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, griddata
from scipy.ndimage import gaussian_filter
import statsmodels.api as sm
import requests
import tempfile
import shutil
import xarray as xr
import urllib.request
from urllib.error import URLError
import ssl

# 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

# Suppress SSL warnings for oceanic data downloads
ssl._create_default_https_context = ssl._create_unverified_context

# -----------------------------
# 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 - FIXED TAIWAN CLASSIFICATION
# -----------------------------
# 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
}

# FIXED: Taiwan color mapping with correct CMA 2006 standards
taiwan_color_map_fixed = {
    'Tropical Depression': '#808080',     # Gray
    'Tropical Storm': '#0000FF',          # Blue
    'Severe Tropical Storm': '#00FFFF',   # Cyan
    'Typhoon': '#FFFF00',                 # Yellow
    'Severe Typhoon': '#FFA500',          # Orange
    'Super 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_fixed(category):
    """Get corrected Taiwan standard color"""
    return taiwan_color_map_fixed.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'}
}

# FIXED: Taiwan standard with correct CMA 2006 thresholds
taiwan_standard_fixed = {
    'Super Typhoon': {'wind_speed_ms': 51.0, 'wind_speed_kt': 99.2, 'color': 'Red', 'hex': '#FF0000'},
    'Severe Typhoon': {'wind_speed_ms': 41.5, 'wind_speed_kt': 80.7, 'color': 'Orange', 'hex': '#FFA500'},
    'Typhoon': {'wind_speed_ms': 32.7, 'wind_speed_kt': 63.6, 'color': 'Yellow', 'hex': '#FFFF00'},
    'Severe Tropical Storm': {'wind_speed_ms': 24.5, 'wind_speed_kt': 47.6, 'color': 'Cyan', 'hex': '#00FFFF'},
    'Tropical Storm': {'wind_speed_ms': 17.2, 'wind_speed_kt': 33.4, 'color': 'Blue', 'hex': '#0000FF'},
    'Tropical Depression': {'wind_speed_ms': 0, 'wind_speed_kt': 0, 'color': 'Gray', 'hex': '#808080'}
}

# -----------------------------
# ENHANCED: Oceanic Data Integration
# -----------------------------

class OceanicDataManager:
    """Manages real-time oceanic data for enhanced typhoon prediction"""
    
    def __init__(self):
        self.sst_base_url = "https://www.ncei.noaa.gov/erddap/griddap/NOAA_OISST_V2.nc"
        self.slp_base_url = "https://psl.noaa.gov/thredds/dodsC/Datasets/ncep.reanalysis.dailyavgs/surface/slp.nc"
        self.cache_dir = os.path.join(DATA_PATH, 'oceanic_cache')
        self.create_cache_directory()
        
    def create_cache_directory(self):
        """Create cache directory for oceanic data"""
        try:
            os.makedirs(self.cache_dir, exist_ok=True)
        except Exception as e:
            logging.warning(f"Could not create cache directory: {e}")
            self.cache_dir = tempfile.mkdtemp()
    
    def get_sst_data(self, lat_min, lat_max, lon_min, lon_max, date_start, date_end=None):
        """
        Fetch Sea Surface Temperature data from NOAA OISST v2
        
        Parameters:
        lat_min, lat_max: Latitude bounds
        lon_min, lon_max: Longitude bounds  
        date_start: Start date (datetime or string)
        date_end: End date (datetime or string, optional)
        """
        try:
            if date_end is None:
                date_end = date_start
                
            # Convert dates to strings if needed
            if isinstance(date_start, datetime):
                date_start_str = date_start.strftime('%Y-%m-%d')
            else:
                date_start_str = str(date_start)
                
            if isinstance(date_end, datetime):
                date_end_str = date_end.strftime('%Y-%m-%d')
            else:
                date_end_str = str(date_end)
            
            # Construct ERDDAP URL with parameters
            url_params = (
                f"?sst[({date_start_str}):1:({date_end_str})]"
                f"[({lat_min}):1:({lat_max})]"
                f"[({lon_min}):1:({lon_max})]"
            )
            full_url = self.sst_base_url + url_params
            
            logging.info(f"Fetching SST data from: {full_url}")
            
            # Use xarray to open the remote dataset
            with warnings.catch_warnings():
                warnings.simplefilter("ignore")
                ds = xr.open_dataset(full_url)
                
                # Extract SST data
                sst_data = ds['sst'].values
                lats = ds['latitude'].values
                lons = ds['longitude'].values
                times = ds['time'].values
                
                ds.close()
                
                return {
                    'sst': sst_data,
                    'latitude': lats,
                    'longitude': lons,
                    'time': times,
                    'success': True
                }
                
        except Exception as e:
            logging.error(f"Error fetching SST data: {e}")
            return self._get_fallback_sst_data(lat_min, lat_max, lon_min, lon_max)
    
    def get_slp_data(self, lat_min, lat_max, lon_min, lon_max, date_start, date_end=None):
        """
        Fetch Sea Level Pressure data from NCEP/NCAR Reanalysis
        
        Parameters similar to get_sst_data
        """
        try:
            if date_end is None:
                date_end = date_start
                
            # Convert dates for OPeNDAP access
            if isinstance(date_start, datetime):
                # NCEP uses different time indexing, may need adjustment
                date_start_str = date_start.strftime('%Y-%m-%d')
            else:
                date_start_str = str(date_start)
                
            if isinstance(date_end, datetime):
                date_end_str = date_end.strftime('%Y-%m-%d')
            else:
                date_end_str = str(date_end)
            
            logging.info(f"Fetching SLP data for {date_start_str} to {date_end_str}")
            
            # Use xarray to open OPeNDAP dataset
            with warnings.catch_warnings():
                warnings.simplefilter("ignore")
                
                # Open the full dataset (this might be large, so we'll subset)
                ds = xr.open_dataset(self.slp_base_url)
                
                # Subset by time and location
                # Note: Coordinate names might vary, adjust as needed
                lat_coord = 'lat' if 'lat' in ds.dims else 'latitude'
                lon_coord = 'lon' if 'lon' in ds.dims else 'longitude'
                
                # Subset the data
                subset = ds.sel(
                    time=slice(date_start_str, date_end_str),
                    **{lat_coord: slice(lat_min, lat_max),
                       lon_coord: slice(lon_min, lon_max)}
                )
                
                # Extract SLP data
                slp_data = subset['slp'].values
                lats = subset[lat_coord].values
                lons = subset[lon_coord].values
                times = subset['time'].values
                
                ds.close()
                
                return {
                    'slp': slp_data,
                    'latitude': lats,
                    'longitude': lons,
                    'time': times,
                    'success': True
                }
                
        except Exception as e:
            logging.error(f"Error fetching SLP data: {e}")
            return self._get_fallback_slp_data(lat_min, lat_max, lon_min, lon_max)
    
    def _get_fallback_sst_data(self, lat_min, lat_max, lon_min, lon_max):
        """Generate realistic fallback SST data based on climatology"""
        # Create a reasonable grid
        lats = np.linspace(lat_min, lat_max, 20)
        lons = np.linspace(lon_min, lon_max, 20)
        
        # Generate climatological SST values for Western Pacific
        sst_values = np.zeros((1, len(lats), len(lons)))
        
        for i, lat in enumerate(lats):
            for j, lon in enumerate(lons):
                # Climatological SST estimation for Western Pacific
                if lat < 10:  # Tropical
                    base_sst = 29.0
                elif lat < 20:  # Subtropical
                    base_sst = 28.0 - (lat - 10) * 0.3
                elif lat < 30:  # Temperate
                    base_sst = 25.0 - (lat - 20) * 0.5
                else:  # Cool waters
                    base_sst = 20.0 - (lat - 30) * 0.3
                
                # Add some realistic variation
                sst_values[0, i, j] = base_sst + np.random.normal(0, 0.5)
        
        return {
            'sst': sst_values,
            'latitude': lats,
            'longitude': lons,
            'time': [datetime.now()],
            'success': False,
            'note': 'Using climatological fallback data'
        }
    
    def _get_fallback_slp_data(self, lat_min, lat_max, lon_min, lon_max):
        """Generate realistic fallback SLP data"""
        lats = np.linspace(lat_min, lat_max, 20)
        lons = np.linspace(lon_min, lon_max, 20)
        
        slp_values = np.zeros((1, len(lats), len(lons)))
        
        for i, lat in enumerate(lats):
            for j, lon in enumerate(lons):
                # Climatological SLP estimation
                if lat < 30:  # Subtropical high influence
                    base_slp = 1013 + 3 * np.cos(np.radians(lat * 6))
                else:  # Mid-latitude
                    base_slp = 1010 - (lat - 30) * 0.2
                
                slp_values[0, i, j] = base_slp + np.random.normal(0, 2)
        
        return {
            'slp': slp_values,
            'latitude': lats,
            'longitude': lons,
            'time': [datetime.now()],
            'success': False,
            'note': 'Using climatological fallback data'
        }
    
    def interpolate_data_to_point(self, data_dict, target_lat, target_lon, variable='sst'):
        """Interpolate gridded data to a specific point"""
        try:
            data = data_dict[variable]
            lats = data_dict['latitude']
            lons = data_dict['longitude']
            
            # Take most recent time if multiple times available
            if len(data.shape) == 3:  # time, lat, lon
                data_2d = data[-1, :, :]
            else:  # lat, lon
                data_2d = data
            
            # Create coordinate grids
            lon_grid, lat_grid = np.meshgrid(lons, lats)
            
            # Flatten for interpolation
            points = np.column_stack((lat_grid.flatten(), lon_grid.flatten()))
            values = data_2d.flatten()
            
            # Remove NaN values
            valid_mask = ~np.isnan(values)
            points = points[valid_mask]
            values = values[valid_mask]
            
            if len(values) == 0:
                return np.nan
            
            # Interpolate to target point
            interpolated_value = griddata(
                points, values, (target_lat, target_lon), 
                method='linear', fill_value=np.nan
            )
            
            # If linear interpolation fails, try nearest neighbor
            if np.isnan(interpolated_value):
                interpolated_value = griddata(
                    points, values, (target_lat, target_lon), 
                    method='nearest'
                )
            
            return interpolated_value
            
        except Exception as e:
            logging.error(f"Error interpolating {variable} data: {e}")
            return np.nan

# Global oceanic data manager
oceanic_manager = None

# -----------------------------
# 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 - FIXED TAIWAN CLASSIFICATION
# -----------------------------

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_fixed(wind_speed):
    """
    FIXED Taiwan categorization system based on CMA 2006 standards
    Reference: CMA Tropical Cyclone Data Center official classification
    """
    if pd.isna(wind_speed):
        return 'Tropical Depression'
    
    # Convert from knots to m/s if input appears to be in knots
    if wind_speed > 50:  # Likely in knots, convert to m/s
        wind_speed_ms = wind_speed * 0.514444
    else:
        wind_speed_ms = wind_speed
    
    # CMA 2006 Classification Standards (used by Taiwan CWA)
    if wind_speed_ms >= 51.0:
        return 'Super Typhoon'        # ≥51.0 m/s (≥99.2 kt)
    elif wind_speed_ms >= 41.5:
        return 'Severe Typhoon'       # 41.5–50.9 m/s (80.7–99.1 kt)
    elif wind_speed_ms >= 32.7:
        return 'Typhoon'              # 32.7–41.4 m/s (63.6–80.6 kt)
    elif wind_speed_ms >= 24.5:
        return 'Severe Tropical Storm' # 24.5–32.6 m/s (47.6–63.5 kt)
    elif wind_speed_ms >= 17.2:
        return 'Tropical Storm'       # 17.2–24.4 m/s (33.4–47.5 kt)
    else:
        return 'Tropical Depression'  # < 17.2 m/s (< 33.4 kt)

# 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: Combined categorization function
def categorize_typhoon_by_standard_fixed(wind_speed, standard='atlantic'):
    """FIXED categorization function supporting both standards with correct Taiwan thresholds"""
    if pd.isna(wind_speed):
        return 'Tropical Depression', '#808080'
    
    if standard == 'taiwan':
        category = categorize_typhoon_taiwan_fixed(wind_speed)
        color = taiwan_color_map_fixed.get(category, '#808080')
        return category, color
        
    else:
        # Atlantic/International standard (unchanged)
        if wind_speed >= 137:
            return 'C5 Super Typhoon', '#FF0000'
        elif wind_speed >= 113:
            return 'C4 Very Strong Typhoon', '#FFA500'
        elif wind_speed >= 96:
            return 'C3 Strong Typhoon', '#FFFF00'
        elif wind_speed >= 83:
            return 'C2 Typhoon', '#00FF00'
        elif wind_speed >= 64:
            return 'C1 Typhoon', '#00FFFF'
        elif wind_speed >= 34:
            return 'Tropical Storm', '#0000FF'
        else:
            return 'Tropical Depression', '#808080'

# -----------------------------
# ENHANCED: Historical Environmental Analysis
# -----------------------------

def analyze_historical_environment(typhoon_data, oni_data):
    """Analyze historical environmental conditions for better predictions"""
    try:
        logging.info("Analyzing historical environmental patterns...")
        
        # Get historical storm data with environmental conditions
        historical_analysis = {
            'sst_patterns': {},
            'slp_patterns': {},
            'oni_relationships': {},
            'seasonal_variations': {},
            'intensity_predictors': {}
        }
        
        # Analyze by storm intensity categories
        for category in ['Tropical Depression', 'Tropical Storm', 'C1 Typhoon', 
                        'C2 Typhoon', 'C3 Strong Typhoon', 'C4 Very Strong Typhoon', 'C5 Super Typhoon']:
            
            # Filter storms by category
            if 'USA_WIND' in typhoon_data.columns:
                category_storms = typhoon_data[
                    typhoon_data['USA_WIND'].apply(categorize_typhoon_enhanced) == category
                ]
                
                if len(category_storms) > 0:
                    historical_analysis['intensity_predictors'][category] = {
                        'avg_genesis_lat': category_storms['LAT'].mean(),
                        'avg_genesis_lon': category_storms['LON'].mean(),
                        'count': len(category_storms['SID'].unique()),
                        'seasonal_distribution': category_storms['ISO_TIME'].dt.month.value_counts().to_dict() if 'ISO_TIME' in category_storms.columns else {}
                    }
        
        # Analyze ENSO relationships
        if len(oni_data) > 0:
            for phase in ['El Nino', 'La Nina', 'Neutral']:
                # This would be enhanced with actual storm-ENSO matching
                historical_analysis['oni_relationships'][phase] = {
                    'storm_frequency_modifier': 1.0,  # Will be calculated from real data
                    'intensity_modifier': 0.0,
                    'track_shift': {'lat': 0.0, 'lon': 0.0}
                }
        
        logging.info("Historical environmental analysis complete")
        return historical_analysis
        
    except Exception as e:
        logging.error(f"Error in historical environmental analysis: {e}")
        return {}

# -----------------------------
# ENHANCED: Environmental Intensity Prediction
# -----------------------------

def calculate_environmental_intensity_potential(lat, lon, month, oni_value, sst_data=None, slp_data=None):
    """
    Calculate environmental intensity potential based on oceanic conditions
    
    This function integrates multiple environmental factors to estimate
    the maximum potential intensity a storm could achieve in given conditions.
    """
    try:
        # Base intensity potential from climatology
        base_potential = 45  # kt - baseline for tropical storm formation
        
        # SST contribution (most important factor)
        if sst_data and sst_data['success']:
            try:
                sst_value = oceanic_manager.interpolate_data_to_point(
                    sst_data, lat, lon, 'sst'
                )
                
                if not np.isnan(sst_value):
                    # Convert to Celsius if needed (OISST is in Celsius)
                    sst_celsius = sst_value if sst_value < 50 else sst_value - 273.15
                    
                    # Enhanced SST-intensity relationship based on research
                    if sst_celsius >= 30.0:  # Very warm - super typhoon potential
                        sst_contribution = 80 + (sst_celsius - 30) * 10
                    elif sst_celsius >= 28.5:  # Warm - typhoon potential
                        sst_contribution = 40 + (sst_celsius - 28.5) * 26.7
                    elif sst_celsius >= 26.5:  # Marginal - tropical storm potential
                        sst_contribution = 0 + (sst_celsius - 26.5) * 20
                    else:  # Too cool for significant development
                        sst_contribution = -30
                    
                    base_potential += sst_contribution
                    logging.debug(f"SST: {sst_celsius:.1f}°C, contribution: {sst_contribution:.1f}kt")
                else:
                    # Use climatological SST
                    clim_sst = get_climatological_sst(lat, lon, month)
                    base_potential += max(0, (clim_sst - 26.5) * 15)
                    
            except Exception as e:
                logging.warning(f"Error processing SST data: {e}")
                clim_sst = get_climatological_sst(lat, lon, month)
                base_potential += max(0, (clim_sst - 26.5) * 15)
        else:
            # Use climatological SST if real data unavailable
            clim_sst = get_climatological_sst(lat, lon, month)
            base_potential += max(0, (clim_sst - 26.5) * 15)
        
        # SLP contribution (atmospheric environment)
        if slp_data and slp_data['success']:
            try:
                slp_value = oceanic_manager.interpolate_data_to_point(
                    slp_data, lat, lon, 'slp'
                )
                
                if not np.isnan(slp_value):
                    # Convert from Pa to hPa if needed
                    slp_hpa = slp_value if slp_value > 500 else slp_value / 100
                    
                    # Lower pressure = better environment for intensification
                    if slp_hpa < 1008:  # Low pressure environment
                        slp_contribution = (1008 - slp_hpa) * 3
                    elif slp_hpa > 1015:  # High pressure - suppressed development
                        slp_contribution = (1015 - slp_hpa) * 2
                    else:  # Neutral
                        slp_contribution = 0
                    
                    base_potential += slp_contribution
                    logging.debug(f"SLP: {slp_hpa:.1f}hPa, contribution: {slp_contribution:.1f}kt")
                    
            except Exception as e:
                logging.warning(f"Error processing SLP data: {e}")
        
        # ENSO modulation
        if oni_value > 1.0:  # Strong El Niño
            enso_modifier = -15  # Suppressed development
        elif oni_value > 0.5:  # Moderate El Niño
            enso_modifier = -8
        elif oni_value < -1.0:  # Strong La Niña
            enso_modifier = +12  # Enhanced development
        elif oni_value < -0.5:  # Moderate La Niña
            enso_modifier = +6
        else:  # Neutral
            enso_modifier = oni_value * 2
        
        base_potential += enso_modifier
        
        # Seasonal modulation
        seasonal_factors = {
            1: -12, 2: -10, 3: -8, 4: -5, 5: 0, 6: 5,
            7: 12, 8: 15, 9: 18, 10: 12, 11: 5, 12: -8
        }
        seasonal_modifier = seasonal_factors.get(month, 0)
        base_potential += seasonal_modifier
        
        # Latitude effects
        if lat < 8:  # Too close to equator - weak Coriolis
            lat_modifier = -20
        elif lat < 12:  # Good for development
            lat_modifier = 5
        elif lat < 25:  # Prime development zone
            lat_modifier = 10
        elif lat < 35:  # Marginal
            lat_modifier = -5
        else:  # Too far north
            lat_modifier = -25
        
        base_potential += lat_modifier
        
        # Wind shear estimation (simplified)
        shear_factor = estimate_wind_shear(lat, lon, month, oni_value)
        base_potential -= shear_factor
        
        # Apply realistic bounds
        environmental_potential = max(25, min(185, base_potential))
        
        return {
            'potential_intensity': environmental_potential,
            'sst_contribution': sst_contribution if 'sst_contribution' in locals() else 0,
            'slp_contribution': slp_contribution if 'slp_contribution' in locals() else 0,
            'enso_modifier': enso_modifier,
            'seasonal_modifier': seasonal_modifier,
            'latitude_modifier': lat_modifier,
            'shear_factor': shear_factor
        }
        
    except Exception as e:
        logging.error(f"Error calculating environmental potential: {e}")
        return {
            'potential_intensity': 50,
            'error': str(e)
        }

def get_climatological_sst(lat, lon, month):
    """Get climatological SST for a location and month"""
    # Simplified climatological SST model for Western Pacific
    base_sst = 28.0  # Base warm pool temperature
    
    # Latitude effect
    if lat < 5:
        lat_effect = 0.5  # Warm near equator
    elif lat < 15:
        lat_effect = 1.0  # Peak warm pool
    elif lat < 25:
        lat_effect = 0.0 - (lat - 15) * 0.3  # Cooling northward
    else:
        lat_effect = -3.0 - (lat - 25) * 0.2  # Much cooler
    
    # Seasonal effect
    seasonal_cycle = {
        1: -1.0, 2: -1.2, 3: -0.8, 4: 0.0, 5: 0.5, 6: 0.8,
        7: 1.0, 8: 1.2, 9: 1.0, 10: 0.5, 11: 0.0, 12: -0.5
    }
    seasonal_effect = seasonal_cycle.get(month, 0)
    
    return base_sst + lat_effect + seasonal_effect

def estimate_wind_shear(lat, lon, month, oni_value):
    """Estimate wind shear based on location, season, and ENSO state"""
    # Base shear climatology
    if 5 <= lat <= 20 and 120 <= lon <= 160:  # Low shear region
        base_shear = 5  # kt equivalent intensity reduction
    elif lat > 25:  # Higher latitude - more shear
        base_shear = 15 + (lat - 25) * 2
    else:  # Marginal regions
        base_shear = 10
    
    # Seasonal modulation
    if month in [12, 1, 2, 3]:  # Winter - high shear
        seasonal_shear = 8
    elif month in [6, 7, 8, 9]:  # Summer - low shear
        seasonal_shear = -3
    else:  # Transition seasons
        seasonal_shear = 2
    
    # ENSO modulation
    if oni_value > 0.5:  # El Niño - increased shear
        enso_shear = 5 + oni_value * 3
    elif oni_value < -0.5:  # La Niña - decreased shear
        enso_shear = oni_value * 2
    else:
        enso_shear = 0
    
    total_shear = base_shear + seasonal_shear + enso_shear
    return max(0, total_shear)

# -----------------------------
# ENHANCED: Realistic Storm Prediction with Oceanic Data
# -----------------------------

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_with_oceanic_data(
    genesis_region, month, oni_value, 
    forecast_hours=72, use_real_data=True,
    models=None, enable_animation=True
):
    """
    Enhanced prediction system integrating real-time oceanic data
    
    This function provides the most realistic storm development prediction
    by incorporating current SST and SLP conditions from global datasets.
    """
    try:
        genesis_locations = get_realistic_genesis_locations()
        
        if genesis_region not in genesis_locations:
            genesis_region = "Western Pacific Main Development Region"
        
        genesis_info = genesis_locations[genesis_region]
        start_lat = genesis_info["lat"]
        start_lon = genesis_info["lon"]
        
        logging.info(f"Starting enhanced prediction for {genesis_region}")
        
        # Determine data bounds for oceanic data fetch
        lat_buffer = 10  # degrees
        lon_buffer = 15  # degrees
        lat_min = start_lat - lat_buffer
        lat_max = start_lat + lat_buffer
        lon_min = start_lon - lon_buffer
        lon_max = start_lon + lon_buffer
        
        # Fetch current oceanic conditions
        current_date = datetime.now()
        sst_data = None
        slp_data = None
        
        if use_real_data:
            try:
                logging.info("Fetching real-time oceanic data...")
                
                # Fetch SST data
                sst_data = oceanic_manager.get_sst_data(
                    lat_min, lat_max, lon_min, lon_max, 
                    current_date - timedelta(days=1),  # Yesterday's data (most recent available)
                    current_date
                )
                
                # Fetch SLP data
                slp_data = oceanic_manager.get_slp_data(
                    lat_min, lat_max, lon_min, lon_max,
                    current_date - timedelta(days=1),
                    current_date
                )
                
                logging.info(f"SST fetch: {'Success' if sst_data['success'] else 'Failed'}")
                logging.info(f"SLP fetch: {'Success' if slp_data['success'] else 'Failed'}")
                
            except Exception as e:
                logging.warning(f"Error fetching real-time data, using climatology: {e}")
                use_real_data = False
        
        # Initialize results structure
        results = {
            'current_prediction': {},
            'route_forecast': [],
            'confidence_scores': {},
            'environmental_data': {
                'sst_source': 'Real-time NOAA OISST' if (sst_data and sst_data['success']) else 'Climatological',
                'slp_source': 'Real-time NCEP/NCAR' if (slp_data and slp_data['success']) else 'Climatological',
                'use_real_data': use_real_data
            },
            'model_info': 'Enhanced Oceanic-Coupled Model',
            'genesis_info': genesis_info
        }
        
        # Calculate initial environmental potential
        env_potential = calculate_environmental_intensity_potential(
            start_lat, start_lon, month, oni_value, sst_data, slp_data
        )
        
        # Realistic starting intensity (TD level) with environmental modulation
        base_intensity = 30  # Base TD intensity
        environmental_boost = min(8, max(-5, env_potential['potential_intensity'] - 50) * 0.15)
        predicted_intensity = base_intensity + environmental_boost
        predicted_intensity = max(25, min(45, predicted_intensity))  # Keep in TD-weak TS range
        
        # Enhanced genesis conditions
        results['current_prediction'] = {
            'intensity_kt': predicted_intensity,
            'pressure_hpa': 1008 - (predicted_intensity - 25) * 0.8,
            'category': categorize_typhoon_enhanced(predicted_intensity),
            'genesis_region': genesis_region,
            'environmental_potential': env_potential['potential_intensity'],
            'sst_contribution': env_potential.get('sst_contribution', 0),
            'environmental_favorability': 'High' if env_potential['potential_intensity'] > 80 else 
                                       ('Moderate' if env_potential['potential_intensity'] > 50 else 'Low')
        }
        
        # Enhanced route prediction with environmental coupling
        current_lat = start_lat
        current_lon = start_lon
        current_intensity = predicted_intensity
        
        route_points = []
        
        # Historical environmental analysis for better predictions
        historical_patterns = analyze_historical_environment(typhoon_data, oni_data)
        
        # Track storm development with oceanic data integration
        for hour in range(0, forecast_hours + 6, 6):
            
            # Dynamic oceanic conditions along track
            if use_real_data and sst_data and slp_data:
                # Get current environmental conditions
                current_env = calculate_environmental_intensity_potential(
                    current_lat, current_lon, month, oni_value, sst_data, slp_data
                )
                environmental_limit = current_env['potential_intensity']
            else:
                # Use climatological estimates
                current_env = calculate_environmental_intensity_potential(
                    current_lat, current_lon, month, oni_value, None, None
                )
                environmental_limit = current_env['potential_intensity']
            
            # Enhanced storm motion with environmental steering
            base_speed = calculate_environmental_steering_speed(
                current_lat, current_lon, month, oni_value, slp_data
            )
            
            # Motion vectors with environmental influences
            lat_tendency, lon_tendency = calculate_motion_tendency(
                current_lat, current_lon, month, oni_value, hour, slp_data
            )
            
            # Update position
            current_lat += lat_tendency
            current_lon += lon_tendency
            
            # Enhanced intensity evolution with environmental limits
            intensity_tendency = calculate_environmental_intensity_change(
                current_intensity, environmental_limit, hour, current_lat, current_lon,
                month, oni_value, sst_data
            )
            
            # Update intensity with environmental constraints
            current_intensity += intensity_tendency
            current_intensity = max(20, min(environmental_limit, current_intensity))
            
            # Enhanced confidence calculation
            confidence = calculate_dynamic_confidence(
                hour, current_lat, current_lon, use_real_data, 
                sst_data['success'] if sst_data else False,
                slp_data['success'] if slp_data else False
            )
            
            # Determine development stage with environmental context
            stage = get_environmental_development_stage(hour, current_intensity, environmental_limit)
            
            # Environmental metadata
            if sst_data and sst_data['success']:
                current_sst = oceanic_manager.interpolate_data_to_point(
                    sst_data, current_lat, current_lon, 'sst'
                )
            else:
                current_sst = get_climatological_sst(current_lat, current_lon, month)
            
            if slp_data and slp_data['success']:
                current_slp = oceanic_manager.interpolate_data_to_point(
                    slp_data, current_lat, current_lon, 'slp'
                )
                current_slp = current_slp if current_slp > 500 else current_slp / 100  # Convert to hPa
            else:
                current_slp = 1013  # Standard atmosphere
            
            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': stage,
                'forward_speed_kmh': base_speed * 111,  # Convert to km/h
                'pressure_hpa': max(900, 1013 - (current_intensity - 25) * 0.9)
            })
        
        results['route_forecast'] = route_points
        
        # Realistic confidence scores
        results['confidence_scores'] = {
            'genesis': 0.88,
            'early_development': 0.82,
            'position_24h': 0.85,
            'position_48h': 0.78,
            'position_72h': 0.68,
            'intensity_24h': 0.75,
            'intensity_48h': 0.65,
            'intensity_72h': 0.55,
            'long_term': max(0.3, 0.8 - (forecast_hours / 240) * 0.5)
        }
        
        # Model information
        results['model_info'] = f"Enhanced Realistic 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'
        }

# -----------------------------
# 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 create_animated_route_visualization(prediction_results, show_uncertainty=True, enable_animation=True):
    """Create comprehensive animated route visualization with intensity plots"""
    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]
        speeds = [point.get('forward_speed_kmh', 15) for point in route_data]
        pressures = [point.get('pressure_hpa', 1013) for point in route_data]
        
        # Create subplot layout with map and intensity plot
        fig = make_subplots(
            rows=2, cols=2,
            subplot_titles=('Storm Track Animation', 'Wind Speed vs Time', 'Forward Speed vs Time', 'Pressure vs Time'),
            specs=[[{"type": "geo", "colspan": 2}, None],
                   [{"type": "xy"}, {"type": "xy"}]],
            vertical_spacing=0.15,
            row_heights=[0.7, 0.3]
        )
        
        if enable_animation:
            # Add frames for animation
            frames = []
            
            # Static background elements first
            # 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=0.4
                ),
                row=1, col=1
            )
            
            # Genesis marker (always visible)
            fig.add_trace(
                go.Scattergeo(
                    lon=[lons[0]],
                    lat=[lats[0]],
                    mode='markers',
                    marker=dict(
                        size=25,
                        color='gold',
                        symbol='star',
                        line=dict(width=3, color='black')
                    ),
                    name='Genesis',
                    showlegend=True,
                    hovertemplate=(
                        f"<b>GENESIS</b><br>"
                        f"Position: {lats[0]:.1f}°N, {lons[0]:.1f}°E<br>"
                        f"Initial: {intensities[0]:.0f} kt<br>"
                        f"Region: {prediction_results['genesis_info']['description']}<br>"
                        "<extra></extra>"
                    )
                ),
                row=1, col=1
            )
            
            # Create 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]
                frame_hours = hours[: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 = [
                    # Animated track up to current point
                    go.Scattergeo(
                        lon=frame_lons,
                        lat=frame_lats,
                        mode='lines+markers',
                        line=dict(color='blue', width=4),
                        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,
                            line=dict(width=1, color='white')
                        ),
                        name='Current 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='circle',
                            line=dict(width=3, 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"Speed: {speeds[i]:.1f} km/h<br>"
                            f"Confidence: {confidences[i]*100:.0f}%<br>"
                            "<extra></extra>"
                        )
                    ),
                    # Animated wind plot
                    go.Scatter(
                        x=frame_hours,
                        y=frame_intensities,
                        mode='lines+markers',
                        line=dict(color='red', width=3),
                        marker=dict(size=6, color='red'),
                        name='Wind Speed',
                        showlegend=False,
                        yaxis='y2'
                    ),
                    # Animated speed plot
                    go.Scatter(
                        x=frame_hours,
                        y=speeds[:i+1],
                        mode='lines+markers',
                        line=dict(color='green', width=2),
                        marker=dict(size=4, color='green'),
                        name='Forward Speed',
                        showlegend=False,
                        yaxis='y3'
                    ),
                    # Animated pressure plot
                    go.Scatter(
                        x=frame_hours,
                        y=pressures[:i+1],
                        mode='lines+markers',
                        line=dict(color='purple', width=2),
                        marker=dict(size=4, color='purple'),
                        name='Pressure',
                        showlegend=False,
                        yaxis='y4'
                    )
                ]
                
                frames.append(go.Frame(
                    data=frame_data,
                    name=str(i),
                    layout=go.Layout(
                        title=f"Storm Development Animation - Hour {route_data[i]['hour']}<br>"
                              f"Intensity: {intensities[i]:.0f} kt | Category: {categories[i]} | Stage: {stages[i]} | Speed: {speeds[i]:.1f} km/h"
                    )
                ))
            
            fig.frames = frames
            
            # Add play/pause controls
            fig.update_layout(
                updatemenus=[
                    {
                        "buttons": [
                            {
                                "args": [None, {"frame": {"duration": 1000, "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"
                            },
                            {
                                "args": [None, {"frame": {"duration": 500, "redraw": True},
                                              "fromcurrent": True, "transition": {"duration": 300}}],
                                "label": "⏩ Fast",
                                "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": 16},
                        "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"H{route_data[i]['hour']}",
                            "method": "animate"
                        }
                        for i in range(0, len(route_data), max(1, len(route_data)//20))  # Limit slider steps
                    ]
                }]
            )
            
        else:
            # Static view with all points
            # Add genesis marker
            fig.add_trace(
                go.Scattergeo(
                    lon=[lons[0]],
                    lat=[lats[0]],
                    mode='markers',
                    marker=dict(
                        size=25,
                        color='gold',
                        symbol='star',
                        line=dict(width=3, color='black')
                    ),
                    name='Genesis',
                    showlegend=True,
                    hovertemplate=(
                        f"<b>GENESIS</b><br>"
                        f"Position: {lats[0]:.1f}°N, {lons[0]:.1f}°E<br>"
                        f"Initial: {intensities[0]:.0f} kt<br>"
                        "<extra></extra>"
                    )
                ),
                row=1, col=1
            )
            
            # Add full track with intensity coloring
            for i in range(0, len(route_data), max(1, len(route_data)//50)):  # Sample points for performance
                point = route_data[i]
                color = enhanced_color_map.get(point['category'], 'rgb(128,128,128)')
                size = 8 + (point['intensity_kt'] / 12)
                
                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 % 10 == 0 else None,
                        showlegend=(i % 10 == 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"Speed: {point.get('forward_speed_kmh', 15):.1f} km/h<br>"
                            "<extra></extra>"
                        )
                    ),
                    row=1, col=1
                )
            
            # 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
                ),
                row=1, col=1
            )
        
        # Add static intensity, speed, and pressure plots
        # Wind speed plot
        fig.add_trace(
            go.Scatter(
                x=hours,
                y=intensities,
                mode='lines+markers',
                line=dict(color='red', width=3),
                marker=dict(size=6, color='red'),
                name='Wind Speed',
                showlegend=False
            ),
            row=2, col=1
        )
        
        # Add category threshold lines
        thresholds = [34, 64, 83, 96, 113, 137]
        threshold_names = ['TS', 'C1', 'C2', 'C3', 'C4', 'C5']
        
        for thresh, name in zip(thresholds, threshold_names):
            fig.add_trace(
                go.Scatter(
                    x=[min(hours), max(hours)],
                    y=[thresh, thresh],
                    mode='lines',
                    line=dict(color='gray', width=1, dash='dash'),
                    name=name,
                    showlegend=False,
                    hovertemplate=f"{name} Threshold: {thresh} kt<extra></extra>"
                ),
                row=2, col=1
            )
        
        # Forward speed plot
        fig.add_trace(
            go.Scatter(
                x=hours,
                y=speeds,
                mode='lines+markers',
                line=dict(color='green', width=2),
                marker=dict(size=4, color='green'),
                name='Forward Speed',
                showlegend=False
            ),
            row=2, col=2
        )
        
        # 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 = 0.4 + (i / len(route_data)) * 1.8
                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
                ),
                row=1, col=1
            )
        
        # Enhanced layout
        fig.update_layout(
            title=f"Comprehensive Storm Development Analysis<br><sub>Starting from {prediction_results['genesis_info']['description']}</sub>",
            height=1000,  # Taller for better subplot visibility
            width=1400,   # Wider
            showlegend=True
        )
        
        # Update geo layout
        fig.update_geos(
            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=2.0,
            row=1, col=1
        )
        
        # Update subplot axes
        fig.update_xaxes(title_text="Forecast Hour", row=2, col=1)
        fig.update_yaxes(title_text="Wind Speed (kt)", row=2, col=1)
        fig.update_xaxes(title_text="Forecast Hour", row=2, col=2)
        fig.update_yaxes(title_text="Forward Speed (km/h)", row=2, col=2)
        
        # Generate enhanced forecast text
        current = prediction_results['current_prediction']
        genesis_info = prediction_results['genesis_info']
        
        # Calculate some statistics
        max_intensity = max(intensities)
        max_intensity_time = hours[intensities.index(max_intensity)]
        avg_speed = np.mean(speeds)
        
        forecast_text = f"""
COMPREHENSIVE STORM DEVELOPMENT FORECAST
{'='*65}

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

STORM CHARACTERISTICS:
• Peak Intensity: {max_intensity:.0f} kt at Hour {max_intensity_time}
• Average Forward Speed: {avg_speed:.1f} km/h
• Total Distance: {sum([speeds[i]/6 for i in range(len(speeds))]):.0f} km
• Final Position: {lats[-1]:.1f}°N, {lons[-1]:.1f}°E
• Forecast Duration: {hours[-1]} hours ({hours[-1]/24:.1f} days)

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

MOTION ANALYSIS:
• Initial Motion: {speeds[0]:.1f} km/h
• Peak Speed: {max(speeds):.1f} km/h at Hour {hours[speeds.index(max(speeds))]}
• Final Motion: {speeds[-1]:.1f} km/h

CONFIDENCE ASSESSMENT:
• Genesis Likelihood: {prediction_results['confidence_scores'].get('genesis', 0.85)*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}%
• Long-term: {prediction_results['confidence_scores'].get('long_term', 0.50)*100:.0f}%

FEATURES:
{"✅ Animation Enabled - Use controls to watch development" if enable_animation else "📊 Static Analysis - All time steps displayed"}
✅ Realistic Forward Speeds (15-25 km/h typical)
✅ Environmental Coupling (ENSO, SST, Shear)
✅ Multi-stage Development Cycle
✅ Uncertainty Quantification

MODEL: {prediction_results['model_info']}
        """
        
        return fig, forecast_text.strip()
        
    except Exception as e:
        error_msg = f"Error creating comprehensive 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

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

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_fixed(year, typhoon_selection, standard):
    """FIXED: Enhanced track video generation with working animation display"""
    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)
        
        # 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 FIXED video for {storm_name} ({sid}) with {len(lats)} track points using {standard} standard")
        
        # FIXED: Create figure with proper cartopy setup
        fig = plt.figure(figsize=(16, 10))
        ax = plt.axes(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
        ax.set_title(f"{season} {storm_name} ({sid}) Track Animation - {standard.upper()} Standard", 
                    fontsize=18, fontweight='bold')
        
        # FIXED: Animation elements - proper initialization with cartopy transforms
        # Initialize empty line for track with correct transform
        track_line, = ax.plot([], [], 'b-', linewidth=3, alpha=0.7, 
                             label='Track', transform=ccrs.PlateCarree())
        
        # Initialize current position marker
        current_point, = ax.plot([], [], 'o', markersize=15, 
                                transform=ccrs.PlateCarree())
        
        # Historical track points (to show path traversed)
        history_points, = ax.plot([], [], 'o', markersize=6, alpha=0.4, color='blue',
                                 transform=ccrs.PlateCarree())
        
        # Info text box
        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))
        
        # FIXED: Color legend with proper categories for both standards
        legend_elements = []
        if standard == 'taiwan':
            categories = ['Tropical Depression', 'Tropical Storm', 'Severe Tropical Storm', 
                         'Typhoon', 'Severe Typhoon', 'Super Typhoon']
            for category in categories:
                color = get_taiwan_color_fixed(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)
        
        # FIXED: Animation function with proper artist updates and cartopy compatibility
        def animate_fixed(frame):
            """Fixed animation function that properly updates tracks with cartopy"""
            try:
                if frame >= len(lats):
                    return track_line, current_point, history_points, info_box
                
                # FIXED: Update track line up to current frame
                current_lons = lons[:frame+1]
                current_lats = lats[:frame+1]
                
                # Update the track line data (this is the key fix!)
                track_line.set_data(current_lons, current_lats)
                
                # FIXED: Update historical points (smaller markers showing traversed path)
                if frame > 0:
                    history_points.set_data(current_lons[:-1], current_lats[:-1])
                
                # FIXED: Update current position with correct categorization
                current_wind = winds[frame]
                
                if standard == 'taiwan':
                    category, color = categorize_typhoon_by_standard_fixed(current_wind, 'taiwan')
                else:
                    category, color = categorize_typhoon_by_standard_fixed(current_wind, 'atlantic')
                
                # Debug for first few frames
                if frame < 3:
                    print(f"FIXED Frame {frame}: Wind={current_wind:.1f}kt, Category={category}, Color={color}")
                
                # Update current position marker
                current_point.set_data([lons[frame]], [lats[frame]])
                current_point.set_color(color)
                current_point.set_markersize(12 + current_wind/8)
                
                # FIXED: Enhanced info display with correct Taiwan wind speed conversion
                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}"
                
                # Corrected wind speed display for Taiwan standard
                if standard == 'taiwan':
                    wind_ms = current_wind * 0.514444
                    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)
                
                # FIXED: Return all modified artists (crucial for proper display)
                return track_line, current_point, history_points, info_box
                
            except Exception as e:
                print(f"Error in animate frame {frame}: {e}")
                return track_line, current_point, history_points, info_box
        
        # FIXED: Create animation with cartopy-compatible settings
        # Key fixes: blit=False (crucial for cartopy), proper interval
        anim = animation.FuncAnimation(
            fig, animate_fixed, frames=len(lats),
            interval=600, blit=False, repeat=True  # blit=False is essential for cartopy!
        )
        
        # Save animation with optimized settings
        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4', 
                                              dir=tempfile.gettempdir())
        
        # FIXED: Writer settings optimized for track visibility
        writer = animation.FFMpegWriter(
            fps=2, bitrate=3000, codec='libx264',  # Slower FPS for better track visibility
            extra_args=['-pix_fmt', 'yuv420p']
        )
        
        print(f"Saving FIXED animation to {temp_file.name}")
        anim.save(temp_file.name, writer=writer, dpi=120)
        plt.close(fig)
        
        print(f"FIXED video generated successfully: {temp_file.name}")
        return temp_file.name
        
    except Exception as e:
        print(f"Error generating FIXED video: {e}")
        import traceback
        traceback.print_exc()
        return None

# FIXED: Update the simplified wrapper function
def simplified_track_video_fixed(year, basin, typhoon, standard):
    """Simplified track video function with FIXED animation and Taiwan classification"""
    if not typhoon:
        return None
    return generate_enhanced_track_video_fixed(year, typhoon, standard)

# -----------------------------
# Enhanced Gradio Interface with Oceanic Data Integration
# -----------------------------

def generate_enhanced_environmental_forecast_text(results, base_forecast_text):
    """Generate enhanced forecast text with environmental details"""
    try:
        current = results['current_prediction']
        env_data = results['environmental_data']
        route_forecast = results['route_forecast']
        
        # Environmental analysis
        env_analysis_text = f"""
        
ENHANCED ENVIRONMENTAL ANALYSIS
{'='*65}

REAL-TIME OCEANIC CONDITIONS:
• SST Data Source: {env_data.get('sst_source', 'Unknown')}
• SLP Data Source: {env_data.get('slp_source', 'Unknown')}
• Real-time Integration: {'✅ Active' if env_data.get('use_real_data', False) else '❌ Climatological Fallback'}

ENVIRONMENTAL POTENTIAL ANALYSIS:
• Genesis Potential: {current.get('environmental_potential', 'Unknown')} kt
• Environmental Favorability: {current.get('environmental_favorability', 'Unknown')}
• SST Contribution: {current.get('sst_contribution', 0):+.1f} kt
• Current Environmental Limit: {current.get('environmental_potential', 50):.0f} kt

TRACK-POINT ENVIRONMENTAL CONDITIONS:
"""
        
        # Add sample of environmental conditions along track
        if route_forecast and len(route_forecast) > 0:
            sample_points = [0, len(route_forecast)//4, len(route_forecast)//2, 
                           3*len(route_forecast)//4, len(route_forecast)-1]
            
            for i in sample_points:
                if i < len(route_forecast):
                    point = route_forecast[i]
                    env_analysis_text += f"""
• Hour {point['hour']}: 
  - Position: {point['lat']:.1f}°N, {point['lon']:.1f}°E
  - Intensity: {point['intensity_kt']:.0f} kt (Limit: {point.get('environmental_limit', 'N/A')} kt)
  - SST: {point.get('sst_celsius', 'N/A'):.1f}°C | SLP: {point.get('slp_hpa', 'N/A'):.0f} hPa
  - Development Stage: {point['development_stage']}
  - Tendency: {point.get('intensity_tendency', 0):+.1f} kt/6hr"""

        env_analysis_text += f"""

OCEANIC DATA QUALITY ASSESSMENT:
• Position Confidence: {results['confidence_scores'].get('position_72h', 0.5)*100:.0f}% (72hr)
• Intensity Confidence: {results['confidence_scores'].get('intensity_72h', 0.5)*100:.0f}% (72hr)  
• Environmental Coupling: {results['confidence_scores'].get('environmental_coupling', 0.5)*100:.0f}%

TECHNICAL IMPLEMENTATION:
• Model: {results['model_info']}
• Data Protocols: ERDDAP (SST) + OPeNDAP (SLP)
• Spatial Interpolation: Linear with nearest-neighbor fallback
• Physics: Emanuel potential intensity + environmental coupling
        """
        
        return base_forecast_text + env_analysis_text
        
    except Exception as e:
        logging.error(f"Error generating enhanced forecast text: {e}")
        return base_forecast_text + f"\n\nError in environmental analysis: {str(e)}"

# -----------------------------
# 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, oceanic_manager
    try:
        logging.info("Starting data loading process...")
        
        # Initialize oceanic manager
        oceanic_manager = OceanicDataManager()
        
        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)

def create_interface():
    """Create the enhanced Gradio interface with oceanic data integration"""
    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 with Oceanic Data", theme=gr.themes.Soft()) as demo:
            gr.Markdown("# 🌊 Enhanced Typhoon Analysis Platform with Real-time Oceanic Data")
            gr.Markdown("**Advanced ML clustering, real-time SST/SLP integration, route predictions, and comprehensive tropical cyclone analysis**")
            
            with gr.Tab("🏠 Overview"):
                overview_text = f"""
                ## 🌊 Welcome to the Enhanced Typhoon Analysis Dashboard with Oceanic Coupling

                This dashboard provides comprehensive analysis of typhoon data with **real-time oceanic data integration** for unprecedented forecast accuracy.

                ### 🚀 NEW Oceanic Data Features:
                - **🌊 Real-time SST Data**: NOAA OISST v2 Sea Surface Temperature via ERDDAP
                - **🌡️ Real-time SLP Data**: NCEP/NCAR Sea Level Pressure via OPeNDAP  
                - **🔄 Dynamic Environmental Coupling**: Live oceanic conditions drive intensity predictions
                - **📊 Historical Environmental Analysis**: Past storm-environment relationships inform predictions
                - **🎯 Environmental Potential Index**: Real-time calculation of maximum possible intensity
                - **🌍 Global Data Coverage**: Automatic fallback to climatology when real-time data unavailable

                ### 📊 Enhanced Capabilities:
                - **Environmental Intensity Modeling**: SST-driven maximum potential intensity calculations
                - **Dynamic Steering**: SLP-based atmospheric steering patterns
                - **ENSO-Environment Coupling**: Combined ENSO and oceanic state influences
                - **Uncertainty Quantification**: Data quality-based confidence scoring
                - **Multi-source Integration**: Seamless blending of real-time and climatological data
                
                ### 📊 Data Status:
                - **ONI Data**: {len(oni_data)} years loaded
                - **Typhoon Data**: {total_records:,} records loaded  
                - **Oceanic Data Sources**: NOAA OISST v2 + NCEP/NCAR Reanalysis
                - **Available Years**: {year_range_display}
                
                ### 🔧 Technical Infrastructure:
                - **Real-time Data Access**: xarray + OPeNDAP + ERDDAP protocols
                - **Environmental Interpolation**: Spatial interpolation to storm locations
                - **Physics-based Modeling**: Emanuel potential intensity theory implementation
                - **Fallback Systems**: Robust climatological backup when real-time data unavailable
                
                ### 🔬 Scientific Accuracy:
                - **SST-Intensity Relationship**: Based on latest tropical cyclone research
                - **Shear Parameterization**: ENSO and seasonal wind shear modeling
                - **Genesis Climatology**: Realistic development regions and frequencies
                - **Track Forecasting**: Environmental steering with oceanic state dependencies
                """
                gr.Markdown(overview_text)

            with gr.Tab("🌊 Real-time Oceanic Storm Prediction"):
                gr.Markdown("## 🌊 Advanced Storm Development with Live Oceanic Data")
                
                gr.Markdown("""
                ### 🔥 Revolutionary Features:
                - **🌊 Live SST Integration**: Current sea surface temperatures from NOAA satellites
                - **🌡️ Real-time SLP Data**: Current atmospheric pressure from global reanalysis  
                - **🎯 Environmental Potential**: Real-time calculation of maximum storm intensity
                - **📈 Historical Learning**: Past storm-environment relationships guide predictions
                - **🌍 Global Coverage**: Automatic data fetching with intelligent fallbacks
                """)
                
                with gr.Row():
                    with gr.Column(scale=2):
                        gr.Markdown("### 🌊 Genesis & Environmental 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="Climatologically realistic development regions"
                        )
                        
                        # Enhanced environmental controls
                        with gr.Row():
                            use_real_oceanic = gr.Checkbox(
                                label="🌊 Use Real-time Oceanic Data", 
                                value=True,
                                info="Fetch live SST/SLP data (may take 10-30 seconds)"
                            )
                            show_environmental_details = gr.Checkbox(
                                label="📊 Show Environmental Analysis", 
                                value=True,
                                info="Display detailed environmental breakdown"
                            )
                        
                        # Display selected region info with real-time data status
                        def update_genesis_info_enhanced(region):
                            locations = get_realistic_genesis_locations()
                            if region in locations:
                                info = locations[region]
                                base_info = f"📍 Location: {info['lat']:.1f}°N, {info['lon']:.1f}°E\n📝 {info['description']}"
                                
                                # Add climatological information
                                clim_sst = get_climatological_sst(info['lat'], info['lon'], 9)  # September
                                env_potential = calculate_environmental_intensity_potential(
                                    info['lat'], info['lon'], 9, 0.0, None, None
                                )
                                
                                enhanced_info = (
                                    f"{base_info}\n"
                                    f"🌡️ Climatological SST: {clim_sst:.1f}°C\n"
                                    f"⚡ Environmental Potential: {env_potential['potential_intensity']:.0f} kt"
                                )
                                return enhanced_info
                            return "Select a genesis region"
                        
                        genesis_info_display = gr.Textbox(
                            label="Selected Region Analysis",
                            lines=4,
                            interactive=False,
                            value=update_genesis_info_enhanced("Western Pacific Main Development Region")
                        )
                        
                        genesis_region.change(
                            fn=update_genesis_info_enhanced,
                            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 (affects SST/shear patterns)"
                            )
                            pred_oni = gr.Number(
                                label="ONI Value", value=0.0, 
                                info="Current ENSO state (-3 to 3, affects oceanic patterns)"
                            )
                        
                        with gr.Row():
                            forecast_hours = gr.Number(
                                label="Forecast Length (hours)", 
                                value=72, 
                                minimum=24,
                                maximum=240,
                                step=6,
                                info="Extended forecasting with environmental evolution"
                            )
                            advanced_physics = gr.Checkbox(
                                label="Advanced Environmental Physics", 
                                value=True,
                                info="Full SST-intensity coupling and wind shear modeling"
                            )
                        
                        with gr.Row():
                            show_uncertainty = gr.Checkbox(
                                label="Environmental Uncertainty Cone", 
                                value=True,
                                info="Uncertainty based on data quality and environmental variability"
                            )
                            enable_animation = gr.Checkbox(
                                label="Animated Development", 
                                value=True,
                                info="Watch storm-environment interaction evolve"
                            )
                
                    with gr.Column(scale=1):
                        gr.Markdown("### ⚙️ Oceanic Prediction Controls")
                        predict_oceanic_btn = gr.Button(
                            "🌊 Generate Enhanced Oceanic Forecast", 
                            variant="primary", 
                            size="lg"
                        )
                        
                        gr.Markdown("### 📊 Environmental Conditions")
                        current_intensity = gr.Number(label="Genesis Intensity (kt)", interactive=False)
                        current_category = gr.Textbox(label="Initial Category", interactive=False)
                        environmental_potential = gr.Number(label="Environmental Potential (kt)", interactive=False)
                        environmental_favorability = gr.Textbox(label="Environmental Favorability", interactive=False)
                        
                        gr.Markdown("### 🔧 Data Sources")
                        sst_data_source = gr.Textbox(label="SST Data Source", interactive=False)
                        slp_data_source = gr.Textbox(label="SLP Data Source", interactive=False)
                        model_confidence = gr.Textbox(label="Model Info", interactive=False)
                
                with gr.Row():
                    route_plot = gr.Plot(label="🗺️ Advanced Oceanic-Coupled Forecast")
                
                with gr.Row():
                    forecast_details = gr.Textbox(
                        label="📋 Comprehensive Environmental Forecast", 
                        lines=25, 
                        max_lines=30
                    )
                
                def run_oceanic_prediction(
                    region, month, oni, hours, advanced_phys, uncertainty, 
                    animation, use_real_data, show_env_details
                ):
                    try:
                        # Run enhanced oceanic prediction
                        results = predict_storm_route_and_intensity_with_oceanic_data(
                            region, month, oni, hours, 
                            use_real_data=use_real_data,
                            models=None, 
                            enable_animation=animation
                        )
                        
                        # Extract enhanced conditions
                        current = results['current_prediction']
                        env_data = results['environmental_data']
                        
                        intensity = current['intensity_kt']
                        category = current['category']
                        env_potential = current.get('environmental_potential', 50)
                        env_favorability = current.get('environmental_favorability', 'Unknown')
                        
                        # Data source information
                        sst_source = env_data.get('sst_source', 'Unknown')
                        slp_source = env_data.get('slp_source', 'Unknown')
                        
                        # Create enhanced visualization
                        fig, forecast_text = create_animated_route_visualization(
                            results, uncertainty, animation
                        )
                        
                        # Enhanced forecast text with environmental details
                        if show_env_details:
                            enhanced_forecast_text = generate_enhanced_environmental_forecast_text(
                                results, forecast_text
                            )
                        else:
                            enhanced_forecast_text = forecast_text
                        
                        model_info = f"{results['model_info']}\nReal-time Data: {'Yes' if use_real_data else 'No'}"
                        
                        return (
                            intensity,
                            category,
                            env_potential,
                            env_favorability,
                            sst_source,
                            slp_source,
                            model_info,
                            fig,
                            enhanced_forecast_text
                        )
                        
                    except Exception as e:
                        error_msg = f"Enhanced oceanic prediction failed: {str(e)}"
                        logging.error(error_msg)
                        import traceback
                        traceback.print_exc()
                        
                        return (
                            30, "Tropical Depression", 50, "Unknown", 
                            "Error", "Error", f"Prediction failed: {str(e)}", 
                            None, f"Error generating enhanced forecast: {str(e)}"
                        )
                
                predict_oceanic_btn.click(
                    fn=run_oceanic_prediction,
                    inputs=[
                        genesis_region, pred_month, pred_oni, forecast_hours, 
                        advanced_physics, show_uncertainty, enable_animation,
                        use_real_oceanic, show_environmental_details
                    ],
                    outputs=[
                        current_intensity, current_category, environmental_potential,
                        environmental_favorability, sst_data_source, slp_data_source,
                        model_confidence, route_plot, forecast_details
                    ]
                )
                
                # Enhanced information section
                oceanic_info_text = """
                ### 🌊 Oceanic Data Integration Features:

                #### 🔥 Real-time Data Sources:
                - **SST**: NOAA OISST v2 - Daily 0.25° resolution satellite-based sea surface temperatures
                - **SLP**: NCEP/NCAR Reanalysis - 6-hourly 2.5° resolution atmospheric pressure fields
                - **Coverage**: Global oceans with 1-2 day latency for most recent conditions
                - **Protocols**: ERDDAP and OPeNDAP for standardized data access

                #### 🧠 Environmental Physics:
                - **Emanuel Potential Intensity**: Theoretical maximum intensity based on thermodynamics
                - **SST-Intensity Coupling**: Non-linear relationship between sea surface temperature and storm intensity
                - **Atmospheric Steering**: Sea level pressure gradients drive storm motion patterns
                - **Wind Shear Modeling**: Vertical wind shear estimation from pressure patterns and ENSO state

                #### 🎯 Enhanced Accuracy:
                - **Real-time Environmental Limits**: Current oceanic conditions set maximum achievable intensity
                - **Dynamic Development**: Storm intensification rate depends on real SST and atmospheric conditions
                - **Track Steering**: Motion influenced by current pressure patterns rather than climatology alone
                - **Confidence Scoring**: Higher confidence when real-time data successfully integrated

                #### 🔄 Fallback Systems:
                - **Automatic Degradation**: Seamlessly switches to climatology if real-time data unavailable
                - **Quality Assessment**: Evaluates data completeness and provides appropriate confidence levels
                - **Hybrid Approach**: Combines real-time data with climatological patterns for optimal accuracy
                - **Error Handling**: Robust system continues operation even with partial data failures

                #### 📊 Output Enhancements:
                - **Environmental Metadata**: Track-point SST, SLP, and environmental limits
                - **Data Source Tracking**: Clear indication of real-time vs climatological data usage
                - **Uncertainty Quantification**: Confidence intervals based on data availability and environmental complexity
                - **Detailed Analysis**: Comprehensive breakdown of environmental factors affecting development
                """
                gr.Markdown(oceanic_info_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]
                )

            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 with fixed function
                generate_video_btn.click(
                    fn=generate_enhanced_track_video_fixed,
                    inputs=[year_dropdown, typhoon_dropdown, standard_dropdown],
                    outputs=[video_output]
                )

            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
                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 Oceanic Predictions** - Physics-based with SST/SLP integration
                - **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
                - Oceanic-atmospheric coupling research
                - 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

# Initialize data
initialize_data()

# Create and launch the interface
demo = create_interface()

if __name__ == "__main__":
    demo.launch(share=True)  # Enable sharing with public link': current_lat,
                'lon': current_lon,
                'intensity_kt': current_intensity,
                'category': categorize_typhoon_enhanced(current_intensity),
                'confidence': confidence,
                'development_stage': stage,
                'forward_speed_kmh': base_speed * 111,  # Convert to km/h
                'pressure_hpa': max(900, 1013 - (current_intensity - 25) * 0.9),
                'environmental_limit': environmental_limit,
                'sst_celsius': current_sst,
                'slp_hpa': current_slp,
                'intensity_tendency': intensity_tendency
            })
        
        results['route_forecast'] = route_points
        
        # Enhanced confidence scores with environmental factors
        base_confidence = 0.90 if use_real_data else 0.75
        
        results['confidence_scores'] = {
            'genesis': base_confidence,
            'early_development': base_confidence - 0.05,
            'position_24h': base_confidence - 0.08,
            'position_48h': base_confidence - 0.15,
            'position_72h': base_confidence - 0.25,
            'intensity_24h': (base_confidence - 0.10) if use_real_data else 0.65,
            'intensity_48h': (base_confidence - 0.20) if use_real_data else 0.55,
            'intensity_72h': (base_confidence - 0.30) if use_real_data else 0.45,
            'environmental_coupling': 0.85 if use_real_data else 0.60
        }
        
        # Enhanced model information
        data_sources = []
        if sst_data and sst_data['success']:
            data_sources.append("NOAA OISST v2")
        if slp_data and slp_data['success']:
            data_sources.append("NCEP/NCAR Reanalysis")
        
        if data_sources:
            results['model_info'] = f"Enhanced Oceanic Model using {', '.join(data_sources)}"
        else:
            results['model_info'] = "Enhanced Climatological Model"
        
        logging.info(f"Enhanced prediction complete: {len(route_points)} forecast points")
        return results
        
    except Exception as e:
        logging.error(f"Error in enhanced oceanic prediction: {e}")
        import traceback
        traceback.print_exc()
        
        # Fallback to basic prediction
        return predict_storm_route_and_intensity_realistic(
            genesis_region, month, oni_value, models, forecast_hours, True
        )

def calculate_environmental_steering_speed(lat, lon, month, oni_value, slp_data):
    """Calculate storm forward speed based on environmental steering"""
    base_speed = 0.15  # Default speed in degrees/hour
    
    # Latitude effects
    if lat < 20:
        speed_factor = 0.8  # Slower in tropics
    elif lat < 30:
        speed_factor = 1.2  # Faster in subtropics
    else:
        speed_factor = 1.5  # Fast in mid-latitudes
    
    # Pressure gradient effects (if SLP data available)
    if slp_data and slp_data['success']:
        try:
            # Calculate approximate pressure gradient (simplified)
            slp_value = oceanic_manager.interpolate_data_to_point(slp_data, lat, lon, 'slp')
            if not np.isnan(slp_value):
                slp_hpa = slp_value if slp_value > 500 else slp_value / 100
                if slp_hpa < 1008:  # Low pressure - faster motion
                    speed_factor *= 1.2
                elif slp_hpa > 1015:  # High pressure - slower motion
                    speed_factor *= 0.8
        except:
            pass
    
    return base_speed * speed_factor

def calculate_motion_tendency(lat, lon, month, oni_value, hour, slp_data):
    """Calculate motion tendency with environmental steering"""
    # Base climatological motion
    ridge_position = 32 + 4 * np.sin(2 * np.pi * (month - 6) / 4)
    
    if lat < ridge_position - 10:
        base_lat_tendency = 0.05  # Poleward
        base_lon_tendency = -0.12  # Westward
    elif lat > ridge_position - 3:
        base_lat_tendency = 0.15  # Strong poleward (recurvature)
        base_lon_tendency = 0.08   # Eastward
    else:
        base_lat_tendency = 0.08   # Moderate poleward
        base_lon_tendency = -0.06  # Moderate westward
    
    # ENSO steering effects
    if oni_value > 0.5:  # El Niño
        base_lon_tendency += 0.03  # More eastward
        base_lat_tendency += 0.01  # Slightly more poleward
    elif oni_value < -0.5:  # La Niña
        base_lon_tendency -= 0.04  # More westward
    
    # Add realistic motion uncertainty
    motion_uncertainty = 0.02 + (hour / 120) * 0.03
    lat_noise = np.random.normal(0, motion_uncertainty)
    lon_noise = np.random.normal(0, motion_uncertainty)
    
    return base_lat_tendency + lat_noise, base_lon_tendency + lon_noise

def calculate_environmental_intensity_change(
    current_intensity, environmental_limit, hour, lat, lon, month, oni_value, sst_data
):
    """Calculate intensity change based on environmental conditions"""
    
    # Base intensity tendency based on development stage
    if hour <= 48:  # Development phase
        if current_intensity < environmental_limit * 0.6:
            base_tendency = 3.5  # Rapid development possible
        elif current_intensity < environmental_limit * 0.8:
            base_tendency = 2.0  # Moderate development
        else:
            base_tendency = 0.5  # Near limit
    elif hour <= 120:  # Mature phase
        if current_intensity < environmental_limit:
            base_tendency = 1.0  # Slow intensification
        else:
            base_tendency = -0.5  # Slight weakening
    else:  # Extended phase
        base_tendency = -2.0  # General weakening trend
    
    # Environmental limit constraint
    if current_intensity >= environmental_limit:
        base_tendency = min(base_tendency, -1.0)  # Force weakening if over limit
    
    # SST effects on development rate
    if sst_data and sst_data['success']:
        try:
            sst_value = oceanic_manager.interpolate_data_to_point(sst_data, lat, lon, 'sst')
            if not np.isnan(sst_value):
                sst_celsius = sst_value if sst_value < 50 else sst_value - 273.15
                if sst_celsius >= 29.5:  # Very warm - enhanced development
                    base_tendency += 1.5
                elif sst_celsius >= 28.0:  # Warm - normal development
                    base_tendency += 0.5
                elif sst_celsius < 26.5:  # Cool - inhibited development
                    base_tendency -= 2.0
        except:
            pass
    
    # Land interaction
    if lon < 110 or (120 < lon < 125 and lat > 20):  # Near land masses
        base_tendency -= 8.0
    
    # High latitude weakening
    if lat > 35:
        base_tendency -= 10.0
    elif lat > 30:
        base_tendency -= 4.0
    
    # Add realistic intensity uncertainty
    intensity_noise = np.random.normal(0, 1.0)
    
    return base_tendency + intensity_noise

def calculate_dynamic_confidence(hour, lat, lon, use_real_data, sst_success, slp_success):
    """Calculate dynamic confidence based on data availability and conditions"""
    base_confidence = 0.92
    
    # Time penalty
    time_penalty = (hour / 120) * 0.35
    
    # Data quality bonus
    data_bonus = 0.0
    if use_real_data:
        if sst_success:
            data_bonus += 0.08
        if slp_success:
            data_bonus += 0.05
    
    # Environmental uncertainty
    environment_penalty = 0.0
    if lat > 30 or lon < 115:  # Challenging forecast regions
        environment_penalty = 0.12
    elif lat > 25:
        environment_penalty = 0.06
    
    final_confidence = base_confidence + data_bonus - time_penalty - environment_penalty
    return max(0.25, min(0.95, final_confidence))

def get_environmental_development_stage(hour, intensity, environmental_limit):
    """Determine development stage based on time and environmental context"""
    intensity_fraction = intensity / max(environmental_limit, 50)
    
    if hour <= 24:
        return 'Genesis'
    elif hour <= 72:
        if intensity_fraction < 0.3:
            return 'Early Development'
        elif intensity_fraction < 0.6:
            return 'Active Development'
        else:
            return 'Rapid Development'
    elif hour <= 120:
        if intensity_fraction > 0.8:
            return 'Peak Intensity'
        else:
            return 'Mature Stage'
    else:
        return 'Extended Forecast'

def predict_storm_route_and_intensity_realistic(genesis_region, month, oni_value, models=None, forecast_hours=72, use_advanced_physics=True):
    """Realistic prediction with proper typhoon speeds and development"""
    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 = 30  # Start at TD level (25-35 kt)
        
        # Environmental factors for genesis
        if oni_value > 1.0:  # Strong El Niño - suppressed development
            intensity_modifier = -6
        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
        seasonal_factors = {
            1: -8, 2: -6, 3: -4, 4: -2, 5: 2, 6: 6,
            7: 10, 8: 12, 9: 15, 10: 10, 11: 4, 12: -5
        }
        seasonal_modifier = seasonal_factors.get(month, 0)
        
        # Genesis region favorability
        region_factors = {
            "Western Pacific Main Development Region": 8,
            "South China Sea": 4,
            "Philippine Sea": 5,
            "Marshall Islands": 7,
            "Monsoon Trough": 6,
            "ITCZ Region": 3,
            "Subtropical Region": 2,
            "Bay of Bengal": 4,
            "Eastern Pacific": 6,
            "Atlantic MDR": 5
        }
        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))  # Keep in TD-weak TS range
        
        # Add realistic uncertainty for genesis
        intensity_uncertainty = np.random.normal(0, 2)
        predicted_intensity += intensity_uncertainty
        predicted_intensity = max(25, min(38, predicted_intensity))  # TD range
        
        results['current_prediction'] = {
            'intensity_kt': predicted_intensity,
            'pressure_hpa': 1008 - (predicted_intensity - 25) * 0.6,  # Realistic TD pressure
            'category': categorize_typhoon_enhanced(predicted_intensity),
            'genesis_region': genesis_region
        }
        
        # REALISTIC route prediction with proper typhoon speeds
        current_lat = lat
        current_lon = lon
        current_intensity = predicted_intensity
        
        route_points = []
        
        # Track storm development over time with REALISTIC SPEEDS
        for hour in range(0, forecast_hours + 6, 6):
            
            # REALISTIC typhoon motion - much faster speeds
            # Typical typhoon forward speed: 15-25 km/h (0.14-0.23°/hour)
            
            # Base forward speed depends on latitude and storm intensity
            if current_lat < 20:  # Low latitude - slower
                base_speed = 0.12  # ~13 km/h
            elif current_lat < 30:  # Mid latitude - moderate
                base_speed = 0.18  # ~20 km/h
            else:  # High latitude - faster
                base_speed = 0.25  # ~28 km/h
            
            # Intensity affects speed (stronger storms can move faster)
            intensity_speed_factor = 1.0 + (current_intensity - 50) / 200
            base_speed *= max(0.8, min(1.4, intensity_speed_factor))
            
            # Beta drift (Coriolis effect) - realistic values
            beta_drift_lat = 0.02 * np.sin(np.radians(current_lat))
            beta_drift_lon = -0.05 * np.cos(np.radians(current_lat))
            
            # Seasonal steering patterns with realistic speeds
            if month in [6, 7, 8, 9]:  # Peak season
                ridge_strength = 1.2
                ridge_position = 32 + 4 * np.sin(2 * np.pi * (month - 6) / 4)
            else:  # Off season
                ridge_strength = 0.9
                ridge_position = 28
            
            # REALISTIC motion based on position relative to subtropical ridge
            if current_lat < ridge_position - 10:  # Well south of ridge - westward movement
                lat_tendency = base_speed * 0.3 + beta_drift_lat  # Slight poleward
                lon_tendency = -base_speed * 0.9 + beta_drift_lon  # Strong westward
            elif current_lat > ridge_position - 3:  # Near ridge - recurvature
                lat_tendency = base_speed * 0.8 + beta_drift_lat  # Strong poleward
                lon_tendency = base_speed * 0.4 + beta_drift_lon  # Eastward
            else:  # In between - normal WNW motion
                lat_tendency = base_speed * 0.4 + beta_drift_lat  # Moderate poleward
                lon_tendency = -base_speed * 0.7 + beta_drift_lon  # Moderate westward
            
            # ENSO steering modulation (realistic effects)
            if oni_value > 0.5:  # El Niño - more eastward/poleward motion
                lon_tendency += 0.05
                lat_tendency += 0.02
            elif oni_value < -0.5:  # La Niña - more westward motion
                lon_tendency -= 0.08
                lat_tendency -= 0.01
            
            # Add motion uncertainty that grows with time (realistic error growth)
            motion_uncertainty = 0.02 + (hour / 120) * 0.04
            lat_noise = np.random.normal(0, motion_uncertainty)
            lon_noise = np.random.normal(0, motion_uncertainty)
            
            # Update position with realistic speeds
            current_lat += lat_tendency + lat_noise
            current_lon += lon_tendency + lon_noise
            
            # REALISTIC intensity evolution with proper development cycles
            
            # Development phase (first 48-72 hours) - realistic intensification
            if hour <= 48:
                if current_intensity < 50:  # Still weak - rapid development possible
                    if 10 <= current_lat <= 25 and 115 <= current_lon <= 165:  # Favorable environment
                        intensity_tendency = 4.5 if current_intensity < 35 else 3.0
                    elif 120 <= current_lon <= 155 and 15 <= current_lat <= 20:  # Best environment
                        intensity_tendency = 6.0 if current_intensity < 40 else 4.0
                    else:
                        intensity_tendency = 2.0
                elif current_intensity < 80:  # Moderate intensity
                    intensity_tendency = 2.5 if (120 <= current_lon <= 155 and 10 <= current_lat <= 25) else 1.0
                else:  # Already strong
                    intensity_tendency = 1.0
                    
            # Mature phase (48-120 hours) - peak intensity maintenance
            elif hour <= 120:
                if current_lat < 25 and current_lon > 120:  # Still in favorable waters
                    if current_intensity < 120:
                        intensity_tendency = 1.5
                    else:
                        intensity_tendency = 0.0  # Maintain intensity
                else:
                    intensity_tendency = -1.5
                    
            # Extended phase (120+ hours) - gradual weakening
            else:
                if current_lat < 30 and current_lon > 115:
                    intensity_tendency = -2.0  # Slow weakening
                else:
                    intensity_tendency = -3.5  # Faster weakening
            
            # Environmental modulation (realistic effects)
            if current_lat > 35:  # High latitude - rapid weakening
                intensity_tendency -= 12
            elif current_lat > 30:  # Moderate latitude
                intensity_tendency -= 5
            elif current_lon < 110:  # Land interaction
                intensity_tendency -= 15
            elif 125 <= current_lon <= 155 and 10 <= current_lat <= 25:  # Warm pool
                intensity_tendency += 2
            elif 160 <= current_lon <= 180 and 15 <= current_lat <= 30:  # Still warm
                intensity_tendency += 1
            
            # SST effects (realistic temperature impact)
            if current_lat < 8:  # Very warm but weak Coriolis
                intensity_tendency += 0.5
            elif 8 <= current_lat <= 20:  # Sweet spot for development
                intensity_tendency += 2.0
            elif 20 < current_lat <= 30:  # Marginal
                intensity_tendency -= 1.0
            elif current_lat > 30:  # Cool waters
                intensity_tendency -= 4.0
            
            # Shear effects (simplified but realistic)
            if month in [12, 1, 2, 3]:  # High shear season
                intensity_tendency -= 2.0
            elif month in [7, 8, 9]:  # Low shear season
                intensity_tendency += 1.0
            
            # Update intensity with realistic bounds and variability
            intensity_noise = np.random.normal(0, 1.5)  # Small random fluctuations
            current_intensity += intensity_tendency + intensity_noise
            current_intensity = max(20, min(185, current_intensity))  # Realistic range
            
            # Calculate confidence based on forecast time and environment
            base_confidence = 0.92
            time_penalty = (hour / 120) * 0.45
            environment_penalty = 0.15 if current_lat > 30 or current_lon < 115 else 0
            confidence = max(0.25, base_confidence - time_penalty - environment_penalty)
            
            # Determine development stage
            if hour <= 24:
                stage = 'Genesis'
            elif hour <= 72:
                stage = 'Development'
            elif hour <= 120:
                stage = 'Mature'
            elif hour <= 240:
                stage = 'Extended'
            else:
                stage = 'Long-term'
            
            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': stage,
                'forward_speed_kmh': base_speed * 111,  # Convert to km/h
                'pressure_hpa': max(900, 1013 - (current_intensity - 25) * 0.9)
            })
        
        results['route_forecast'] = route_points
        
        # Realistic confidence scores
        results['confidence_scores'] = {
            'genesis': 0.88,
            'early_development': 0.82,
            'position_24h': 0.85,
            'position_48h': 0.78,
            'position_72h': 0.68,
            'intensity_24h': 0.75,
            'intensity_48h': 0.65,
            'intensity_72h': 0.55,
            'long_term': max(0.3, 0.8 - (forecast_hours / 240) * 0.5)
        }
        
        # Model information
        results['model_info'] = f"Enhanced Realistic 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'
        }

# Update the existing predict_storm_route_and_intensity_realistic function to use oceanic data
def predict_storm_route_and_intensity_realistic_enhanced(
    genesis_region, month, oni_value, models=None, 
    forecast_hours=72, use_advanced_physics=True
):
    """Enhanced wrapper that uses oceanic data when available"""
    return predict_storm_route_and_intensity_with_oceanic_data(
        genesis_region, month, oni_value, forecast_hours, 
        use_real_data=True, models=models, enable_animation=True
    )

# Initialize data
initialize_data()

# Create and launch the interface
demo = create_interface()

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