import gradio as gr
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
import pickle
import tropycal.tracks as tracks
import pandas as pd
import numpy as np
import cachetools
import functools
import hashlib
import os
from datetime import datetime, timedelta
from datetime import date
from scipy import stats
from scipy.optimize import minimize, curve_fit
from sklearn.linear_model import LinearRegression
from sklearn.cluster import KMeans
from scipy.interpolate import interp1d
from fractions import Fraction
import statsmodels.api as sm
import time
import threading
import requests
from io import StringIO   
import tempfile
import csv  
from collections import defaultdict
import shutil
import filecmp
import warnings
warnings.filterwarnings('ignore')

# Constants
DATA_PATH = os.getcwd()
ONI_DATA_PATH = os.path.join(DATA_PATH, 'oni_data.csv')
TYPHOON_DATA_PATH = os.path.join(DATA_PATH, 'processed_typhoon_data.csv')
LOCAL_iBtrace_PATH = os.path.join(DATA_PATH, 'ibtracs.WP.list.v04r00.csv')
iBtrace_uri = 'https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r00/access/csv/ibtracs.WP.list.v04r00.csv'
CACHE_FILE = 'ibtracs_cache.pkl'
CACHE_EXPIRY_DAYS = 1

# Color mappings
COLOR_MAP = {
    'C5 Super Typhoon': 'rgb(255, 0, 0)',
    'C4 Very Strong Typhoon': 'rgb(255, 63, 0)', 
    'C3 Strong Typhoon': 'rgb(255, 127, 0)',
    'C2 Typhoon': 'rgb(255, 191, 0)',
    'C1 Typhoon': 'rgb(255, 255, 0)',
    'Tropical Storm': 'rgb(0, 255, 255)',
    'Tropical Depression': 'rgb(173, 216, 230)'
}

class TyphoonAnalyzer:
    def __init__(self):
        self.last_oni_update = None
        self.ensure_data_files_exist()
        self.load_initial_data()
        
    def ensure_data_files_exist(self):
        """Ensure all required data files exist before loading"""
        print("Checking and downloading required data files...")
        
        # Create data directory if it doesn't exist
        os.makedirs(DATA_PATH, exist_ok=True)
        
        # Download ONI data if it doesn't exist
        if not os.path.exists(ONI_DATA_PATH):
            print("Downloading ONI data...")
            url = "https://www.cpc.ncep.noaa.gov/data/indices/oni.ascii.txt"
            temp_file = os.path.join(DATA_PATH, "temp_oni.ascii.txt")
            try:
                response = requests.get(url)
                response.raise_for_status()
                with open(temp_file, 'wb') as f:
                    f.write(response.content)
                self.convert_oni_ascii_to_csv(temp_file, ONI_DATA_PATH)
                print("ONI data downloaded and converted successfully")
            except Exception as e:
                print(f"Error downloading ONI data: {e}")
                raise
            finally:
                if os.path.exists(temp_file):
                    os.remove(temp_file)

        # Download IBTrACS data if it doesn't exist
        if not os.path.exists(LOCAL_iBtrace_PATH):
            print("Downloading IBTrACS data...")
            try:
                response = requests.get(iBtrace_uri)
                response.raise_for_status()
                with open(LOCAL_iBtrace_PATH, 'w') as f:
                    f.write(response.text)
                print("IBTrACS data downloaded successfully")
            except Exception as e:
                print(f"Error downloading IBTrACS data: {e}")
                raise

        # Create processed typhoon data if it doesn't exist
        if not os.path.exists(TYPHOON_DATA_PATH):
            print("Processing typhoon data...")
            try:
                self.convert_typhoondata(LOCAL_iBtrace_PATH, TYPHOON_DATA_PATH)
                print("Typhoon data processed successfully")
            except Exception as e:
                print(f"Error processing typhoon data: {e}")
                raise

        print("All required data files are ready")

    def load_initial_data(self):
        """Initialize all required data"""
        print("Loading initial data...")
        self.update_oni_data()
        self.oni_df = self.fetch_oni_data_from_csv()
        self.ibtracs = self.load_ibtracs_data()
        self.update_typhoon_data()
        self.oni_data, self.typhoon_data = self.load_data()
        self.oni_long = self.process_oni_data(self.oni_data)
        self.typhoon_max = self.process_typhoon_data(self.typhoon_data)
        self.merged_data = self.merge_data()
        print("Initial data loading complete")

    def convert_typhoondata(self, input_file, output_file):
        """Convert IBTrACS data to processed format"""
        print(f"Converting typhoon data from {input_file} to {output_file}")
        with open(input_file, 'r') as infile:
            # Skip the header lines
            next(infile)
            next(infile)
            
            reader = csv.reader(infile)
            sid_data = defaultdict(list)
            
            for row in reader:
                if not row:  # Skip blank lines
                    continue
                
                sid = row[0]
                iso_time = row[6]
                sid_data[sid].append((row, iso_time))

        with open(output_file, 'w', newline='') as outfile:
            fieldnames = ['SID', 'ISO_TIME', 'LAT', 'LON', 'SEASON', 'NAME', 
                         'WMO_WIND', 'WMO_PRES', 'USA_WIND', 'USA_PRES', 
                         'START_DATE', 'END_DATE']
            writer = csv.DictWriter(outfile, fieldnames=fieldnames)
            writer.writeheader()
            
            for sid, data in sid_data.items():
                start_date = min(data, key=lambda x: x[1])[1]
                end_date = max(data, key=lambda x: x[1])[1]
                
                for row, iso_time in data:
                    writer.writerow({
                        'SID': row[0],
                        'ISO_TIME': iso_time,
                        'LAT': row[8],
                        'LON': row[9],
                        'SEASON': row[1],
                        'NAME': row[5],
                        'WMO_WIND': row[10].strip() or ' ',
                        'WMO_PRES': row[11].strip() or ' ',
                        'USA_WIND': row[23].strip() or ' ',
                        'USA_PRES': row[24].strip() or ' ',
                        'START_DATE': start_date,
                        'END_DATE': end_date
                    })

    def fetch_oni_data_from_csv(self):
        """Load ONI data from CSV"""
        df = pd.read_csv(ONI_DATA_PATH)
        df = df.melt(id_vars=['Year'], var_name='Month', value_name='ONI')
        
        # Convert month numbers to month names
        month_map = {
            '01': 'Jan', '02': 'Feb', '03': 'Mar', '04': 'Apr',
            '05': 'May', '06': 'Jun', '07': 'Jul', '08': 'Aug',
            '09': 'Sep', '10': 'Oct', '11': 'Nov', '12': 'Dec'
        }
        df['Month'] = df['Month'].map(month_map)
        
        # Now create the date
        df['Date'] = pd.to_datetime(df['Year'].astype(str) + df['Month'], format='%Y%b')
        return df.set_index('Date')

    def update_oni_data(self):
        """Update ONI data from NOAA"""
        if not self._should_update_oni():
            return
        
        url = "https://www.cpc.ncep.noaa.gov/data/indices/oni.ascii.txt"
        with tempfile.NamedTemporaryFile(delete=False) as temp_file:
            try:
                response = requests.get(url)
                response.raise_for_status()
                temp_file.write(response.content)
                self.convert_oni_ascii_to_csv(temp_file.name, ONI_DATA_PATH)
                self.last_oni_update = date.today()
            except Exception as e:
                print(f"Error updating ONI data: {e}")
            finally:
                if os.path.exists(temp_file.name):
                    os.remove(temp_file.name)

    def _should_update_oni(self):
        """Check if ONI data should be updated"""
        today = datetime.now()
        return (today.day in [1, 15] or 
                today.day == (today.replace(day=1, month=today.month%12+1) - timedelta(days=1)).day)

    def convert_oni_ascii_to_csv(self, input_file, output_file):
        """Convert ONI ASCII data to CSV format"""
        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
        }
        
        with open(input_file, 'r') as f:
            next(f)  # Skip header
            for line in f:
                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

        with open(output_file, 'w', newline='') as f:
            writer = csv.writer(f)
            writer.writerow(['Year'] + [f"{m:02d}" for m in range(1, 13)])
            for year in sorted(data.keys()):
                writer.writerow([year] + data[year])

    def load_ibtracs_data(self):
        """Load IBTrACS data with caching"""
        if os.path.exists(CACHE_FILE):
            cache_time = datetime.fromtimestamp(os.path.getmtime(CACHE_FILE))
            if datetime.now() - cache_time < timedelta(days=CACHE_EXPIRY_DAYS):
                with open(CACHE_FILE, 'rb') as f:
                    return pickle.load(f)

        if os.path.exists(LOCAL_iBtrace_PATH):
            ibtracs = tracks.TrackDataset(basin='west_pacific', source='ibtracs', 
                                         ibtracs_url=LOCAL_iBtrace_PATH)
        else:
            response = requests.get(iBtrace_uri)
            response.raise_for_status()
            with open(LOCAL_iBtrace_PATH, 'w') as f:
                f.write(response.text)
            ibtracs = tracks.TrackDataset(basin='west_pacific', source='ibtracs',
                                         ibtracs_url=LOCAL_iBtrace_PATH)

        with open(CACHE_FILE, 'wb') as f:
            pickle.dump(ibtracs, f)
        return ibtracs

    def update_typhoon_data(self):
        """Update typhoon data from IBTrACS"""
        try:
            response = requests.head(iBtrace_uri)
            remote_modified = datetime.strptime(response.headers['Last-Modified'], 
                                               '%a, %d %b %Y %H:%M:%S GMT')
            local_modified = (datetime.fromtimestamp(os.path.getmtime(LOCAL_iBtrace_PATH)) 
                            if os.path.exists(LOCAL_iBtrace_PATH) else datetime.min)
            
            if remote_modified > local_modified:
                response = requests.get(iBtrace_uri)
                response.raise_for_status()
                with open(LOCAL_iBtrace_PATH, 'w') as f:
                    f.write(response.text)
                print("Typhoon data updated successfully")
        except Exception as e:
            print(f"Error updating typhoon data: {e}")

    def load_data(self):
        """Load ONI and typhoon data"""
        oni_data = pd.read_csv(ONI_DATA_PATH)
        typhoon_data = pd.read_csv(TYPHOON_DATA_PATH, low_memory=False)
        typhoon_data['ISO_TIME'] = pd.to_datetime(typhoon_data['ISO_TIME'])
        return oni_data, typhoon_data

    def process_oni_data(self, oni_data):
        """Process ONI data"""
        oni_long = oni_data.melt(id_vars=['Year'], var_name='Month', value_name='ONI')
        
        # Create a mapping for month numbers
        month_map = {
            '01': 1, '02': 2, '03': 3, '04': 4,
            '05': 5, '06': 6, '07': 7, '08': 8,
            '09': 9, '10': 10, '11': 11, '12': 12
        }
        
        # Convert month strings to numbers directly
        oni_long['Month'] = oni_long['Month'].map(month_map)
        
        return oni_long

    def process_typhoon_data(self, typhoon_data):
        """Process typhoon data"""
        typhoon_data['USA_WIND'] = pd.to_numeric(typhoon_data['USA_WIND'], errors='coerce')
        typhoon_data['WMO_PRES'] = pd.to_numeric(typhoon_data['WMO_PRES'], errors='coerce')
        typhoon_data['ISO_TIME'] = pd.to_datetime(typhoon_data['ISO_TIME'])
        typhoon_data['Year'] = typhoon_data['ISO_TIME'].dt.year
        typhoon_data['Month'] = typhoon_data['ISO_TIME'].dt.month
        
        typhoon_max = typhoon_data.groupby(['SID', 'Year', 'Month']).agg({
            'USA_WIND': 'max',
            'WMO_PRES': 'min',
            'NAME': 'first',
            'LAT': 'first',
            'LON': 'first',
            'ISO_TIME': 'first'
        }).reset_index()
        
        typhoon_max['Category'] = typhoon_max['USA_WIND'].apply(self.categorize_typhoon)
        return typhoon_max

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

    def categorize_typhoon(self, wind_speed):
        """Categorize typhoon based on wind speed"""
        if np.isnan(wind_speed):
            return 'Unknown'
        if wind_speed >= 137:
            return 'C5 Super Typhoon'
        elif wind_speed >= 113:
            return 'C4 Very Strong Typhoon'
        elif wind_speed >= 96:
            return 'C3 Strong Typhoon'
        elif wind_speed >= 83:
            return 'C2 Typhoon'
        elif wind_speed >= 64:
            return 'C1 Typhoon'
        elif wind_speed >= 34:
            return 'Tropical Storm'
        else:
            return 'Tropical Depression'

    def analyze_typhoon(self, start_year, start_month, end_year, end_month, enso_value='all'):
        """Main analysis function"""
        start_date = datetime(start_year, start_month, 1)
        end_date = datetime(end_year, end_month, 28)
        
        filtered_data = self.merged_data[
            (self.merged_data['ISO_TIME'] >= start_date) & 
            (self.merged_data['ISO_TIME'] <= end_date)
        ]
        
        if enso_value != 'all':
            filtered_data = filtered_data[
                (filtered_data['ONI'] >= 0.5 if enso_value == 'el_nino' else
                 filtered_data['ONI'] <= -0.5 if enso_value == 'la_nina' else
                 (filtered_data['ONI'] > -0.5) & (filtered_data['ONI'] < 0.5))
            ]
        
        return {
            'tracks': self.create_tracks_plot(filtered_data),
            'wind': self.create_wind_analysis(filtered_data),
            'pressure': self.create_pressure_analysis(filtered_data),
            'stats': self.generate_statistics(filtered_data)
        }

    def create_tracks_plot(self, data):
        """Create typhoon tracks visualization"""
        fig = go.Figure()
        
        fig.update_layout(
            title={
                'text': 'Typhoon Tracks',
                'y':0.95,
                'x':0.5,
                'xanchor': 'center',
                'yanchor': 'top'
            },
            showlegend=True,
            legend=dict(
                yanchor="top",
                y=0.99,
                xanchor="left",
                x=0.01,
                bgcolor='rgba(255, 255, 255, 0.8)'
            ),
            geo=dict(
                projection_type='mercator',
                showland=True,
                showcoastlines=True,
                landcolor='rgb(243, 243, 243)',
                countrycolor='rgb(204, 204, 204)',
                coastlinecolor='rgb(214, 214, 214)',
                showocean=True,
                oceancolor='rgb(230, 250, 255)',
                showlakes=True,
                lakecolor='rgb(230, 250, 255)',
                lataxis=dict(range=[0, 50]),
                lonaxis=dict(range=[100, 180]),
                center=dict(lat=20, lon=140),
                bgcolor='rgba(255, 255, 255, 0.5)'
            ),
            paper_bgcolor='rgba(255, 255, 255, 0.5)',
            plot_bgcolor='rgba(255, 255, 255, 0.5)'
        )

        for category in COLOR_MAP.keys():
            category_data = data[data['Category'] == category]
            for _, storm in category_data.groupby('SID'):
                track_data = self.typhoon_data[self.typhoon_data['SID'] == storm['SID'].iloc[0]]
                track_data = track_data.sort_values('ISO_TIME')
                
                fig.add_trace(go.Scattergeo(
                    lon=track_data['LON'],
                    lat=track_data['LAT'],
                    mode='lines',
                    line=dict(
                        width=2,
                        color=COLOR_MAP[category]
                    ),
                    name=category,
                    legendgroup=category,
                    showlegend=True if storm.iloc[0]['SID'] == category_data.iloc[0]['SID'] else False,
                    hovertemplate=(
                        f"Name: {storm['NAME'].iloc[0]}<br>" +
                        f"Category: {category}<br>" +
                        f"Wind Speed: {storm['USA_WIND'].iloc[0]:.1f} kt<br>" +
                        f"Pressure: {storm['WMO_PRES'].iloc[0]:.1f} hPa<br>" +
                        f"Date: {track_data['ISO_TIME'].dt.strftime('%Y-%m-%d %H:%M').iloc[0]}<br>" +
                        f"Lat: {track_data['LAT'].iloc[0]:.2f}°N<br>" +
                        f"Lon: {track_data['LON'].iloc[0]:.2f}°E<br>" +
                        "<extra></extra>"
                    )
                ))

        return fig

    def create_wind_analysis(self, data):
        """Create wind speed analysis plot"""
        fig = px.scatter(data,
            x='ONI',
            y='USA_WIND',
            color='Category',
            color_discrete_map=COLOR_MAP,
            title='Wind Speed vs ONI Index',
            labels={
                'ONI': 'Oceanic Niño Index',
                'USA_WIND': 'Maximum Wind Speed (kt)'
            },
            hover_data=['NAME', 'ISO_TIME', 'Category']
        )
        
        # Add regression line
        x = data['ONI']
        y = data['USA_WIND']
        slope, intercept = np.polyfit(x, y, 1)
        fig.add_trace(
            go.Scatter(
                x=x,
                y=slope * x + intercept,
                mode='lines',
                name=f'Regression (slope={slope:.2f})',
                line=dict(color='black', dash='dash')
            )
        )
        
        return fig

    def create_pressure_analysis(self, data):
        """Create pressure analysis plot"""
        fig = px.scatter(data,
            x='ONI',
            y='WMO_PRES',
            color='Category',
            color_discrete_map=COLOR_MAP,
            title='Pressure vs ONI Index',
            labels={
                'ONI': 'Oceanic Niño Index',
                'WMO_PRES': 'Minimum Pressure (hPa)'
            },
            hover_data=['NAME', 'ISO_TIME', 'Category']
        )
        
        # Add regression line
        x = data['ONI']
        y = data['WMO_PRES']
        slope, intercept = np.polyfit(x, y, 1)
        fig.add_trace(
            go.Scatter(
                x=x,
                y=slope * x + intercept,
                mode='lines',
                name=f'Regression (slope={slope:.2f})',
                line=dict(color='black', dash='dash')
            )
        )
        
        return fig

    def generate_statistics(self, data):
        """Generate statistical summary"""
        stats = {
            'total_typhoons': len(data['SID'].unique()),
            'avg_wind': data['USA_WIND'].mean(),
            'max_wind': data['USA_WIND'].max(),
            'avg_pressure': data['WMO_PRES'].mean(),
            'min_pressure': data['WMO_PRES'].min(),
            'oni_correlation_wind': data['ONI'].corr(data['USA_WIND']),
            'oni_correlation_pressure': data['ONI'].corr(data['WMO_PRES']),
            'category_counts': data['Category'].value_counts().to_dict()
        }
        
        return f"""
### Statistical Summary

- Total Typhoons: {stats['total_typhoons']}
- Average Wind Speed: {stats['avg_wind']:.2f} kt
- Maximum Wind Speed: {stats['max_wind']:.2f} kt
- Average Pressure: {stats['avg_pressure']:.2f} hPa
- Minimum Pressure: {stats['min_pressure']:.2f} hPa
- ONI-Wind Speed Correlation: {stats['oni_correlation_wind']:.3f}
- ONI-Pressure Correlation: {stats['oni_correlation_pressure']:.3f}

### Category Distribution
{chr(10).join(f'- {cat}: {count}' for cat, count in stats['category_counts'].items())}
"""

    def analyze_clusters(self, year, n_clusters):
        """Analyze typhoon clusters for a specific year"""
        year_data = self.typhoon_data[self.typhoon_data['SEASON'] == year]
        if year_data.empty:
            return go.Figure(), "No data available for selected year"

        # Prepare data for clustering
        routes = []
        for _, storm in year_data.groupby('SID'):
            if len(storm) > 1:
                # Standardize route length
                t = np.linspace(0, 1, len(storm))
                t_new = np.linspace(0, 1, 100)
                lon_interp = interp1d(t, storm['LON'], kind='linear')(t_new)
                lat_interp = interp1d(t, storm['LAT'], kind='linear')(t_new)
                routes.append(np.column_stack((lon_interp, lat_interp)))
        
        if len(routes) < n_clusters:
            return go.Figure(), f"Not enough typhoons ({len(routes)}) for {n_clusters} clusters"

        # Perform clustering
        routes_array = np.array(routes)
        routes_reshaped = routes_array.reshape(routes_array.shape[0], -1)
        kmeans = KMeans(n_clusters=n_clusters, random_state=42)
        clusters = kmeans.fit_predict(routes_reshaped)
        
        # Create visualization
        fig = go.Figure()
        
        # Set layout
        fig.update_layout(
            title=f'Typhoon Route Clusters ({year})',
            showlegend=True,
            geo=dict(
                projection_type='mercator',
                showland=True,
                showcoastlines=True,
                landcolor='rgb(243, 243, 243)',
                countrycolor='rgb(204, 204, 204)',
                coastlinecolor='rgb(214, 214, 214)',
                showocean=True,
                oceancolor='rgb(230, 250, 255)',
                lataxis=dict(range=[0, 50]),
                lonaxis=dict(range=[100, 180]),
                center=dict(lat=20, lon=140)
            )
        )
        
        # Plot routes colored by cluster
        for route, cluster_id in zip(routes, clusters):
            fig.add_trace(go.Scattergeo(
                lon=route[:, 0],
                lat=route[:, 1],
                mode='lines',
                line=dict(
                    width=1, 
                    color=f'hsl({cluster_id * 360/n_clusters}, 50%, 50%)'
                ),
                name=f'Cluster {cluster_id + 1}',
                showlegend=False
            ))
        
        # Plot cluster centers
        for i in range(n_clusters):
            center = kmeans.cluster_centers_[i].reshape(-1, 2)
            fig.add_trace(go.Scattergeo(
                lon=center[:, 0],
                lat=center[:, 1],
                mode='lines',
                name=f'Cluster {i+1} Center',
                line=dict(
                    width=3,
                    color=f'hsl({i * 360/n_clusters}, 100%, 50%)'
                )
            ))
        
        # Generate statistics text
        stats_text = "### Clustering Results\n\n"
        cluster_counts = np.bincount(clusters)
        for i in range(n_clusters):
            stats_text += f"- Cluster {i+1}: {cluster_counts[i]} typhoons\n"
        
        return fig, stats_text

    def get_typhoons_for_year(self, year):
        """Get list of typhoons for a specific year"""
        try:
            season = self.ibtracs.get_season(year)
            storm_summary = season.summary()
            
            typhoon_options = []
            for i in range(storm_summary['season_storms']):
                storm_id = storm_summary['id'][i]
                storm_name = storm_summary['name'][i]
                typhoon_options.append({'label': f"{storm_name} ({storm_id})", 'value': storm_id})
            
            return typhoon_options
        except Exception as e:
            print(f"Error getting typhoons for year {year}: {str(e)}")
            return []

    def create_typhoon_animation(self, year, storm_id, standard='atlantic'):
        """Create animated visualization of typhoon path"""
        if not storm_id:
            return go.Figure(), "Please select a typhoon"
            
        storm = self.ibtracs.get_storm(storm_id)
        
        fig = go.Figure()

        # Base map setup with correct scaling
        fig.update_layout(
            title=f"{year} - {storm.name} Typhoon Path",
            geo=dict(
                projection_type='natural earth',
                showland=True,
                landcolor='rgb(243, 243, 243)',
                countrycolor='rgb(204, 204, 204)',
                coastlinecolor='rgb(100, 100, 100)',
                showocean=True,
                oceancolor='rgb(230, 250, 255)',
                lataxis=dict(range=[0, 50]),
                lonaxis=dict(range=[100, 180]),
                center=dict(lat=20, lon=140),
            ),
            updatemenus=[{
                "buttons": [
                    {
                        "args": [None, {"frame": {"duration": 100, "redraw": True},
                                        "fromcurrent": True,
                                        "transition": {"duration": 0}}],
                        "label": "Play",
                        "method": "animate"
                    },
                    {
                        "args": [[None], {"frame": {"duration": 0, "redraw": True},
                                          "mode": "immediate",
                                          "transition": {"duration": 0}}],
                        "label": "Pause",
                        "method": "animate"
                    }
                ],
                "direction": "left",
                "pad": {"r": 10, "t": 87},
                "showactive": False,
                "type": "buttons",
                "x": 0.1,
                "xanchor": "right",
                "y": 0,
                "yanchor": "top"
            }]
        )

        # Create animation frames
        frames = []
        for i in range(len(storm.time)):
            category, color = self.categorize_typhoon_by_standard(storm.vmax[i], standard)
            
            # Get extra radius data if available
            radius_info = ""
            if hasattr(storm, 'dict'):
                r34_ne = storm.dict.get('USA_R34_NE', [None])[i] if 'USA_R34_NE' in storm.dict else None
                r34_se = storm.dict.get('USA_R34_SE', [None])[i] if 'USA_R34_SE' in storm.dict else None
                r34_sw = storm.dict.get('USA_R34_SW', [None])[i] if 'USA_R34_SW' in storm.dict else None
                r34_nw = storm.dict.get('USA_R34_NW', [None])[i] if 'USA_R34_NW' in storm.dict else None
                rmw = storm.dict.get('USA_RMW', [None])[i] if 'USA_RMW' in storm.dict else None
                eye = storm.dict.get('USA_EYE', [None])[i] if 'USA_EYE' in storm.dict else None
                
                if any([r34_ne, r34_se, r34_sw, r34_nw, rmw, eye]):
                    radius_info = f"<br>R34: NE={r34_ne}, SE={r34_se}, SW={r34_sw}, NW={r34_nw}<br>"
                    radius_info += f"RMW: {rmw}<br>Eye Diameter: {eye}"
            
            frame = go.Frame(
                data=[
                    go.Scattergeo(
                        lon=storm.lon[:i+1],
                        lat=storm.lat[:i+1],
                        mode='lines',
                        line=dict(width=2, color='blue'),
                        name='Path Traveled',
                        showlegend=False,
                    ),
                    go.Scattergeo(
                        lon=[storm.lon[i]],
                        lat=[storm.lat[i]],
                        mode='markers+text',
                        marker=dict(size=10, color=color, symbol='star'),
                        text=category,
                        textposition="top center",
                        textfont=dict(size=12, color=color),
                        name='Current Location',
                        hovertemplate=(
                            f"{storm.time[i].strftime('%Y-%m-%d %H:%M')}<br>"
                            f"Category: {category}<br>"
                            f"Wind Speed: {storm.vmax[i]:.1f} kt<br>"
                            f"{radius_info}"
                        ),
                    ),
                ],name=f"frame{i}"
            )
            frames.append(frame)

        fig.frames = frames
        
        # Add initial track and starting point
        fig.add_trace(
            go.Scattergeo(
                lon=storm.lon,
                lat=storm.lat,
                mode='lines',
                line=dict(width=2, color='gray'),
                name='Complete Path',
                showlegend=True,
            )
        )

        fig.add_trace(
            go.Scattergeo(
                lon=[storm.lon[0]],
                lat=[storm.lat[0]],
                mode='markers',
                marker=dict(size=10, color='green', symbol='star'),
                name='Starting Point',
                text=storm.time[0].strftime('%Y-%m-%d %H:%M'),
                hoverinfo='text+name',
            )
        )

        # Add slider for frame selection
        sliders = [{
            "active": 0,
            "yanchor": "top",
            "xanchor": "left",
            "currentvalue": {
                "font": {"size": 20},
                "prefix": "Time: ",
                "visible": True,
                "xanchor": "right"
            },
            "transition": {"duration": 100, "easing": "cubic-in-out"},
            "pad": {"b": 10, "t": 50},
            "len": 0.9,
            "x": 0.1,
            "y": 0,
            "steps": [
                {
                    "args": [[f"frame{k}"],
                            {"frame": {"duration": 100, "redraw": True},
                            "mode": "immediate",
                            "transition": {"duration": 0}}
                            ],
                    "label": storm.time[k].strftime('%Y-%m-%d %H:%M'),
                    "method": "animate"
                }
                for k in range(len(storm.time))
            ]
        }]
        
        fig.update_layout(sliders=sliders)

        info_text = f"""
        ### Typhoon Information
        - **Name:** {storm.name}
        - **Start Date:** {storm.time[0].strftime('%Y-%m-%d %H:%M')}
        - **End Date:** {storm.time[-1].strftime('%Y-%m-%d %H:%M')}
        - **Duration:** {(storm.time[-1] - storm.time[0]).total_seconds() / 3600:.1f} hours
        - **Maximum Wind Speed:** {max(storm.vmax):.1f} kt
        - **Minimum Pressure:** {min(storm.mslp):.1f} hPa
        - **Peak Category:** {self.categorize_typhoon_by_standard(max(storm.vmax), standard)[0]}
        """

        return fig, info_text

    def search_typhoons(self, query):
        """Search for typhoons by name"""
        if not query:
            return go.Figure(), "Please enter a typhoon name to search"
        
        # Find all typhoons matching the query
        matching_storms = []
        
        # Limit search to last 30 years to improve performance
        current_year = datetime.now().year
        start_year = current_year - 30
        
        for year in range(start_year, current_year + 1):
            try:
                season = self.ibtracs.get_season(year)
                for storm_id in season.summary()['id']:
                    storm = self.ibtracs.get_storm(storm_id)
                    if query.lower() in storm.name.lower():
                        matching_storms.append((year, storm))
            except Exception as e:
                print(f"Error searching year {year}: {str(e)}")
                continue
        
        if not matching_storms:
            return go.Figure(), "No typhoons found matching your search"
        
        # Create visualization of all matching typhoons
        fig = go.Figure()
        
        fig.update_layout(
            title=f"Typhoons Matching: '{query}'",
            geo=dict(
                projection_type='natural earth',
                showland=True,
                landcolor='rgb(243, 243, 243)',
                countrycolor='rgb(204, 204, 204)',
                coastlinecolor='rgb(100, 100, 100)',
                showocean=True,
                oceancolor='rgb(230, 250, 255)',
                lataxis=dict(range=[0, 50]),
                lonaxis=dict(range=[100, 180]),
                center=dict(lat=20, lon=140),
            )
        )
        
        # Plot each matching storm with a different color
        colors = px.colors.qualitative.Plotly
        
        for i, (year, storm) in enumerate(matching_storms):
            color = colors[i % len(colors)]
            
            fig.add_trace(go.Scattergeo(
                lon=storm.lon,
                lat=storm.lat,
                mode='lines',
                line=dict(width=3, color=color),
                name=f"{storm.name} ({year})",
                hovertemplate=(
                    f"Name: {storm.name}<br>"
                    f"Year: {year}<br>"
                    f"Max Wind: {max(storm.vmax):.1f} kt<br>"
                    f"Min Pressure: {min(storm.mslp):.1f} hPa<br>"
                    f"Position: %{lat:.2f}°N, %{lon:.2f}°E"
                )
            ))
        
        # Add starting points
        for i, (year, storm) in enumerate(matching_storms):
            color = colors[i % len(colors)]
            
            fig.add_trace(go.Scattergeo(
                lon=[storm.lon[0]],
                lat=[storm.lat[0]],
                mode='markers',
                marker=dict(size=10, color=color, symbol='circle'),
                name=f"Start: {storm.name} ({year})",
                showlegend=False,
                hoverinfo='name'
            ))
        
        # Create information text
        info_text = f"### Found {len(matching_storms)} typhoons matching '{query}':\n\n"
        
        for year, storm in matching_storms:
            info_text += f"- **{storm.name} ({year})**\n"
            info_text += f"  - Time: {storm.time[0].strftime('%Y-%m-%d')} to {storm.time[-1].strftime('%Y-%m-%d')}\n"
            info_text += f"  - Max Wind: {max(storm.vmax):.1f} kt\n"
            info_text += f"  - Min Pressure: {min(storm.mslp):.1f} hPa\n"
            info_text += f"  - Category: {self.categorize_typhoon_by_standard(max(storm.vmax))[0]}\n\n"
        
        return fig, info_text

    def categorize_typhoon_by_standard(self, wind_speed, standard='atlantic'):
        """
        Categorize typhoon based on wind speed and chosen standard
        wind_speed is in knots
        """
        if standard == 'taiwan':
            # Convert knots to m/s for Taiwan standard
            wind_speed_ms = wind_speed * 0.514444
            
            if wind_speed_ms >= 51.0:
                return 'Strong Typhoon', 'rgb(255, 0, 0)'
            elif wind_speed_ms >= 33.7:
                return 'Medium Typhoon', 'rgb(255, 127, 0)'
            elif wind_speed_ms >= 17.2:
                return 'Mild Typhoon', 'rgb(255, 255, 0)'
            else:
                return 'Tropical Depression', 'rgb(173, 216, 230)'
        else:
            # Atlantic standard uses knots
            if wind_speed >= 137:
                return 'C5 Super Typhoon', 'rgb(255, 0, 0)'
            elif wind_speed >= 113:
                return 'C4 Very Strong Typhoon', 'rgb(255, 63, 0)'
            elif wind_speed >= 96:
                return 'C3 Strong Typhoon', 'rgb(255, 127, 0)'
            elif wind_speed >= 83:
                return 'C2 Typhoon', 'rgb(255, 191, 0)'
            elif wind_speed >= 64:
                return 'C1 Typhoon', 'rgb(255, 255, 0)'
            elif wind_speed >= 34:
                return 'Tropical Storm', 'rgb(0, 255, 255)'
            else:
                return 'Tropical Depression', 'rgb(173, 216, 230)'

def create_interface():
    analyzer = TyphoonAnalyzer()

    with gr.Blocks(title="Typhoon Analysis Dashboard", theme=gr.themes.Base()) as demo:
        gr.Markdown("# Typhoon Analysis Dashboard")
        
        with gr.Tabs():
            # Main Analysis Tab
            with gr.Tab("Main Analysis"):
                with gr.Row():
                    with gr.Column():
                        start_year = gr.Slider(1900, 2024, 2000, label="Start Year")
                        start_month = gr.Slider(1, 12, 1, label="Start Month")
                    with gr.Column():
                        end_year = gr.Slider(1900, 2024, 2024, label="End Year")
                        end_month = gr.Slider(1, 12, 12, label="End Month")
                
                enso_dropdown = gr.Dropdown(
                    choices=["all", "el_nino", "la_nina", "neutral"],
                    value="all",
                    label="ENSO Phase"
                )
                
                analyze_btn = gr.Button("Analyze")
                
                tracks_plot = gr.Plot(label="Typhoon Tracks")
                
                with gr.Row():
                    wind_plot = gr.Plot(label="Wind Speed Analysis")
                    pressure_plot = gr.Plot(label="Pressure Analysis")
                
                stats_text = gr.Markdown()

            # Typhoon Animation Tab
            with gr.Tab("Typhoon Animation"):
                with gr.Row():
                    animation_year = gr.Slider(
                        minimum=1950, 
                        maximum=2024, 
                        value=2020,
                        step=1,
                        label="Select Year"
                    )
                
                with gr.Row():
                    animation_typhoon = gr.Dropdown(
                        choices=[],
                        label="Select Typhoon",
                        interactive=True
                    )
                    
                    standard_dropdown = gr.Dropdown(
                        choices=[
                            {"label": "Atlantic Standard", "value": "atlantic"},
                            {"label": "Taiwan Standard", "value": "taiwan"}
                        ],
                        value="atlantic",
                        label="Classification Standard"
                    )
                
                animation_btn = gr.Button("Show Typhoon Path", variant="primary")
                animation_plot = gr.Plot(label="Typhoon Path Animation")
                animation_info = gr.Markdown()

            # Search Tab
            with gr.Tab("Typhoon Search"):
                with gr.Row():
                    search_input = gr.Textbox(label="Search Typhoon Name")
                    search_btn = gr.Button("Search Typhoons", variant="primary")
                
                search_results = gr.Plot(label="Search Results")
                search_info = gr.Markdown()

        # Event handlers
        def analyze_callback(start_y, start_m, end_y, end_m, enso):
            results = analyzer.analyze_typhoon(start_y, start_m, end_y, end_m, enso)
            return [
                results['tracks'],
                results['wind'],
                results['pressure'],
                results['stats']
            ]

        def update_typhoon_choices(year):
            typhoons = analyzer.get_typhoons_for_year(year)
            return gr.update(choices=typhoons, value=None)

        # Connect events for main analysis
        analyze_btn.click(
            analyze_callback,
            inputs=[start_year, start_month, end_year, end_month, enso_dropdown],
            outputs=[tracks_plot, wind_plot, pressure_plot, stats_text]
        )

        # Connect events for Animation tab
        animation_year.change(
            update_typhoon_choices,
            inputs=[animation_year],
            outputs=[animation_typhoon]
        )

        animation_btn.click(
            analyzer.create_typhoon_animation,
            inputs=[animation_year, animation_typhoon, standard_dropdown],
            outputs=[animation_plot, animation_info]
        )

        # Connect events for Search tab
        search_btn.click(
            analyzer.search_typhoons,
            inputs=[search_input],
            outputs=[search_results, search_info]
        )

    return demo

if __name__ == "__main__":
    demo = create_interface()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=True
    )