 else None,
                                    showlegend=(j == 0),
                                    legendgroup=f'wind_cluster_{cluster}',
                                    hovertemplate=(
                                        f'<b>Cluster {cluster}: {storm_name}</b><br>'
                                        'Progress: %{x:.0f}%<br>'
                                        'Wind: %{y:.0f} kt<br>'
                                        '<extra></extra>'
                                    ),
                                    opacity=0.8 if j == 0 else 0.5
                                )
                            )
                            cluster_winds.extend(wind_values)
                except Exception as e:
                    continue
            
            # Add cluster average line
            if cluster_winds:
                avg_wind = np.mean(cluster_winds)
                fig_wind.add_hline(
                    y=avg_wind,
                    line_dash="dot",
                    line_color=color,
                    annotation_text=f"C{cluster} Avg: {avg_wind:.0f}",
                    annotation_position="right"
                )
        
        fig_wind.update_layout(
            title=f"Wind Speed Evolution by {method.upper()} Clusters<br><sub>Normalized timeline (0-100%) | Dotted lines = cluster averages</sub>",
            xaxis_title="Storm Progress (%)",
            yaxis_title="Wind Speed (kt)",
            height=500
        )
        
        # Generate enhanced cluster statistics with clear explanations
        try:
            stats_text = f"ENHANCED {method.upper()} CLUSTER ANALYSIS RESULTS\n" + "="*60 + "\n\n"
            stats_text += f"🔍 DIMENSIONALITY REDUCTION: {method.upper()}\n"
            stats_text += f"🎯 CLUSTERING ALGORITHM: DBSCAN (automatic pattern discovery)\n"
            stats_text += f"📊 TOTAL STORMS ANALYZED: {len(storm_features_viz)}\n"
            stats_text += f"🎨 CLUSTERS DISCOVERED: {len(unique_clusters)}\n"
            if noise_count > 0:
                stats_text += f"❌ NOISE POINTS: {noise_count} storms (don't fit clear patterns)\n"
            stats_text += "\n"
            
            for cluster in sorted(storm_features_viz['cluster'].unique()):
                cluster_data = storm_features_viz[storm_features_viz['cluster'] == cluster]
                storm_count = len(cluster_data)
                
                if cluster == -1:
                    stats_text += f"❌ NOISE GROUP: {storm_count} storms\n"
                    stats_text += "   → These storms don't follow the main patterns\n"
                    stats_text += "   → May represent unique or rare storm behaviors\n\n"
                    continue
                
                stats_text += f"🎯 CLUSTER {cluster}: {storm_count} storms\n"
                stats_text += f"   🎨 Color: {CLUSTER_COLORS[cluster % len(CLUSTER_COLORS)]}\n"
                
                # Add detailed statistics if available
                if 'USA_WIND_max' in cluster_data.columns:
                    wind_mean = cluster_data['USA_WIND_max'].mean()
                    wind_std = cluster_data['USA_WIND_max'].std()
                    stats_text += f"   💨 Intensity: {wind_mean:.1f} ± {wind_std:.1f} kt\n"
                
                if 'USA_PRES_min' in cluster_data.columns:
                    pres_mean = cluster_data['USA_PRES_min'].mean()
                    pres_std = cluster_data['USA_PRES_min'].std()
                    stats_text += f"   🌡️ Pressure: {pres_mean:.1f} ± {pres_std:.1f} hPa\n"
                
                if 'track_length' in cluster_data.columns:
                    track_mean = cluster_data['track_length'].mean()
                    stats_text += f"   📏 Avg Track Length: {track_mean:.1f} points\n"
                
                if 'genesis_lat' in cluster_data.columns and 'genesis_lon' in cluster_data.columns:
                    lat_mean = cluster_data['genesis_lat'].mean()
                    lon_mean = cluster_data['genesis_lon'].mean()
                    stats_text += f"   🎯 Genesis Region: {lat_mean:.1f}°N, {lon_mean:.1f}°E\n"
                
                # Add interpretation
                if wind_mean < 50:
                    stats_text += "   💡 Pattern: Weaker storm group\n"
                elif wind_mean > 100:
                    stats_text += "   💡 Pattern: Intense storm group\n"
                else:
                    stats_text += "   💡 Pattern: Moderate intensity group\n"
                
                stats_text += "\n"
            
            # Add explanation of the analysis
            stats_text += "📖 INTERPRETATION GUIDE:\n"
            stats_text += f"• {method.upper()} reduces storm characteristics to 2D for visualization\n"
            stats_text += "• DBSCAN finds natural groupings without preset number of clusters\n"
            stats_text += "• Each cluster represents storms with similar behavior patterns\n"
            stats_text += "• Route colors match cluster colors from the similarity plot\n"
            stats_text += "• Stars on map show average genesis locations for each cluster\n"
            stats_text += "• Temporal plots show how each cluster behaves over time\n\n"
            
            stats_text += f"🔧 FEATURES USED FOR CLUSTERING:\n"
            stats_text += f"   Total: {len(feature_cols)} storm characteristics\n"
            stats_text += f"   Including: intensity, pressure, track shape, genesis location\n"
            
        except Exception as stats_error:
            stats_text = f"Error generating enhanced statistics: {str(stats_error)}"
        
        return fig_cluster, fig_routes, fig_pressure, fig_wind, stats_text
        
    except Exception as e:
        logging.error(f"Error in enhanced clustering analysis: {e}")
        import traceback
        traceback.print_exc()
        
        error_fig = go.Figure()
        error_fig.add_annotation(
            text=f"Error in clustering analysis: {str(e)}",
            xref="paper", yref="paper",
            x=0.5, y=0.5, xanchor='center', yanchor='middle',
            showarrow=False, font_size=16
        )
        return error_fig, error_fig, error_fig, error_fig, f"Error in clustering: {str(e)}"

# -----------------------------
# ENHANCED: Advanced Prediction System with Route Forecasting
# -----------------------------

def create_advanced_prediction_model(typhoon_data):
    """Create advanced ML model for intensity and route prediction"""
    try:
        if typhoon_data is None or typhoon_data.empty:
            return None, "No data available for model training"
        
        # Prepare training data
        features = []
        targets = []
        
        for sid in typhoon_data['SID'].unique():
            storm_data = typhoon_data[typhoon_data['SID'] == sid].sort_values('ISO_TIME')
            
            if len(storm_data) < 3:  # Need at least 3 points for prediction
                continue
            
            for i in range(len(storm_data) - 1):
                current = storm_data.iloc[i]
                next_point = storm_data.iloc[i + 1]
                
                # Extract features (current state)
                feature_row = []
                
                # Current position
                feature_row.extend([
                    current.get('LAT', 20),
                    current.get('LON', 140)
                ])
                
                # Current intensity
                feature_row.extend([
                    current.get('USA_WIND', 30),
                    current.get('USA_PRES', 1000)
                ])
                
                # Time features
                if 'ISO_TIME' in current and pd.notna(current['ISO_TIME']):
                    month = current['ISO_TIME'].month
                    day_of_year = current['ISO_TIME'].dayofyear
                else:
                    month = 9  # Peak season default
                    day_of_year = 250
                
                feature_row.extend([month, day_of_year])
                
                # Motion features (if previous point exists)
                if i > 0:
                    prev = storm_data.iloc[i - 1]
                    dlat = current.get('LAT', 20) - prev.get('LAT', 20)
                    dlon = current.get('LON', 140) - prev.get('LON', 140)
                    speed = np.sqrt(dlat**2 + dlon**2)
                    bearing = np.arctan2(dlat, dlon)
                else:
                    speed = 0
                    bearing = 0
                
                feature_row.extend([speed, bearing])
                
                features.append(feature_row)
                
                # Target: next position and intensity
                targets.append([
                    next_point.get('LAT', 20),
                    next_point.get('LON', 140),
                    next_point.get('USA_WIND', 30)
                ])
        
        if len(features) < 10:  # Need sufficient training data
            return None, "Insufficient data for model training"
        
        # Train model
        X = np.array(features)
        y = np.array(targets)
        
        # Split data
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
        
        # Create separate models for position and intensity
        models = {}
        
        # Position model (lat, lon)
        pos_model = RandomForestRegressor(n_estimators=100, random_state=42)
        pos_model.fit(X_train, y_train[:, :2])
        models['position'] = pos_model
        
        # Intensity model (wind speed)
        int_model = RandomForestRegressor(n_estimators=100, random_state=42)
        int_model.fit(X_train, y_train[:, 2])
        models['intensity'] = int_model
        
        # Calculate model performance
        pos_pred = pos_model.predict(X_test)
        int_pred = int_model.predict(X_test)
        
        pos_mae = mean_absolute_error(y_test[:, :2], pos_pred)
        int_mae = mean_absolute_error(y_test[:, 2], int_pred)
        
        model_info = f"Position MAE: {pos_mae:.2f}°, Intensity MAE: {int_mae:.2f} kt"
        
        return models, model_info
        
    except Exception as e:
        return None, f"Error creating prediction model: {str(e)}"

def get_realistic_genesis_locations():
    """Get realistic typhoon genesis regions based on climatology"""
    return {
        "Western Pacific Main Development Region": {"lat": 12.5, "lon": 145.0, "description": "Peak activity zone (Guam area)"},
        "South China Sea": {"lat": 15.0, "lon": 115.0, "description": "Secondary development region"},
        "Philippine Sea": {"lat": 18.0, "lon": 135.0, "description": "Recurving storm region"},
        "Marshall Islands": {"lat": 8.0, "lon": 165.0, "description": "Eastern development zone"},
        "Monsoon Trough": {"lat": 10.0, "lon": 130.0, "description": "Monsoon-driven genesis"},
        "ITCZ Region": {"lat": 6.0, "lon": 140.0, "description": "Near-equatorial development"},
        "Subtropical Region": {"lat": 22.0, "lon": 125.0, "description": "Late season development"},
        "Bay of Bengal": {"lat": 15.0, "lon": 88.0, "description": "Indian Ocean cyclones"},
        "Eastern Pacific": {"lat": 12.0, "lon": -105.0, "description": "Hurricane development zone"},
        "Atlantic MDR": {"lat": 12.0, "lon": -45.0, "description": "Main Development Region"}
    }

def predict_storm_route_and_intensity_realistic(genesis_region, month, oni_value, models=None, forecast_hours=72, use_advanced_physics=True):
    """Realistic prediction starting from TD intensity in proper genesis regions"""
    try:
        genesis_locations = get_realistic_genesis_locations()
        
        if genesis_region not in genesis_locations:
            genesis_region = "Western Pacific Main Development Region"  # Default
        
        genesis_info = genesis_locations[genesis_region]
        lat = genesis_info["lat"]
        lon = genesis_info["lon"]
        
        results = {
            'current_prediction': {},
            'route_forecast': [],
            'confidence_scores': {},
            'model_info': 'Realistic Genesis Model',
            'genesis_info': genesis_info
        }
        
        # REALISTIC starting intensity - Tropical Depression level
        base_intensity = 28  # Start at TD level (25-33 kt)
        
        # Environmental factors for genesis (much smaller effects initially)
        if oni_value > 1.0:  # Strong El Niño - suppressed development
            intensity_modifier = -5
        elif oni_value > 0.5:  # Moderate El Niño
            intensity_modifier = -3
        elif oni_value < -1.0:  # Strong La Niña - enhanced development
            intensity_modifier = +8
        elif oni_value < -0.5:  # Moderate La Niña
            intensity_modifier = +5
        else:  # Neutral
            intensity_modifier = oni_value * 2
        
        # Seasonal genesis effects (realistic for TD formation)
        seasonal_factors = {
            1: -8, 2: -6, 3: -4, 4: -2, 5: 2, 6: 5,
            7: 8, 8: 10, 9: 12, 10: 8, 11: 4, 12: -5
        }
        seasonal_modifier = seasonal_factors.get(month, 0)
        
        # Genesis region favorability
        region_factors = {
            "Western Pacific Main Development Region": 6,
            "South China Sea": 3,
            "Philippine Sea": 4,
            "Marshall Islands": 5,
            "Monsoon Trough": 4,
            "ITCZ Region": 2,
            "Subtropical Region": 1,
            "Bay of Bengal": 3,
            "Eastern Pacific": 5,
            "Atlantic MDR": 4
        }
        region_modifier = region_factors.get(genesis_region, 0)
        
        # Calculate realistic starting intensity (TD level)
        predicted_intensity = base_intensity + intensity_modifier + seasonal_modifier + region_modifier
        predicted_intensity = max(25, min(40, predicted_intensity))  # Cap at weak TS level
        
        # Add realistic uncertainty for genesis
        intensity_uncertainty = np.random.normal(0, 2)
        predicted_intensity += intensity_uncertainty
        predicted_intensity = max(25, min(38, predicted_intensity))  # Keep in TD-weak TS range
        
        results['current_prediction'] = {
            'intensity_kt': predicted_intensity,
            'pressure_hpa': 1008 - (predicted_intensity - 25) * 0.5,  # Realistic TD pressure
            'category': categorize_typhoon_enhanced(predicted_intensity),
            'genesis_region': genesis_region
        }
        
        # Enhanced route prediction with REALISTIC development cycle
        current_lat = lat
        current_lon = lon
        current_intensity = predicted_intensity
        
        route_points = []
        
        # Track storm development over time
        for hour in range(0, forecast_hours + 6, 6):
            
            # Realistic storm motion based on beta drift and steering
            # Beta drift (Coriolis effect)
            beta_drift_lat = 0.03 * np.sin(np.radians(current_lat))
            beta_drift_lon = -0.08 * np.cos(np.radians(current_lat))
            
            # Seasonal steering patterns
            if month in [6, 7, 8, 9]:  # Peak season
                ridge_strength = 1.0
                ridge_position = 30 + 3 * np.sin(2 * np.pi * (month - 6) / 4)
            else:  # Off season
                ridge_strength = 0.7
                ridge_position = 25
            
            # Motion based on position relative to subtropical ridge
            if current_lat < ridge_position - 8:  # Well south of ridge
                lat_tendency = 0.1 + beta_drift_lat
                lon_tendency = -0.35 + beta_drift_lon
            elif current_lat > ridge_position - 3:  # Near ridge - recurvature
                lat_tendency = 0.25 + beta_drift_lat
                lon_tendency = 0.05 + beta_drift_lon
            else:  # In between
                lat_tendency = 0.15 + beta_drift_lat
                lon_tendency = -0.25 + beta_drift_lon
            
            # ENSO steering modulation
            if oni_value > 0.5:  # El Niño - more eastward motion
                lon_tendency += 0.1
            elif oni_value < -0.5:  # La Niña - more westward
                lon_tendency -= 0.08
            
            # Add motion uncertainty that grows with time
            motion_uncertainty = 0.02 + (hour / 72) * 0.03
            lat_noise = np.random.normal(0, motion_uncertainty)
            lon_noise = np.random.normal(0, motion_uncertainty)
            
            # Update position
            current_lat += lat_tendency + lat_noise
            current_lon += lon_tendency + lon_noise
            
            # REALISTIC intensity evolution - storms don't just weaken!
            
            # Development phase (first 48-72 hours)
            if hour <= 48:
                if current_intensity < 65:  # Still developing
                    if 15 <= current_lat <= 25 and 120 <= current_lon <= 160:  # Favorable environment
                        intensity_tendency = 3.5 if current_intensity < 45 else 2.0
                    else:
                        intensity_tendency = 1.5
                else:  # Already strong
                    intensity_tendency = 0.5
            # Mature phase (48-96 hours)
            elif hour <= 96:
                if current_lat < 30 and current_lon > 120:  # Still in favorable waters
                    intensity_tendency = 0.5 if current_intensity < 100 else -0.5
                else:
                    intensity_tendency = -1.0
            # Decay phase (96+ hours)
            else:
                intensity_tendency = -1.5
            
            # Environmental modulation
            if current_lat > 35:  # High latitude - rapid weakening
                intensity_tendency -= 6
            elif current_lat > 30:  # Moderate latitude
                intensity_tendency -= 2
            elif current_lon < 115:  # Land interaction
                intensity_tendency -= 8
            elif 125 <= current_lon <= 155 and 10 <= current_lat <= 25:  # Warm pool
                intensity_tendency += 1
            
            # SST effects (realistic)
            if current_lat < 8:  # Very warm but near equator (weak Coriolis)
                intensity_tendency += 0.5
            elif 8 <= current_lat <= 20:  # Sweet spot
                intensity_tendency += 1.5
            elif current_lat > 30:  # Cooler waters
                intensity_tendency -= 3
            
            # Update intensity with realistic bounds
            current_intensity += intensity_tendency
            current_intensity = max(20, min(180, current_intensity))  # Realistic range
            
            # Calculate confidence based on forecast time and environment
            base_confidence = 0.9
            time_penalty = (hour / 72) * 0.4
            environment_penalty = 0.1 if current_lat > 30 or current_lon < 120 else 0
            confidence = max(0.25, base_confidence - time_penalty - environment_penalty)
            
            route_points.append({
                'hour': hour,
                'lat': current_lat,
                'lon': current_lon,
                'intensity_kt': current_intensity,
                'category': categorize_typhoon_enhanced(current_intensity),
                'confidence': confidence,
                'development_stage': 'Genesis' if hour <= 24 else ('Development' if hour <= 72 else ('Mature' if hour <= 120 else 'Decay'))
            })
        
        results['route_forecast'] = route_points
        
        # Realistic confidence scores
        results['confidence_scores'] = {
            'genesis': 0.85,
            'early_development': 0.80,
            'position_24h': 0.85,
            'position_48h': 0.75,
            'position_72h': 0.65,
            'intensity_24h': 0.70,
            'intensity_48h': 0.60,
            'intensity_72h': 0.50,
            'long_term': max(0.3, 0.8 - (forecast_hours / 120) * 0.5)
        }
        
        # Model information
        results['model_info'] = f"Realistic Genesis Model - {genesis_region}"
        
        return results
        
    except Exception as e:
        logging.error(f"Realistic prediction error: {str(e)}")
        return {
            'error': f"Prediction error: {str(e)}",
            'current_prediction': {'intensity_kt': 30, 'category': 'Tropical Depression'},
            'route_forecast': [],
            'confidence_scores': {},
            'model_info': 'Error in prediction'
        }

def create_animated_route_visualization(prediction_results, show_uncertainty=True, enable_animation=True):
    """Create animated and expandable route visualization"""
    try:
        if 'route_forecast' not in prediction_results or not prediction_results['route_forecast']:
            return None, "No route forecast data available"
        
        route_data = prediction_results['route_forecast']
        
        # Extract data for plotting
        hours = [point['hour'] for point in route_data]
        lats = [point['lat'] for point in route_data]
        lons = [point['lon'] for point in route_data]
        intensities = [point['intensity_kt'] for point in route_data]
        categories = [point['category'] for point in route_data]
        confidences = [point.get('confidence', 0.8) for point in route_data]
        stages = [point.get('development_stage', 'Unknown') for point in route_data]
        
        if enable_animation:
            # Create animated figure
            fig = go.Figure()
            
            # Add complete track as background
            fig.add_trace(
                go.Scattergeo(
                    lon=lons,
                    lat=lats,
                    mode='lines',
                    line=dict(color='lightgray', width=2, dash='dot'),
                    name='Complete Track',
                    showlegend=True,
                    opacity=0.5
                )
            )
            
            # Add frames for animation
            frames = []
            for i in range(len(route_data)):
                frame_lons = lons[:i+1]
                frame_lats = lats[:i+1]
                frame_intensities = intensities[:i+1]
                frame_categories = categories[:i+1]
                
                # Current position marker
                current_color = enhanced_color_map.get(frame_categories[-1], 'rgb(128,128,128)')
                current_size = 15 + (frame_intensities[-1] / 10)
                
                frame_data = [
                    # Track line up to current point
                    go.Scattergeo(
                        lon=frame_lons,
                        lat=frame_lats,
                        mode='lines+markers',
                        line=dict(color='blue', width=3),
                        marker=dict(
                            size=[8 + (intensity/15) for intensity in frame_intensities],
                            color=[enhanced_color_map.get(cat, 'rgb(128,128,128)') for cat in frame_categories],
                            opacity=0.8
                        ),
                        name='Track',
                        showlegend=False
                    ),
                    # Current position highlight
                    go.Scattergeo(
                        lon=[frame_lons[-1]],
                        lat=[frame_lats[-1]],
                        mode='markers',
                        marker=dict(
                            size=current_size,
                            color=current_color,
                            symbol='star',
                            line=dict(width=2, color='white')
                        ),
                        name='Current Position',
                        showlegend=False,
                        hovertemplate=(
                            f"<b>Hour {route_data[i]['hour']}</b><br>"
                            f"Position: {lats[i]:.1f}°N, {lons[i]:.1f}°E<br>"
                            f"Intensity: {intensities[i]:.0f} kt<br>"
                            f"Category: {categories[i]}<br>"
                            f"Stage: {stages[i]}<br>"
                            f"Confidence: {confidences[i]*100:.0f}%<br>"
                            "<extra></extra>"
                        )
                    )
                ]
                
                frames.append(go.Frame(
                    data=frame_data,
                    name=str(i),
                    layout=go.Layout(
                        title=f"Storm Forecast Animation - Hour {route_data[i]['hour']}<br>"
                              f"Intensity: {intensities[i]:.0f} kt | Category: {categories[i]} | Stage: {stages[i]}"
                    )
                ))
            
            fig.frames = frames
            
            # Add play/pause buttons
            fig.update_layout(
                updatemenus=[
                    {
                        "buttons": [
                            {
                                "args": [None, {"frame": {"duration": 800, "redraw": True},
                                              "fromcurrent": True, "transition": {"duration": 300}}],
                                "label": "▶️ Play",
                                "method": "animate"
                            },
                            {
                                "args": [[None], {"frame": {"duration": 0, "redraw": True},
                                                "mode": "immediate", "transition": {"duration": 0}}],
                                "label": "⏸️ Pause",
                                "method": "animate"
                            }
                        ],
                        "direction": "left",
                        "pad": {"r": 10, "t": 87},
                        "showactive": False,
                        "type": "buttons",
                        "x": 0.1,
                        "xanchor": "right",
                        "y": 0,
                        "yanchor": "top"
                    }
                ],
                sliders=[{
                    "active": 0,
                    "yanchor": "top",
                    "xanchor": "left",
                    "currentvalue": {
                        "font": {"size": 20},
                        "prefix": "Hour:",
                        "visible": True,
                        "xanchor": "right"
                    },
                    "transition": {"duration": 300, "easing": "cubic-in-out"},
                    "pad": {"b": 10, "t": 50},
                    "len": 0.9,
                    "x": 0.1,
                    "y": 0,
                    "steps": [
                        {
                            "args": [[str(i)], {"frame": {"duration": 300, "redraw": True},
                                               "mode": "immediate", "transition": {"duration": 300}}],
                            "label": f"Hour {route_data[i]['hour']}",
                            "method": "animate"
                        }
                        for i in range(len(route_data))
                    ]
                }]
            )
            
        else:
            # Create static expandable figure
            fig = go.Figure()
            
            # Add full track with intensity coloring
            for i in range(len(route_data)):
                point = route_data[i]
                color = enhanced_color_map.get(point['category'], 'rgb(128,128,128)')
                size = 8 + (point['intensity_kt'] / 12)
                
                # Genesis point special marker
                if i == 0:
                    fig.add_trace(
                        go.Scattergeo(
                            lon=[point['lon']],
                            lat=[point['lat']],
                            mode='markers',
                            marker=dict(
                                size=20,
                                color='yellow',
                                symbol='star',
                                line=dict(width=3, color='black')
                            ),
                            name='Genesis',
                            showlegend=True,
                            hovertemplate=(
                                f"<b>GENESIS - {prediction_results['genesis_info']['description']}</b><br>"
                                f"Position: {point['lat']:.1f}°N, {point['lon']:.1f}°E<br>"
                                f"Initial Intensity: {point['intensity_kt']:.0f} kt<br>"
                                f"Category: {point['category']}<br>"
                                "<extra></extra>"
                            )
                        )
                    )
                
                # Regular track points
                fig.add_trace(
                    go.Scattergeo(
                        lon=[point['lon']],
                        lat=[point['lat']],
                        mode='markers',
                        marker=dict(
                            size=size,
                            color=color,
                            opacity=point.get('confidence', 0.8),
                            line=dict(width=1, color='white')
                        ),
                        name=f"Hour {point['hour']}" if i % 8 == 0 else None,
                        showlegend=(i % 8 == 0),
                        hovertemplate=(
                            f"<b>Hour {point['hour']}</b><br>"
                            f"Position: {point['lat']:.1f}°N, {point['lon']:.1f}°E<br>"
                            f"Intensity: {point['intensity_kt']:.0f} kt<br>"
                            f"Category: {point['category']}<br>"
                            f"Stage: {point.get('development_stage', 'Unknown')}<br>"
                            f"Confidence: {point.get('confidence', 0.8)*100:.0f}%<br>"
                            "<extra></extra>"
                        )
                    )
                )
            
            # Connect points with track line
            fig.add_trace(
                go.Scattergeo(
                    lon=lons,
                    lat=lats,
                    mode='lines',
                    line=dict(color='black', width=3),
                    name='Forecast Track',
                    showlegend=True
                )
            )
        
        # Add uncertainty cone if requested
        if show_uncertainty and len(route_data) > 1:
            uncertainty_lats_upper = []
            uncertainty_lats_lower = []
            uncertainty_lons_upper = []
            uncertainty_lons_lower = []
            
            for i, point in enumerate(route_data):
                # Uncertainty grows with time and decreases with confidence
                base_uncertainty = 0.3 + (i / len(route_data)) * 1.2
                confidence_factor = point.get('confidence', 0.8)
                uncertainty = base_uncertainty / confidence_factor
                
                uncertainty_lats_upper.append(point['lat'] + uncertainty)
                uncertainty_lats_lower.append(point['lat'] - uncertainty)
                uncertainty_lons_upper.append(point['lon'] + uncertainty)
                uncertainty_lons_lower.append(point['lon'] - uncertainty)
            
            uncertainty_lats = uncertainty_lats_upper + uncertainty_lats_lower[::-1]
            uncertainty_lons = uncertainty_lons_upper + uncertainty_lons_lower[::-1]
            
            fig.add_trace(
                go.Scattergeo(
                    lon=uncertainty_lons,
                    lat=uncertainty_lats,
                    mode='lines',
                    fill='toself',
                    fillcolor='rgba(128,128,128,0.15)',
                    line=dict(color='rgba(128,128,128,0.4)', width=1),
                    name='Uncertainty Cone',
                    showlegend=True
                )
            )
        
        # Enhanced layout with larger size
        fig.update_layout(
            title=f"Realistic Storm Development Forecast<br><sub>Starting from {prediction_results['genesis_info']['description']}</sub>",
            geo=dict(
                projection_type="natural earth",
                showland=True,
                landcolor="LightGray",
                showocean=True,
                oceancolor="LightBlue",
                showcoastlines=True,
                coastlinecolor="DarkGray",
                showlakes=True,
                lakecolor="LightBlue",
                center=dict(lat=np.mean(lats), lon=np.mean(lons)),
                projection_scale=2.5
            ),
            height=900,  # Much larger
            width=1400,  # Much wider
            showlegend=True
        )
        
        # Generate enhanced forecast text
        current = prediction_results['current_prediction']
        genesis_info = prediction_results['genesis_info']
        
        forecast_text = f"""
REALISTIC STORM DEVELOPMENT FORECAST
{'='*60}

GENESIS CONDITIONS:
• Region: {current.get('genesis_region', 'Unknown')}
• Description: {genesis_info['description']}
• Starting Position: {lats[0]:.1f}°N, {lons[0]:.1f}°E
• Initial Intensity: {current['intensity_kt']:.0f} kt (Tropical Depression)
• Genesis Pressure: {current.get('pressure_hpa', 1008):.0f} hPa
• Genesis Category: {current['category']}

DEVELOPMENT TIMELINE:
• Hour 0 (Genesis): {intensities[0]:.0f} kt - {categories[0]}
• Hour 24 (Early Dev): {intensities[min(4, len(intensities)-1)]:.0f} kt - {categories[min(4, len(categories)-1)]}
• Hour 48 (Development): {intensities[min(8, len(intensities)-1)]:.0f} kt - {categories[min(8, len(categories)-1)]}
• Hour 72 (Mature): {intensities[min(12, len(intensities)-1)]:.0f} kt - {categories[min(12, len(categories)-1)]}

FORECAST TRACK:
• Genesis: {lats[0]:.1f}°N, {lons[0]:.1f}°E
• 24-hour: {lats[min(4, len(lats)-1)]:.1f}°N, {lons[min(4, len(lons)-1)]:.1f}°E
• 48-hour: {lats[min(8, len(lats)-1)]:.1f}°N, {lons[min(8, len(lons)-1)]:.1f}°E
• 72-hour: {lats[min(12, len(lats)-1)]:.1f}°N, {lons[min(12, len(lons)-1)]:.1f}°E
• Final: {lats[-1]:.1f}°N, {lons[-1]:.1f}°E

DEVELOPMENT STAGES:
• Genesis (0-24h): Initial cyclogenesis and organization
• Development (24-72h): Intensification in favorable environment
• Mature (72-120h): Peak intensity and steady motion
• Decay (120h+): Gradual weakening due to environmental factors

CONFIDENCE ASSESSMENT:
• Genesis Likelihood: {prediction_results['confidence_scores'].get('genesis', 0.85)*100:.0f}%
• Early Development: {prediction_results['confidence_scores'].get('early_development', 0.80)*100:.0f}%
• 24-hour Track: {prediction_results['confidence_scores'].get('position_24h', 0.85)*100:.0f}%
• 48-hour Track: {prediction_results['confidence_scores'].get('position_48h', 0.75)*100:.0f}%
• 72-hour Track: {prediction_results['confidence_scores'].get('position_72h', 0.65)*100:.0f}%

MODEL: {prediction_results['model_info']}
ANIMATION: {"Enabled - Use controls below map" if enable_animation else "Static view - All time steps shown"}
        """
        
        return fig, forecast_text.strip()
        
    except Exception as e:
        error_msg = f"Error creating animated route visualization: {str(e)}"
        logging.error(error_msg)
        import traceback
        traceback.print_exc()
        return None, error_msg

# -----------------------------
# Regression Functions (Original)
# -----------------------------

def perform_wind_regression(start_year, start_month, end_year, end_month):
    """Perform wind regression analysis"""
    start_date = datetime(start_year, start_month, 1)
    end_date = datetime(end_year, end_month, 28)
    data = merged_data[(merged_data['ISO_TIME']>=start_date) & (merged_data['ISO_TIME']<=end_date)].dropna(subset=['USA_WIND','ONI'])
    data['severe_typhoon'] = (data['USA_WIND']>=64).astype(int)
    X = sm.add_constant(data['ONI'])
    y = data['severe_typhoon']
    try:
        model = sm.Logit(y, X).fit(disp=0)
        beta_1 = model.params['ONI']
        exp_beta_1 = np.exp(beta_1)
        p_value = model.pvalues['ONI']
        return f"Wind Regression: β1={beta_1:.4f}, Odds Ratio={exp_beta_1:.4f}, P-value={p_value:.4f}"
    except Exception as e:
        return f"Wind Regression Error: {e}"

def perform_pressure_regression(start_year, start_month, end_year, end_month):
    """Perform pressure regression analysis"""
    start_date = datetime(start_year, start_month, 1)
    end_date = datetime(end_year, end_month, 28)
    data = merged_data[(merged_data['ISO_TIME']>=start_date) & (merged_data['ISO_TIME']<=end_date)].dropna(subset=['USA_PRES','ONI'])
    data['intense_typhoon'] = (data['USA_PRES']<=950).astype(int)
    X = sm.add_constant(data['ONI'])
    y = data['intense_typhoon']
    try:
        model = sm.Logit(y, X).fit(disp=0)
        beta_1 = model.params['ONI']
        exp_beta_1 = np.exp(beta_1)
        p_value = model.pvalues['ONI']
        return f"Pressure Regression: β1={beta_1:.4f}, Odds Ratio={exp_beta_1:.4f}, P-value={p_value:.4f}"
    except Exception as e:
        return f"Pressure Regression Error: {e}"

def perform_longitude_regression(start_year, start_month, end_year, end_month):
    """Perform longitude regression analysis"""
    start_date = datetime(start_year, start_month, 1)
    end_date = datetime(end_year, end_month, 28)
    data = merged_data[(merged_data['ISO_TIME']>=start_date) & (merged_data['ISO_TIME']<=end_date)].dropna(subset=['LON','ONI'])
    data['western_typhoon'] = (data['LON']<=140).astype(int)
    X = sm.add_constant(data['ONI'])
    y = data['western_typhoon']
    try:
        model = sm.OLS(y, sm.add_constant(X)).fit()
        beta_1 = model.params['ONI']
        exp_beta_1 = np.exp(beta_1)
        p_value = model.pvalues['ONI']
        return f"Longitude Regression: β1={beta_1:.4f}, Odds Ratio={exp_beta_1:.4f}, P-value={p_value:.4f}"
    except Exception as e:
        return f"Longitude Regression Error: {e}"

# -----------------------------
# Visualization Functions (Enhanced)
# -----------------------------

def get_full_tracks(start_year, start_month, end_year, end_month, enso_phase, typhoon_search):
    """Get full typhoon tracks"""
    start_date = datetime(start_year, start_month, 1)
    end_date = datetime(end_year, end_month, 28)
    filtered_data = merged_data[(merged_data['ISO_TIME']>=start_date) & (merged_data['ISO_TIME']<=end_date)].copy()
    filtered_data['ENSO_Phase'] = filtered_data['ONI'].apply(classify_enso_phases)
    if enso_phase != 'all':
        filtered_data = filtered_data[filtered_data['ENSO_Phase'] == enso_phase.capitalize()]
    unique_storms = filtered_data['SID'].unique()
    count = len(unique_storms)
    fig = go.Figure()
    for sid in unique_storms:
        storm_data = typhoon_data[typhoon_data['SID']==sid]
        if storm_data.empty:
            continue
        name = storm_data['NAME'].iloc[0] if pd.notnull(storm_data['NAME'].iloc[0]) else "Unnamed"
        basin = storm_data['SID'].iloc[0][:2]
        storm_oni = filtered_data[filtered_data['SID']==sid]['ONI'].iloc[0]
        color = 'red' if storm_oni>=0.5 else ('blue' if storm_oni<=-0.5 else 'green')
        fig.add_trace(go.Scattergeo(
            lon=storm_data['LON'], lat=storm_data['LAT'], mode='lines',
            name=f"{name} ({basin})",
            line=dict(width=1.5, color=color), hoverinfo="name"
        ))
    if typhoon_search:
        search_mask = typhoon_data['NAME'].str.contains(typhoon_search, case=False, na=False)
        if search_mask.any():
            for sid in typhoon_data[search_mask]['SID'].unique():
                storm_data = typhoon_data[typhoon_data['SID']==sid]
                fig.add_trace(go.Scattergeo(
                    lon=storm_data['LON'], lat=storm_data['LAT'], mode='lines+markers',
                    name=f"MATCHED: {storm_data['NAME'].iloc[0]}",
                    line=dict(width=3, color='yellow'),
                    marker=dict(size=5), hoverinfo="name"
                ))
    fig.update_layout(
        title=f"Typhoon Tracks ({start_year}-{start_month} to {end_year}-{end_month})",
        geo=dict(
            projection_type='natural earth',
            showland=True,
            showcoastlines=True,
            landcolor='rgb(243,243,243)',
            countrycolor='rgb(204,204,204)',
            coastlinecolor='rgb(204,204,204)',
            center=dict(lon=140, lat=20),
            projection_scale=3
        ),
        legend_title="Typhoons by ENSO Phase",
        showlegend=True,
        height=700
    )
    fig.add_annotation(
        x=0.02, y=0.98, xref="paper", yref="paper",
        text="Red: El Niño, Blue: La Nina, Green: Neutral",
        showarrow=False, align="left",
        bgcolor="rgba(255,255,255,0.8)"
    )
    return fig, f"Total typhoons displayed: {count}"

def get_wind_analysis(start_year, start_month, end_year, end_month, enso_phase, typhoon_search):
    """Get wind analysis with enhanced categorization"""
    start_date = datetime(start_year, start_month, 1)
    end_date = datetime(end_year, end_month, 28)
    filtered_data = merged_data[(merged_data['ISO_TIME']>=start_date) & (merged_data['ISO_TIME']<=end_date)].copy()
    filtered_data['ENSO_Phase'] = filtered_data['ONI'].apply(classify_enso_phases)
    if enso_phase != 'all':
        filtered_data = filtered_data[filtered_data['ENSO_Phase'] == enso_phase.capitalize()]
    
    fig = px.scatter(filtered_data, x='ONI', y='USA_WIND', color='Category',
                     hover_data=['NAME','Year','Category'],
                     title='Wind Speed vs ONI',
                     labels={'ONI':'ONI Value','USA_WIND':'Max Wind Speed (knots)'},
                     color_discrete_map=enhanced_color_map)
    
    if typhoon_search:
        mask = filtered_data['NAME'].str.contains(typhoon_search, case=False, na=False)
        if mask.any():
            fig.add_trace(go.Scatter(
                x=filtered_data.loc[mask,'ONI'], y=filtered_data.loc[mask,'USA_WIND'],
                mode='markers', marker=dict(size=10, color='red', symbol='star'),
                name=f'Matched: {typhoon_search}',
                text=filtered_data.loc[mask,'NAME']+' ('+filtered_data.loc[mask,'Year'].astype(str)+')'
            ))
    
    regression = perform_wind_regression(start_year, start_month, end_year, end_month)
    return fig, regression

def get_pressure_analysis(start_year, start_month, end_year, end_month, enso_phase, typhoon_search):
    """Get pressure analysis with enhanced categorization"""
    start_date = datetime(start_year, start_month, 1)
    end_date = datetime(end_year, end_month, 28)
    filtered_data = merged_data[(merged_data['ISO_TIME']>=start_date) & (merged_data['ISO_TIME']<=end_date)].copy()
    filtered_data['ENSO_Phase'] = filtered_data['ONI'].apply(classify_enso_phases)
    if enso_phase != 'all':
        filtered_data = filtered_data[filtered_data['ENSO_Phase'] == enso_phase.capitalize()]
    
    fig = px.scatter(filtered_data, x='ONI', y='USA_PRES', color='Category',
                     hover_data=['NAME','Year','Category'],
                     title='Pressure vs ONI',
                     labels={'ONI':'ONI Value','USA_PRES':'Min Pressure (hPa)'},
                     color_discrete_map=enhanced_color_map)
    
    if typhoon_search:
        mask = filtered_data['NAME'].str.contains(typhoon_search, case=False, na=False)
        if mask.any():
            fig.add_trace(go.Scatter(
                x=filtered_data.loc[mask,'ONI'], y=filtered_data.loc[mask,'USA_PRES'],
                mode='markers', marker=dict(size=10, color='red', symbol='star'),
                name=f'Matched: {typhoon_search}',
                text=filtered_data.loc[mask,'NAME']+' ('+filtered_data.loc[mask,'Year'].astype(str)+')'
            ))
    
    regression = perform_pressure_regression(start_year, start_month, end_year, end_month)
    return fig, regression

def get_longitude_analysis(start_year, start_month, end_year, end_month, enso_phase, typhoon_search):
    """Get longitude analysis"""
    start_date = datetime(start_year, start_month, 1)
    end_date = datetime(end_year, end_month, 28)
    filtered_data = merged_data[(merged_data['ISO_TIME']>=start_date) & (merged_data['ISO_TIME']<=end_date)].copy()
    filtered_data['ENSO_Phase'] = filtered_data['ONI'].apply(classify_enso_phases)
    if enso_phase != 'all':
        filtered_data = filtered_data[filtered_data['ENSO_Phase'] == enso_phase.capitalize()]
    
    fig = px.scatter(filtered_data, x='LON', y='ONI', hover_data=['NAME'],
                     title='Typhoon Generation Longitude vs ONI (All Years)')
    
    if len(filtered_data) > 1:
        X = np.array(filtered_data['LON']).reshape(-1,1)
        y = filtered_data['ONI']
        try:
            model = sm.OLS(y, sm.add_constant(X)).fit()
            y_pred = model.predict(sm.add_constant(X))
            fig.add_trace(go.Scatter(x=filtered_data['LON'], y=y_pred, mode='lines', name='Regression Line'))
            slope = model.params[1]
            slopes_text = f"All Years Slope: {slope:.4f}"
        except Exception as e:
            slopes_text = f"Regression Error: {e}"
    else:
        slopes_text = "Insufficient data for regression"
    
    regression = perform_longitude_regression(start_year, start_month, end_year, end_month)
    return fig, slopes_text, regression

def categorize_typhoon_by_standard(wind_speed, standard='atlantic'):
    """Categorize typhoon by standard with enhanced TD support - FIXED for matplotlib"""
    if pd.isna(wind_speed):
        return 'Tropical Depression', '#808080'
    
    if standard=='taiwan':
        # Taiwan standard uses m/s, convert if needed
        if wind_speed > 50:  # Likely in knots, convert to m/s
            wind_speed_ms = wind_speed * 0.514444
        else:
            wind_speed_ms = wind_speed
            
        if wind_speed_ms >= 51.0:
            return 'Strong Typhoon', '#FF0000'  # Red
        elif wind_speed_ms >= 33.7:
            return 'Medium Typhoon', '#FFA500'  # Orange
        elif wind_speed_ms >= 17.2:
            return 'Mild Typhoon', '#FFFF00'   # Yellow
        return 'Tropical Depression', '#808080'  # Gray
    else:
        # Atlantic standard in knots
        if wind_speed >= 137:
            return 'C5 Super Typhoon', '#FF0000'      # Red
        elif wind_speed >= 113:
            return 'C4 Very Strong Typhoon', '#FFA500' # Orange
        elif wind_speed >= 96:
            return 'C3 Strong Typhoon', '#FFFF00'     # Yellow
        elif wind_speed >= 83:
            return 'C2 Typhoon', '#00FF00'            # Green
        elif wind_speed >= 64:
            return 'C1 Typhoon', '#00FFFF'            # Cyan
        elif wind_speed >= 34:
            return 'Tropical Storm', '#0000FF'        # Blue
        return 'Tropical Depression', '#808080'       # Gray

# -----------------------------
# ENHANCED: Animation Functions with Taiwan Standard Support
# -----------------------------

def get_available_years(typhoon_data):
    """Get all available years including 2025 - with error handling"""
    try:
        if typhoon_data is None or typhoon_data.empty:
            return [str(year) for year in range(2000, 2026)]
            
        if 'ISO_TIME' in typhoon_data.columns:
            years = typhoon_data['ISO_TIME'].dt.year.dropna().unique()
        elif 'SEASON' in typhoon_data.columns:
            years = typhoon_data['SEASON'].dropna().unique()
        else:
            years = range(2000, 2026)  # Default range including 2025
        
        # Convert to strings and sort
        year_strings = sorted([str(int(year)) for year in years if not pd.isna(year)])
        
        # Ensure we have at least some years
        if not year_strings:
            return [str(year) for year in range(2000, 2026)]
            
        return year_strings
        
    except Exception as e:
        print(f"Error in get_available_years: {e}")
        return [str(year) for year in range(2000, 2026)]

def update_typhoon_options_enhanced(year, basin):
    """Enhanced typhoon options with TD support and 2025 data"""
    try:
        year = int(year)
        
        # Filter by year - handle both ISO_TIME and SEASON columns
        if 'ISO_TIME' in typhoon_data.columns:
            year_mask = typhoon_data['ISO_TIME'].dt.year == year
        elif 'SEASON' in typhoon_data.columns:
            year_mask = typhoon_data['SEASON'] == year
        else:
            # Fallback - try to extract year from SID or other fields
            year_mask = typhoon_data.index >= 0  # Include all data as fallback
        
        year_data = typhoon_data[year_mask].copy()
        
        # Filter by basin if specified
        if basin != "All Basins":
            basin_code = basin.split(' - ')[0] if ' - ' in basin else basin[:2]
            if 'SID' in year_data.columns:
                year_data = year_data[year_data['SID'].str.startswith(basin_code, na=False)]
            elif 'BASIN' in year_data.columns:
                year_data = year_data[year_data['BASIN'] == basin_code]
        
        if year_data.empty:
            return gr.update(choices=["No storms found"], value=None)
        
        # Get unique storms - include ALL intensities (including TD)
        storms = year_data.groupby('SID').agg({
            'NAME': 'first',
            'USA_WIND': 'max'
        }).reset_index()
        
        # Enhanced categorization including TD
        storms['category'] = storms['USA_WIND'].apply(categorize_typhoon_enhanced)
        
        # Create options with category information
        options = []
        for _, storm in storms.iterrows():
            name = storm['NAME'] if pd.notna(storm['NAME']) and storm['NAME'] != '' else 'UNNAMED'
            sid = storm['SID']
            category = storm['category']
            max_wind = storm['USA_WIND'] if pd.notna(storm['USA_WIND']) else 0
            
            option = f"{name} ({sid}) - {category} ({max_wind:.0f}kt)"
            options.append(option)
        
        if not options:
            return gr.update(choices=["No storms found"], value=None)
        
        return gr.update(choices=sorted(options), value=options[0])
        
    except Exception as e:
        print(f"Error in update_typhoon_options_enhanced: {e}")
        return gr.update(choices=["Error loading storms"], value=None)

def generate_enhanced_track_video(year, typhoon_selection, standard):
    """Enhanced track video generation with TD support, Taiwan standard, and 2025 compatibility"""
    if not typhoon_selection or typhoon_selection == "No storms found":
        return None
    
    try:
        # Extract SID from selection
        sid = typhoon_selection.split('(')[1].split(')')[0]
        
        # Get storm data
        storm_df = typhoon_data[typhoon_data['SID'] == sid].copy()
        if storm_df.empty:
            print(f"No data found for storm {sid}")
            return None
        
        # Sort by time
        if 'ISO_TIME' in storm_df.columns:
            storm_df = storm_df.sort_values('ISO_TIME')
        
        # Extract data for animation
        lats = storm_df['LAT'].astype(float).values
        lons = storm_df['LON'].astype(float).values
        
        if 'USA_WIND' in storm_df.columns:
            winds = pd.to_numeric(storm_df['USA_WIND'], errors='coerce').fillna(0).values
        else:
            winds = np.full(len(lats), 30)  # Default TD strength
        
        # Enhanced metadata
        storm_name = storm_df['NAME'].iloc[0] if pd.notna(storm_df['NAME'].iloc[0]) else "UNNAMED"
        season = storm_df['SEASON'].iloc[0] if 'SEASON' in storm_df.columns else year
        
        print(f"Generating video for {storm_name} ({sid}) with {len(lats)} track points using {standard} standard")
        
        # Create figure with enhanced map
        fig, ax = plt.subplots(figsize=(16, 10), subplot_kw={'projection': ccrs.PlateCarree()})
        
        # Enhanced map features
        ax.stock_img()
        ax.add_feature(cfeature.COASTLINE, linewidth=0.8)
        ax.add_feature(cfeature.BORDERS, linewidth=0.5)
        ax.add_feature(cfeature.OCEAN, color='lightblue', alpha=0.5)
        ax.add_feature(cfeature.LAND, color='lightgray', alpha=0.5)
        
        # Set extent based on track
        padding = 5
        ax.set_extent([
            min(lons) - padding, max(lons) + padding,
            min(lats) - padding, max(lats) + padding
        ])
        
        # Add gridlines
        gl = ax.gridlines(draw_labels=True, alpha=0.3)
        gl.top_labels = gl.right_labels = False
        
        # Title with enhanced info and standard
        ax.set_title(f"{season} {storm_name} ({sid}) Track Animation - {standard.upper()} Standard", 
                    fontsize=18, fontweight='bold')
        
        # Animation elements
        line, = ax.plot([], [], 'b-', linewidth=3, alpha=0.7, label='Track')
        point, = ax.plot([], [], 'o', markersize=15)
        
        # Enhanced info display
        info_box = ax.text(0.02, 0.98, '', transform=ax.transAxes, 
                          fontsize=12, verticalalignment='top',
                          bbox=dict(boxstyle="round,pad=0.5", facecolor='white', alpha=0.9))
        
        # Color legend with both standards - ENHANCED
        legend_elements = []
        
        if standard == 'taiwan':
            categories = ['Tropical Depression', 'Mild Typhoon', 'Medium Typhoon', 'Strong Typhoon']
            for category in categories:
                color = get_taiwan_color(category)
                legend_elements.append(plt.Line2D([0], [0], marker='o', color='w',
                                                markerfacecolor=color, markersize=10, label=category))
        else:
            categories = ['Tropical Depression', 'Tropical Storm', 'C1 Typhoon', 'C2 Typhoon', 
                         'C3 Strong Typhoon', 'C4 Very Strong Typhoon', 'C5 Super Typhoon']
            for category in categories:
                color = get_matplotlib_color(category)
                legend_elements.append(plt.Line2D([0], [0], marker='o', color='w',
                                                markerfacecolor=color, markersize=10, label=category))
        
        ax.legend(handles=legend_elements, loc='upper right', fontsize=10)
        
        def animate(frame):
            try:
                if frame >= len(lats):
                    return line, point, info_box
                
                # Update track line
                line.set_data(lons[:frame+1], lats[:frame+1])
                
                # Update current position with appropriate categorization
                current_wind = winds[frame]
                
                if standard == 'taiwan':
                    category, color = categorize_typhoon_by_standard(current_wind, 'taiwan')
                else:
                    category, color = categorize_typhoon_by_standard(current_wind, 'atlantic')
                
                # Debug print for first few frames
                if frame < 3:
                    print(f"Frame {frame}: Wind={current_wind:.1f}kt, Category={category}, Color={color}, Standard={standard}")
                
                point.set_data([lons[frame]], [lats[frame]])
                point.set_color(color)
                point.set_markersize(10 + current_wind/8)  # Size based on intensity
                
                # Enhanced info display with standard information
                if 'ISO_TIME' in storm_df.columns and frame < len(storm_df):
                    current_time = storm_df.iloc[frame]['ISO_TIME']
                    time_str = current_time.strftime('%Y-%m-%d %H:%M UTC') if pd.notna(current_time) else 'Unknown'
                else:
                    time_str = f"Step {frame+1}"
                
                # Convert wind speed for Taiwan standard display
                if standard == 'taiwan':
                    wind_ms = current_wind * 0.514444  # Convert to m/s for display
                    wind_display = f"{current_wind:.0f} kt ({wind_ms:.1f} m/s)"
                else:
                    wind_display = f"{current_wind:.0f} kt"
                
                info_text = (
                    f"Storm: {storm_name}\n"
                    f"Time: {time_str}\n"
                    f"Position: {lats[frame]:.1f}°N, {lons[frame]:.1f}°E\n"
                    f"Max Wind: {wind_display}\n"
                    f"Category: {category}\n"
                    f"Standard: {standard.upper()}\n"
                    f"Frame: {frame+1}/{len(lats)}"
                )
                info_box.set_text(info_text)
                
                return line, point, info_box
                
            except Exception as e:
                print(f"Error in animate frame {frame}: {e}")
                return line, point, info_box
        
        # Create animation
        anim = animation.FuncAnimation(
            fig, animate, frames=len(lats),
            interval=400, blit=False, repeat=True  # Slightly slower for better viewing
        )
        
        # Save animation
        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4', 
                                              dir=tempfile.gettempdir())
        
        # Enhanced writer settings
        writer = animation.FFMpegWriter(
            fps=3, bitrate=2000, codec='libx264',  # Slower FPS for better visibility
            extra_args=['-pix_fmt', 'yuv420p']  # Better compatibility
        )
        
        print(f"Saving animation to {temp_file.name}")
        anim.save(temp_file.name, writer=writer, dpi=120)  # Higher DPI for better quality
        plt.close(fig)
        
        print(f"Video generated successfully: {temp_file.name}")
        return temp_file.name
        
    except Exception as e:
        print(f"Error generating video: {e}")
        import traceback
        traceback.print_exc()
        return None

# Simplified wrapper for backward compatibility - FIXED
def simplified_track_video(year, basin, typhoon, standard):
    """Simplified track video function with fixed color handling"""
    if not typhoon:
        return None
    return generate_enhanced_track_video(year, typhoon, standard)

# -----------------------------
# Load & Process Data
# -----------------------------

# Global variables initialization
oni_data = None
typhoon_data = None
merged_data = None

def initialize_data():
    """Initialize all data safely"""
    global oni_data, typhoon_data, merged_data
    try:
        logging.info("Starting data loading process...")
        update_oni_data()
        oni_data, typhoon_data = load_data_fixed(ONI_DATA_PATH, TYPHOON_DATA_PATH)
        
        if oni_data is not None and typhoon_data is not None:
            oni_long = process_oni_data(oni_data)
            typhoon_max = process_typhoon_data(typhoon_data)
            merged_data = merge_data(oni_long, typhoon_max)
            logging.info("Data loading complete.")
        else:
            logging.error("Failed to load required data")
            # Create minimal fallback data
            oni_data = pd.DataFrame({'Year': [2000], 'Jan': [0], 'Feb': [0], 'Mar': [0], 'Apr': [0], 
                                   'May': [0], 'Jun': [0], 'Jul': [0], 'Aug': [0], 'Sep': [0], 
                                   'Oct': [0], 'Nov': [0], 'Dec': [0]})
            typhoon_data = create_fallback_typhoon_data()
            oni_long = process_oni_data(oni_data)
            typhoon_max = process_typhoon_data(typhoon_data)
            merged_data = merge_data(oni_long, typhoon_max)
    except Exception as e:
        logging.error(f"Error during data initialization: {e}")
        # Create minimal fallback data
        oni_data = pd.DataFrame({'Year': [2000], 'Jan': [0], 'Feb': [0], 'Mar': [0], 'Apr': [0], 
                               'May': [0], 'Jun': [0], 'Jul': [0], 'Aug': [0], 'Sep': [0], 
                               'Oct': [0], 'Nov': [0], 'Dec': [0]})
        typhoon_data = create_fallback_typhoon_data()
        oni_long = process_oni_data(oni_data)
        typhoon_max = process_typhoon_data(typhoon_data)
        merged_data = merge_data(oni_long, typhoon_max)

# Initialize data
initialize_data()

# -----------------------------
# ENHANCED: Gradio Interface with Fixed Route Visualization and Enhanced Features
# -----------------------------

def create_interface():
    """Create the enhanced Gradio interface with robust error handling"""
    try:
        # Ensure data is available
        if oni_data is None or typhoon_data is None or merged_data is None:
            logging.warning("Data not properly loaded, creating minimal interface")
            return create_minimal_fallback_interface()
            
        # Get safe data statistics
        try:
            total_storms = len(typhoon_data['SID'].unique()) if 'SID' in typhoon_data.columns else 0
            total_records = len(typhoon_data)
            available_years = get_available_years(typhoon_data)
            year_range_display = f"{available_years[0]} - {available_years[-1]}" if available_years else "Unknown"
        except Exception as e:
            logging.error(f"Error getting data statistics: {e}")
            total_storms = 0
            total_records = 0
            year_range_display = "Unknown"
            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("**Advanced ML clustering, route predictions, and comprehensive tropical cyclone analysis including Tropical Depressions**")
            
            with gr.Tab("🏠 Overview"):
                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 separate visualizations
                - **Predictive Routing**: Advanced storm track and intensity forecasting with uncertainty quantification
                - **Complete TD Support**: Now includes Tropical Depressions (< 34 kt)
                - **Taiwan Standard**: Full support for Taiwan meteorological classification system
                - **2025 Data Ready**: Real-time compatibility with current year data
                - **Enhanced Animations**: High-quality storm track visualizations with both standards
                
                ### 📊 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**: Optimized for Hugging Face Spaces
                
                ### 📈 Research Applications:
                - Climate change impact studies
                - Seasonal forecasting research
                - Storm pattern classification
                - ENSO-typhoon relationship analysis
                - Intensity prediction model development
                """
                gr.Markdown(overview_text)

            with gr.Tab("🔬 Advanced ML Clustering"):
                gr.Markdown("## 🎯 Storm Pattern Analysis with Separate Visualizations")
                gr.Markdown("**Four separate plots: Clustering, Routes, Pressure Evolution, and Wind Evolution**")
                
                with gr.Row():
                    with gr.Column(scale=2):
                        reduction_method = gr.Dropdown(
                            choices=['UMAP', 't-SNE', 'PCA'], 
                            value='UMAP' if UMAP_AVAILABLE else 't-SNE',
                            label="🔍 Dimensionality Reduction Method",
                            info="UMAP provides better global structure preservation"
                        )
                    with gr.Column(scale=1):
                        analyze_clusters_btn = gr.Button("🚀 Generate All Cluster Analyses", variant="primary", size="lg")
                
                with gr.Row():
                    with gr.Column():
                        cluster_plot = gr.Plot(label="📊 Storm Clustering Analysis")
                    with gr.Column():
                        routes_plot = gr.Plot(label="🗺️ Clustered Storm Routes")
                
                with gr.Row():
                    with gr.Column():
                        pressure_plot = gr.Plot(label="🌡️ Pressure Evolution by Cluster")
                    with gr.Column():
                        wind_plot = gr.Plot(label="💨 Wind Speed Evolution by Cluster")
                
                with gr.Row():
                    cluster_stats = gr.Textbox(label="📈 Detailed Cluster Statistics", lines=15, max_lines=20)
                
                def run_separate_clustering_analysis(method):
                    try:
                        # Extract features for clustering
                        storm_features = extract_storm_features(typhoon_data)
                        if storm_features is None:
                            return None, None, None, None, "Error: Could not extract storm features"
                        
                        fig_cluster, fig_routes, fig_pressure, fig_wind, stats = create_separate_clustering_plots(
                            storm_features, typhoon_data, method.lower()
                        )
                        return fig_cluster, fig_routes, fig_pressure, fig_wind, stats
                    except Exception as e:
                        import traceback
                        error_details = traceback.format_exc()
                        error_msg = f"Error: {str(e)}\n\nDetails:\n{error_details}"
                        return None, None, None, None, error_msg
                
                analyze_clusters_btn.click(
                    fn=run_separate_clustering_analysis,
                    inputs=[reduction_method],
                    outputs=[cluster_plot, routes_plot, pressure_plot, wind_plot, cluster_stats]
                )
                
                cluster_info_text = """
                ### 📊 Enhanced Clustering Features:
                - **Separate Visualizations**: Four distinct plots for comprehensive analysis
                - **Multi-dimensional Analysis**: Uses 15+ storm characteristics including intensity, track shape, genesis location
                - **Route Visualization**: Geographic storm tracks colored by cluster membership
                - **Temporal Analysis**: Pressure and wind evolution patterns by cluster
                - **DBSCAN Clustering**: Automatic pattern discovery without predefined cluster count
                - **Interactive**: Hover over points to see storm details, zoom and pan all plots
                
                ### 🎯 How to Interpret:
                - **Clustering Plot**: Each dot is a storm positioned by similarity (close = similar characteristics)
                - **Routes Plot**: Actual geographic storm tracks, colored by which cluster they belong to
                - **Pressure Plot**: Shows how pressure changes over time for storms in each cluster
                - **Wind Plot**: Shows wind speed evolution patterns for each cluster
                - **Cluster Colors**: Each cluster gets a unique color across all four visualizations
                """
                gr.Markdown(cluster_info_text)

            with gr.Tab("🌊 Realistic Storm Genesis & Prediction"):
                gr.Markdown("## 🌊 Realistic Typhoon Development from Genesis")
                
                if CNN_AVAILABLE:
                    gr.Markdown("🧠 **Deep Learning models available** - TensorFlow loaded successfully")
                    method_description = "Hybrid CNN-Physics genesis modeling with realistic development cycles"
                else:
                    gr.Markdown("🔬 **Physics-based models available** - Using climatological relationships")
                    method_description = "Advanced physics-based genesis modeling with environmental coupling"
                
                gr.Markdown(f"**Current Method**: {method_description}")
                gr.Markdown("**🌊 Realistic Genesis**: Select from climatologically accurate development regions")
                gr.Markdown("**📈 TD Starting Point**: Storms begin at realistic Tropical Depression intensities (25-35 kt)")
                gr.Markdown("**🎬 Animation Support**: Watch storm development unfold over time")
                
                with gr.Row():
                    with gr.Column(scale=2):
                        gr.Markdown("### 🌊 Genesis Configuration")
                        genesis_options = list(get_realistic_genesis_locations().keys())
                        genesis_region = gr.Dropdown(
                            choices=genesis_options,
                            value="Western Pacific Main Development Region",
                            label="Typhoon Genesis Region",
                            info="Select realistic development region based on climatology"
                        )
                        
                        # Display selected region info
                        def update_genesis_info(region):
                            locations = get_realistic_genesis_locations()
                            if region in locations:
                                info = locations[region]
                                return f"📍 Location: {info['lat']:.1f}°N, {info['lon']:.1f}°E\n📝 {info['description']}"
                            return "Select a genesis region"
                        
                        genesis_info_display = gr.Textbox(
                            label="Selected Region Info",
                            lines=2,
                            interactive=False,
                            value=update_genesis_info("Western Pacific Main Development Region")
                        )
                        
                        genesis_region.change(
                            fn=update_genesis_info,
                            inputs=[genesis_region],
                            outputs=[genesis_info_display]
                        )
                        
                        with gr.Row():
                            pred_month = gr.Slider(1, 12, label="Month", value=9, info="Peak season: Jul-Oct")
                            pred_oni = gr.Number(label="ONI Value", value=0.0, info="ENSO index (-3 to 3)")
                        with gr.Row():
                            forecast_hours = gr.Slider(
                                6, 240, 
                                label="Forecast Length (hours)", 
                                value=72, 
                                step=6,
                                info="Extended forecasting up to 10 days"
                            )
                            advanced_physics = gr.Checkbox(
                                label="Advanced Physics", 
                                value=True,
                                info="Enhanced environmental modeling"
                            )
                        with gr.Row():
                            show_uncertainty = gr.Checkbox(label="Show Uncertainty Cone", value=True)
                            enable_animation = gr.Checkbox(
                                label="Enable Animation", 
                                value=True,
                                info="Animated storm development vs static view"
                            )
                
                    with gr.Column(scale=1):
                        gr.Markdown("### ⚙️ Prediction Controls")
                        predict_btn = gr.Button("🌊 Generate Realistic Storm Forecast", variant="primary", size="lg")
                        
                        gr.Markdown("### 📊 Genesis Conditions")
                        current_intensity = gr.Number(label="Genesis Intensity (kt)", interactive=False)
                        current_category = gr.Textbox(label="Initial Category", interactive=False)
                        model_confidence = gr.Textbox(label="Model Info", interactive=False)
                
                with gr.Row():
                    route_plot = gr.Plot(label="🗺️ Advanced Route & Intensity Forecast")
                
                with gr.Row():
                    forecast_details = gr.Textbox(label="📋 Detailed Forecast Summary", lines=20, max_lines=25)
                
                def run_realistic_prediction(region, month, oni, hours, advanced_phys, uncertainty, animation):
                    try:
                        # Run realistic prediction with genesis region
                        results = predict_storm_route_and_intensity_realistic(
                            region, month, oni, 
                            forecast_hours=hours, 
                            use_advanced_physics=advanced_phys
                        )
                        
                        # Extract genesis conditions
                        current = results['current_prediction']
                        intensity = current['intensity_kt']
                        category = current['category']
                        genesis_info = results.get('genesis_info', {})
                        
                        # Create enhanced visualization
                        fig, forecast_text = create_animated_route_visualization(
                            results, uncertainty, animation
                        )
                        
                        model_info = f"{results['model_info']}\nGenesis: {genesis_info.get('description', 'Unknown')}"
                        
                        return (
                            intensity,
                            category,
                            model_info,
                            fig,
                            forecast_text
                        )
                    except Exception as e:
                        error_msg = f"Realistic prediction failed: {str(e)}"
                        logging.error(error_msg)
                        import traceback
                        traceback.print_exc()
                        return (
                            30, "Tropical Depression", f"Prediction failed: {str(e)}", 
                            None, f"Error generating realistic forecast: {str(e)}"
                        )
                
                predict_btn.click(
                    fn=run_realistic_prediction,
                    inputs=[genesis_region, pred_month, pred_oni, forecast_hours, advanced_physics, show_uncertainty, enable_animation],
                    outputs=[current_intensity, current_category, model_confidence, route_plot, forecast_details]
                )
                
                prediction_info_text = """
                ### 🌊 Realistic Storm Genesis Features:
                - **Climatological Genesis Regions**: 10 realistic development zones based on historical data
                - **Tropical Depression Starting Point**: Storms begin at realistic 25-35 kt intensities
                - **Animated Development**: Watch storm evolution from genesis through maturity to decay
                - **Extended Forecasting**: Up to 240 hours (10 days) with realistic development cycles
                - **Environmental Coupling**: Advanced physics with ENSO, SST, shear, β-drift, ridge patterns
                - **Realistic Intensity Evolution**: Natural intensification/weakening cycles, not just decay
                
                ### 📊 Enhanced Development Stages:
                - **Genesis (0-24h)**: Initial cyclogenesis at Tropical Depression level
                - **Development (24-72h)**: Natural intensification in favorable environment
                - **Mature (72-120h)**: Peak intensity phase with environmental modulation
                - **Decay (120h+)**: Realistic weakening due to latitude/land/cooler waters
                
                ### 🌍 Genesis Region Selection:
                - **Western Pacific MDR**: Peak activity zone near Guam (12.5°N, 145°E)
                - **South China Sea**: Secondary development region (15°N, 115°E)
                - **Philippine Sea**: Recurving storm region (18°N, 135°E)
                - **Marshall Islands**: Eastern development zone (8°N, 165°E)
                - **Monsoon Trough**: Monsoon-driven genesis (10°N, 130°E)
                - **Other Basins**: Bay of Bengal, Eastern Pacific, Atlantic options
                
                ### 🔬 Enhanced Physics Model:
                - **Beta Drift**: Coriolis-induced storm motion (latitude-dependent)
                - **Ridge Interaction**: Seasonal subtropical ridge position effects
                - **ENSO Modulation**: Non-linear intensity and steering effects
                - **SST Coupling**: Ocean temperature impact on intensification
                - **Shear Analysis**: Environmental wind shear impact assessment
                - **Recurvature Physics**: Advanced extratropical transition modeling
                
                ### 🎬 Animation Features:
                - **Real-time Development**: Watch TD evolve to typhoon intensity
                - **Interactive Controls**: Play/pause buttons and time slider
                - **Stage Tracking**: Visual indicators for development phases
                - **Confidence Evolution**: See how uncertainty grows with time
                - **Genesis Highlighting**: Special markers for initial development location
                """
                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)
                    end_year = gr.Number(label="End Year", value=2025)
                    end_month = gr.Dropdown(label="End Month", choices=list(range(1, 13)), value=6)
                    enso_phase = gr.Dropdown(label="ENSO Phase", choices=['all', 'El Nino', 'La Nina', 'Neutral'], value='all')
                    typhoon_search = gr.Textbox(label="Typhoon Search")
                analyze_btn = gr.Button("Generate Tracks")
                tracks_plot = gr.Plot()
                typhoon_count = gr.Textbox(label="Number of Typhoons Displayed")
                analyze_btn.click(
                    fn=get_full_tracks,
                    inputs=[start_year, start_month, end_year, end_month, enso_phase, typhoon_search],
                    outputs=[tracks_plot, typhoon_count]
                )
            
            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)
                    wind_end_year = gr.Number(label="End Year", value=2024)
                    wind_end_month = gr.Dropdown(label="End Month", choices=list(range(1, 13)), value=6)
                    wind_enso_phase = gr.Dropdown(label="ENSO Phase", choices=['all', 'El Nino', 'La Nina', 'Neutral'], value='all')
                    wind_typhoon_search = gr.Textbox(label="Typhoon Search")
                wind_analyze_btn = gr.Button("Generate Wind Analysis")
                wind_scatter = gr.Plot()
                wind_regression_results = gr.Textbox(label="Wind Regression Results")
                wind_analyze_btn.click(
                    fn=get_wind_analysis,
                    inputs=[wind_start_year, wind_start_month, wind_end_year, wind_end_month, wind_enso_phase, wind_typhoon_search],
                    outputs=[wind_scatter, wind_regression_results]
                )
            
            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)
                    pressure_end_year = gr.Number(label="End Year", value=2024)
                    pressure_end_month = gr.Dropdown(label="End Month", choices=list(range(1, 13)), value=6)
                    pressure_enso_phase = gr.Dropdown(label="ENSO Phase", choices=['all', 'El Nino', 'La Nina', 'Neutral'], value='all')
                    pressure_typhoon_search = gr.Textbox(label="Typhoon Search")
                pressure_analyze_btn = gr.Button("Generate Pressure Analysis")
                pressure_scatter = gr.Plot()
                pressure_regression_results = gr.Textbox(label="Pressure Regression Results")
                pressure_analyze_btn.click(
                    fn=get_pressure_analysis,
                    inputs=[pressure_start_year, pressure_start_month, pressure_end_year, pressure_end_month, pressure_enso_phase, pressure_typhoon_search],
                    outputs=[pressure_scatter, pressure_regression_results]
                )
            
            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)
                    lon_end_year = gr.Number(label="End Year", value=2020)
                    lon_end_month = gr.Dropdown(label="End Month", choices=list(range(1, 13)), value=6)
                    lon_enso_phase = gr.Dropdown(label="ENSO Phase", choices=['all', 'El Nino', 'La Nina', 'Neutral'], value='all')
                    lon_typhoon_search = gr.Textbox(label="Typhoon Search (Optional)")
                lon_analyze_btn = gr.Button("Generate Longitude Analysis")
                regression_plot = gr.Plot()
                slopes_text = gr.Textbox(label="Regression Slopes")
                lon_regression_results = gr.Textbox(label="Longitude Regression Results")
                lon_analyze_btn.click(
                    fn=get_longitude_analysis,
                    inputs=[lon_start_year, lon_start_month, lon_end_year, lon_end_month, lon_enso_phase, lon_typhoon_search],
                    outputs=[regression_plot, slopes_text, lon_regression_results]
                )
            
            with gr.Tab("🎬 Enhanced Track Animation"):
                gr.Markdown("## 🎥 High-Quality Storm Track Visualization (Atlantic & Taiwan Standards)")
                
                with gr.Row():
                    year_dropdown = gr.Dropdown(
                        label="Year",
                        choices=available_years,
                        value=available_years[-1] if available_years else "2024"
                    )
                    basin_dropdown = gr.Dropdown(
                        label="Basin",
                        choices=["All Basins", "WP - Western Pacific", "EP - Eastern Pacific", "NA - North Atlantic"],
                        value="All Basins"
                    )
                
                with gr.Row():
                    typhoon_dropdown = gr.Dropdown(label="Storm Selection (All Categories Including TD)")
                    standard_dropdown = gr.Dropdown(
                        label="🎌 Classification Standard",
                        choices=['atlantic', 'taiwan'], 
                        value='atlantic',
                        info="Atlantic: International standard | Taiwan: Local meteorological standard"
                    )
                
                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
                for input_comp in [year_dropdown, basin_dropdown]:
                    input_comp.change(
                        fn=update_typhoon_options_enhanced,
                        inputs=[year_dropdown, basin_dropdown],
                        outputs=[typhoon_dropdown]
                    )
                
                # Generate video
                generate_video_btn.click(
                    fn=generate_enhanced_track_video,
                    inputs=[year_dropdown, typhoon_dropdown, standard_dropdown],
                    outputs=[video_output]
                )
                
                animation_info_text = """
                ### 🎬 Enhanced Animation Features:
                - **Dual Standards**: Full support for both Atlantic and Taiwan classification systems
                - **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
                
                ### 🎌 Taiwan Standard Features:
                - **m/s Display**: Shows both knots and meters per second
                - **Local Categories**: TD → Mild → Medium → Strong Typhoon
                - **Color Coding**: Gray → Yellow → Orange → Red
                - **CWB Compatible**: Matches Central Weather Bureau classifications
                """
                gr.Markdown(animation_info_text)

            with gr.Tab("📊 Data Statistics & Insights"):
                gr.Markdown("## 📈 Comprehensive Dataset Analysis")
                
                # Create enhanced data summary
                try:
                    if len(typhoon_data) > 0:
                        # Storm category distribution
                        storm_cats = typhoon_data.groupby('SID')['USA_WIND'].max().apply(categorize_typhoon_enhanced)
                        cat_counts = storm_cats.value_counts()
                        
                        # Create distribution chart with enhanced colors
                        fig_dist = px.bar(
                            x=cat_counts.index,
                            y=cat_counts.values,
                            title="Storm Intensity Distribution (Including Tropical Depressions)",
                            labels={'x': 'Category', 'y': 'Number of Storms'},
                            color=cat_counts.index,
                            color_discrete_map=enhanced_color_map
                        )
                        
                        # Seasonal distribution
                        if 'ISO_TIME' in typhoon_data.columns:
                            seasonal_data = typhoon_data.copy()
                            seasonal_data['Month'] = seasonal_data['ISO_TIME'].dt.month
                            monthly_counts = seasonal_data.groupby(['Month', 'SID']).size().groupby('Month').size()
                            
                            fig_seasonal = px.bar(
                                x=monthly_counts.index,
                                y=monthly_counts.values,
                                title="Seasonal Storm Distribution",
                                labels={'x': 'Month', 'y': 'Number of Storms'},
                                color=monthly_counts.values,
                                color_continuous_scale='Viridis'
                            )
                        else:
                            fig_seasonal = None
                        
                        # Basin distribution
                        if 'SID' in typhoon_data.columns:
                            basin_data = typhoon_data['SID'].str[:2].value_counts()
                            fig_basin = px.pie(
                                values=basin_data.values,
                                names=basin_data.index,
                                title="Distribution by Basin"
                            )
                        else:
                            fig_basin = None
                        
                        with gr.Row():
                            gr.Plot(value=fig_dist)
                        
                        if fig_seasonal:
                            with gr.Row():
                                gr.Plot(value=fig_seasonal)
                        
                        if fig_basin:
                            with gr.Row():
                                gr.Plot(value=fig_basin)
                                
                except Exception as e:
                    gr.Markdown(f"Visualization error: {str(e)}")
                
                # Enhanced statistics - FIXED formatting
                total_storms = len(typhoon_data['SID'].unique()) if 'SID' in typhoon_data.columns else 0
                total_records = len(typhoon_data)
                
                if 'SEASON' in typhoon_data.columns:
                    try:
                        min_year = int(typhoon_data['SEASON'].min())
                        max_year = int(typhoon_data['SEASON'].max())
                        year_range = f"{min_year}-{max_year}"
                        years_covered = typhoon_data['SEASON'].nunique()
                    except (ValueError, TypeError):
                        year_range = "Unknown"
                        years_covered = 0
                else:
                    year_range = "Unknown"
                    years_covered = 0
                
                if 'SID' in typhoon_data.columns:
                    try:
                        basins_available = ', '.join(sorted(typhoon_data['SID'].str[:2].unique()))
                        avg_storms_per_year = total_storms / max(years_covered, 1)
                    except Exception:
                        basins_available = "Unknown"
                        avg_storms_per_year = 0
                else:
                    basins_available = "Unknown"
                    avg_storms_per_year = 0
                
                # TD specific statistics
                try:
                    if 'USA_WIND' in typhoon_data.columns:
                        td_storms = len(typhoon_data[typhoon_data['USA_WIND'] < 34]['SID'].unique())
                        ts_storms = len(typhoon_data[(typhoon_data['USA_WIND'] >= 34) & (typhoon_data['USA_WIND'] < 64)]['SID'].unique())
                        typhoon_storms = len(typhoon_data[typhoon_data['USA_WIND'] >= 64]['SID'].unique())
                        td_percentage = (td_storms / max(total_storms, 1)) * 100
                    else:
                        td_storms = ts_storms = typhoon_storms = 0
                        td_percentage = 0
                except Exception as e:
                    print(f"Error calculating TD statistics: {e}")
                    td_storms = ts_storms = typhoon_storms = 0
                    td_percentage = 0
                
                # 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}
                
                ### 🌪️ Storm Category Breakdown:
                - **Tropical Depressions**: {td_storms:,} storms ({td_percentage:.1f}%)
                - **Tropical Storms**: {ts_storms:,} storms
                - **Typhoons (C1-C5)**: {typhoon_storms:,} storms
                
                ### 🚀 Platform Capabilities:
                - **Complete TD Analysis** - First platform to include comprehensive TD tracking
                - **Dual Classification Systems** - Both Atlantic and Taiwan standards supported
                - **Advanced ML Clustering** - DBSCAN pattern recognition with separate visualizations
                - **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
                - Seasonal forecasting research
                - Storm pattern classification
                - ENSO-typhoon relationship analysis
                - Intensity prediction model development
                - Cross-regional classification comparisons
                """
                gr.Markdown(stats_text)

        return demo
    except Exception as e:
        logging.error(f"Error creating Gradio interface: {e}")
        import traceback
        traceback.print_exc()
        # Create a minimal fallback interface
        return create_minimal_fallback_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("**Notice**: Loading with minimal interface due to data issues.")
        
        with gr.Tab("Status"):
            gr.Markdown("""
            ## Platform Status
            
            The application is running but encountered issues loading the full interface.
            This could be due to:
            - Data loading problems
            - Missing dependencies
            - Configuration issues
            
            ### Available Features:
            - Basic interface is functional
            - Error logs are being generated
            - System is ready for debugging
            
            ### Next Steps:
            1. Check the console logs for detailed error information
            2. Verify all required data files are accessible
            3. Ensure all dependencies are properly installed
            4. Try restarting the application
            """)
        
        with gr.Tab("Debug"):
            gr.Markdown("## Debug Information")
            
            def get_debug_info():
                debug_text = f"""
                Python Environment:
                - Working Directory: {os.getcwd()}
                - Data Path: {DATA_PATH}
                - UMAP Available: {UMAP_AVAILABLE}
                - CNN Available: {CNN_AVAILABLE}
                
                Data Status:
                - ONI Data: {'Loaded' if oni_data is not None else 'Failed'}
                - Typhoon Data: {'Loaded' if typhoon_data is not None else 'Failed'}
                - Merged Data: {'Loaded' if merged_data is not None else 'Failed'}
                
                File Checks:
                - ONI Path Exists: {os.path.exists(ONI_DATA_PATH)}
                - Typhoon Path Exists: {os.path.exists(TYPHOON_DATA_PATH)}
                """
                return debug_text
            
            debug_btn = gr.Button("Get Debug Info")
            debug_output = gr.Textbox(label="Debug Information", lines=15)
            debug_btn.click(fn=get_debug_info, outputs=debug_output)
    
    return demo

# Create and launch the interface
demo = create_interface()

if __name__ == "__main__":
    demo.launch(share=True)  # Enable sharing with public link