import os
import pandas as pd
import numpy as np

import dash
from dash import Dash, html, dcc, Input, Output, State, dash_table
import dash_bootstrap_components as dbc
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from sklearn.tree import DecisionTreeClassifier, export_text, plot_tree
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
import matplotlib.pyplot as plt
import io
import base64
import warnings
warnings.filterwarnings('ignore')

DATA_PATH = os.environ.get(
    "GLDAS_CSV",
    "ecuador_gldas_enriquecido_solo_tierra.csv"
)

df = pd.read_csv(DATA_PATH, encoding="utf-8-sig")
df["time"] = pd.to_datetime(df["time"])
df["year"] = df["time"].dt.year
df["month"] = df["time"].dt.month

# ESTACIONES CORREGIDAS PARA ECUADOR (solo 2 estaciones)
# Verano seco: junio-noviembre, Invierno lluvioso: diciembre-mayo
df["season"] = df["month"].map({
    12: "Invierno", 1: "Invierno", 2: "Invierno", 3: "Invierno", 4: "Invierno", 5: "Invierno",
    6: "Verano", 7: "Verano", 8: "Verano", 9: "Verano", 10: "Verano", 11: "Verano"
})

req = {"lat", "lon", "provincia", "canton", "time"}
faltan = req - set(df.columns)
if faltan:
    raise ValueError(f"Faltan columnas en el CSV: {faltan}")

CAND_VARS = [
    "SoilMoi0_10cm_inst", "SoilMoi10_40cm_inst", "RootMoist_inst",
    "SoilTMP0_10cm_inst", "Tair_f_inst", "Rainf_tavg", "Evap_tavg",
    "SWdown_f_tavg", "LWdown_f_tavg", "Qs_acc", "Qsb_acc", "Qsm_acc", "CanopInt_inst",
]
NUM_VARS = [v for v in CAND_VARS if v in df.columns and pd.api.types.is_numeric_dtype(df[v])]

LABELS = {
    "SoilMoi0_10cm_inst":  "Humedad Suelo Superficial (0-10cm)",
    "SoilMoi10_40cm_inst": "Humedad Suelo Profundo (10-40cm)",
    "RootMoist_inst":      "Humedad Zona Radicular",
    "SoilTMP0_10cm_inst":  "Temperatura del Suelo",
    "Tair_f_inst":         "Temperatura del Aire",
    "Rainf_tavg":          "Precipitación Media",
    "Evap_tavg":           "Evapotranspiración",
    "SWdown_f_tavg":       "Radiación Solar Incidente",
    "LWdown_f_tavg":       "Radiación Térmica Incidente",
    "Qs_acc":              "Escorrentía Superficial",
    "Qsb_acc":             "Escorrentía Subsuperficial",
    "Qsm_acc":             "Derretimiento de Nieve",
    "CanopInt_inst":       "Intercepción del Dosel",
}

UNITS = {
    "SoilMoi0_10cm_inst":  "kg/m²", "SoilMoi10_40cm_inst": "kg/m²", "RootMoist_inst": "kg/m²",
    "SoilTMP0_10cm_inst":  "K", "Tair_f_inst": "K", "Rainf_tavg": "kg·m⁻²·s⁻¹",
    "Evap_tavg": "kg·m⁻²·s⁻¹", "SWdown_f_tavg": "W·m⁻²", "LWdown_f_tavg": "W·m⁻²",
    "Qs_acc": "kg/m²", "Qsb_acc": "kg/m²", "Qsm_acc": "kg/m²", "CanopInt_inst": "kg/m²",
}

PROVINCIAS = sorted([p for p in df["provincia"].dropna().unique().tolist()])
CANTONES_POR_PROV = {
    p: sorted(df.loc[df["provincia"] == p, "canton"].dropna().unique().tolist())
    for p in PROVINCIAS
}
YEARS = sorted(df["year"].unique().tolist())
SEASONS = ["Verano", "Invierno"]  # Solo 2 estaciones para Ecuador
MIN_DATE = df["time"].min().date()
MAX_DATE = df["time"].max().date()

app = Dash(
    __name__,
    external_stylesheets=[
        dbc.themes.BOOTSTRAP,
        "https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css"
    ],
    suppress_callback_exceptions=True
)
server = app.server

# --------------------------------------------------
# Estilos personalizados
# --------------------------------------------------
CARD_STYLE = {
    "box-shadow": "0 4px 6px rgba(0, 0, 0, 0.1)",
    "border": "none",
    "border-radius": "10px",
    "margin-bottom": "20px"
}

HEADER_STYLE = {
    "background": "linear-gradient(135deg, #667eea 0%, #764ba2 100%)",
    "color": "white",
    "padding": "30px 0",
    "margin-bottom": "30px",
    "border-radius": "0 0 20px 20px"
}

# --------------------------------------------------
# Layout principal
# --------------------------------------------------
app.layout = dbc.Container([
    # Header mejorado
    html.Div([
        html.H1([
            html.I(className="fas fa-seedling me-3"),
            "AGRO-LEO ECUADOR"
        ], className="text-center mb-2", style={"font-weight": "bold"}),
        html.P("Sistema de Monitoreo Agroclimático basado en datos GLDAS Noah 0.25° (2020-2024)",
               className="text-center mb-0", style={"opacity": "0.9"})
    ], style=HEADER_STYLE),

    # Controles principales
    dbc.Card([
        dbc.CardBody([
            dbc.Row([
                dbc.Col([
                    dbc.Label([html.I(className="fas fa-chart-line me-2"), "Variable Agroclimática"], className="fw-bold"),
                    dcc.Dropdown(
                        id="sel-var",
                        options=[{"label": LABELS.get(v, v), "value": v} for v in NUM_VARS],
                        value=NUM_VARS[0] if NUM_VARS else None,
                        clearable=False,
                        style={"border-radius": "8px"}
                    ),
                ], md=3),

                dbc.Col([
                    dbc.Label([html.I(className="fas fa-calendar me-2"), "Período de Análisis"], className="fw-bold"),
                    dcc.Dropdown(
                        id="sel-years",
                        options=[{"label": f"Año {y}", "value": y} for y in YEARS],
                        value=YEARS,
                        multi=True
                    ),
                ], md=3),

                dbc.Col([
                    dbc.Label([html.I(className="fas fa-map-marker-alt me-2"), "Región"], className="fw-bold"),
                    dcc.Dropdown(
                        id="sel-provinces",
                        options=[{"label": p, "value": p} for p in PROVINCIAS],
                        value=[],
                        multi=True,
                        placeholder="Todas las provincias"
                    ),
                ], md=3),

                dbc.Col([
                    dbc.Label([html.I(className="fas fa-leaf me-2"), "Estación"], className="fw-bold"),
                    dcc.Dropdown(
                        id="sel-season",
                        options=[{"label": s, "value": s} for s in SEASONS],
                        value=[],
                        multi=True,
                        placeholder="Ambas estaciones"
                    ),
                ], md=3),
            ], className="g-3"),

            html.Hr(style={"margin": "20px 0"}),

            dbc.Row([
                dbc.Col([
                    dbc.Label("Cantones (opcional)"),
                    dcc.Dropdown(id="sel-cantons", options=[], value=[], multi=True, placeholder="Seleccionar cantones"),
                ], md=6),
                dbc.Col([
                    dbc.Label("Rango de Fechas"),
                    dcc.DatePickerRange(
                        id="sel-dates",
                        start_date=str(MIN_DATE),
                        end_date=str(MAX_DATE),
                        display_format="DD/MM/YYYY"
                    ),
                ], md=6),
            ], className="g-3"),
        ])
    ], style=CARD_STYLE),

    # KPIs principales
    html.Div(id="main-kpis"),

    # Tabs mejoradas
    dbc.Card([
        dbc.CardBody([
            dcc.Tabs(
                id="main-tabs", 
                value="dashboard",
                children=[
                    dcc.Tab(
                        label="📊 Dashboard Principal", 
                        value="dashboard",
                        className="custom-tab",
                        selected_className="custom-tab--selected"
                    ),
                    dcc.Tab(
                        label="🗺️ Análisis Espacial", 
                        value="spatial",
                        className="custom-tab",
                        selected_className="custom-tab--selected"
                    ),
                    dcc.Tab(
                        label="📈 Series Temporales", 
                        value="temporal",
                        className="custom-tab",
                        selected_className="custom-tab--selected"
                    ),
                    dcc.Tab(
                        label="🌳 Árbol de Clasificación", 
                        value="tree_classification",
                        className="custom-tab",
                        selected_className="custom-tab--selected"
                    ),
                    dcc.Tab(
                        label="📋 Estadísticas Detalladas", 
                        value="statistics",
                        className="custom-tab",
                        selected_className="custom-tab--selected"
                    ),
                ],
                style={"margin-bottom": "20px"}
            ),
            html.Div(id="tab-content")
        ])
    ], style=CARD_STYLE),

    # Footer
    html.Hr(style={"margin-top": "40px"}),
    html.Div([
        html.P([
            "© 2025 AGRO-LEO Ecuador • ",
            html.A("Datos GLDAS", href="https://ldas.gsfc.nasa.gov/gldas", target="_blank"),
            " • Desarrollado para agricultura de precisión"
        ], className="text-center text-muted mb-0")
    ], style={"padding": "20px 0"})

], fluid=True, style={"background-color": "#f8f9fa", "min-height": "100vh", "padding": "0"})

def filtrar_datos(df_orig, var, years, provinces, cantons, season, dates):
    """Filtra el dataframe según los parámetros seleccionados"""
    df_f = df_orig.copy()
    
    if years:
        df_f = df_f[df_f["year"].isin(years)]
    if provinces:
        df_f = df_f[df_f["provincia"].isin(provinces)]
    if cantons:
        df_f = df_f[df_f["canton"].isin(cantons)]
    if season:
        df_f = df_f[df_f["season"].isin(season)]
    if dates and dates[0] and dates[1]:
        df_f = df_f[(df_f["time"] >= pd.to_datetime(dates[0])) & 
                    (df_f["time"] <= pd.to_datetime(dates[1]))]
    
    return df_f

def crear_kpis_principales(df_f, var):
    """Crea las tarjetas de KPIs principales"""
    if df_f.empty or var not in df_f.columns:
        return html.Div()
    
    total_registros = len(df_f)
    fechas_unicas = df_f["time"].nunique()
    ubicaciones = df_f[["lat", "lon"]].drop_duplicates().shape[0]
    na_percent = df_f[var].isna().mean() * 100
    
    var_data = df_f[var].dropna()
    if len(var_data) > 0:
        promedio = var_data.mean()
        minimo = var_data.min()
        maximo = var_data.max()
        desv_std = var_data.std()
    else:
        promedio = minimo = maximo = desv_std = 0
    
    kpis = dbc.Row([
        dbc.Col([
            dbc.Card([
                dbc.CardBody([
                    html.Div([
                        html.I(className="fas fa-database fa-2x text-primary mb-2"),
                        html.H4(f"{total_registros:,}", className="mb-1"),
                        html.P("Registros Totales", className="text-muted mb-0 small")
                    ], className="text-center")
                ])
            ], style=CARD_STYLE)
        ], md=2),
        
        dbc.Col([
            dbc.Card([
                dbc.CardBody([
                    html.Div([
                        html.I(className="fas fa-calendar fa-2x text-success mb-2"),
                        html.H4(f"{fechas_unicas}", className="mb-1"),
                        html.P("Fechas Únicas", className="text-muted mb-0 small")
                    ], className="text-center")
                ])
            ], style=CARD_STYLE)
        ], md=2),
        
        dbc.Col([
            dbc.Card([
                dbc.CardBody([
                    html.Div([
                        html.I(className="fas fa-map-pin fa-2x text-info mb-2"),
                        html.H4(f"{ubicaciones}", className="mb-1"),
                        html.P("Ubicaciones", className="text-muted mb-0 small")
                    ], className="text-center")
                ])
            ], style=CARD_STYLE)
        ], md=2),
        
        dbc.Col([
            dbc.Card([
                dbc.CardBody([
                    html.Div([
                        html.I(className="fas fa-chart-bar fa-2x text-warning mb-2"),
                        html.H4(f"{promedio:.2f}", className="mb-1"),
                        html.P(f"Promedio ({UNITS.get(var, '')})", className="text-muted mb-0 small")
                    ], className="text-center")
                ])
            ], style=CARD_STYLE)
        ], md=2),
        
        dbc.Col([
            dbc.Card([
                dbc.CardBody([
                    html.Div([
                        html.I(className="fas fa-arrows-alt-v fa-2x text-danger mb-2"),
                        html.H4(f"{minimo:.1f} - {maximo:.1f}", className="mb-1"),
                        html.P("Rango Min-Max", className="text-muted mb-0 small")
                    ], className="text-center")
                ])
            ], style=CARD_STYLE)
        ], md=2),
        
        dbc.Col([
            dbc.Card([
                dbc.CardBody([
                    html.Div([
                        html.I(className="fas fa-exclamation-triangle fa-2x text-secondary mb-2"),
                        html.H4(f"{na_percent:.1f}%", className="mb-1"),
                        html.P("Datos Faltantes", className="text-muted mb-0 small")
                    ], className="text-center")
                ])
            ], style=CARD_STYLE)
        ], md=2),
    ], className="g-3 mb-4")
    
    return kpis

def render_dashboard(df_f, var):
    """Dashboard principal con múltiples visualizaciones"""
    
    fig_dist = px.histogram(
        df_f, x=var, nbins=30,
        title=f"Distribución de {LABELS.get(var, var)}",
        labels={var: f"{LABELS.get(var, var)} ({UNITS.get(var, '')})"}
    )
    fig_dist.update_layout(showlegend=False)
    
    df_time = df_f.groupby('time')[var].agg(['mean', 'std']).reset_index()
    fig_time = go.Figure()
    fig_time.add_trace(go.Scatter(
        x=df_time['time'], y=df_time['mean'],
        mode='lines', name='Promedio',
        line=dict(color='#1f77b4', width=2)
    ))
    fig_time.add_trace(go.Scatter(
        x=df_time['time'], y=df_time['mean'] + df_time['std'],
        mode='lines', name='+ 1 Desv. Std', line=dict(width=0),
        showlegend=False, hoverinfo='skip'
    ))
    fig_time.add_trace(go.Scatter(
        x=df_time['time'], y=df_time['mean'] - df_time['std'],
        mode='lines', name='- 1 Desv. Std', line=dict(width=0),
        fillcolor='rgba(31, 119, 180, 0.2)', fill='tonexty',
        showlegend=False, hoverinfo='skip'
    ))
    fig_time.update_layout(
        title=f"Evolución Temporal de {LABELS.get(var, var)}",
        xaxis_title="Fecha",
        yaxis_title=f"{LABELS.get(var, var)} ({UNITS.get(var, '')})"
    )
    
    if 'provincia' in df_f.columns:
        df_prov = df_f.groupby('provincia')[var].agg(['mean', 'count']).reset_index()
        df_prov = df_prov.sort_values('mean', ascending=True)
        
        fig_prov = px.bar(
            df_prov, x='mean', y='provincia',
            title=f"Promedio por Provincia - {LABELS.get(var, var)}",
            labels={'mean': f"{LABELS.get(var, var)} ({UNITS.get(var, '')})", 'provincia': 'Provincia'},
            orientation='h'
        )
        fig_prov.update_layout(height=400)
    else:
        fig_prov = go.Figure()
    
    # ESTACIONES CORREGIDAS PARA ECUADOR
    if 'season' in df_f.columns:
        df_season = df_f.groupby('season')[var].agg(['mean', 'std']).reset_index()
        fig_season = px.bar(
            df_season, x='season', y='mean',
            error_y='std',
            title=f"Variación Estacional Ecuador - {LABELS.get(var, var)}",
            labels={'mean': f"{LABELS.get(var, var)} ({UNITS.get(var, '')})", 'season': 'Estación'},
            color='season',
            color_discrete_map={'Verano': '#ff7f0e', 'Invierno': '#1f77b4'}
        )
        fig_season.update_layout(
            annotations=[
                dict(text="Verano: Junio-Noviembre (seco)", xref="paper", yref="paper",
                     x=0.02, y=0.98, showarrow=False, font_size=10),
                dict(text="Invierno: Diciembre-Mayo (lluvioso)", xref="paper", yref="paper",
                     x=0.02, y=0.93, showarrow=False, font_size=10)
            ]
        )
    else:
        fig_season = go.Figure()
    
    return dbc.Row([
        dbc.Col([
            dcc.Graph(figure=fig_dist, config={"displayModeBar": True})
        ], md=6),
        dbc.Col([
            dcc.Graph(figure=fig_time, config={"displayModeBar": True})
        ], md=6),
        dbc.Col([
            dcc.Graph(figure=fig_prov, config={"displayModeBar": True})
        ], md=6),
        dbc.Col([
            dcc.Graph(figure=fig_season, config={"displayModeBar": True})
        ], md=6),
    ], className="g-3")

def render_spatial_analysis(df_f, var):
    """Análisis espacial con mapa interactivo"""
    
    fig_map = px.scatter_mapbox(
        df_f.sample(min(len(df_f), 10000)) if len(df_f) > 10000 else df_f,
        lat="lat", lon="lon", color=var,
        hover_data={"provincia": True, "canton": True, "time": True, var: ":.3f"},
        zoom=5.2, height=600,
        color_continuous_scale="Viridis",
        title=f"Distribución Espacial - {LABELS.get(var, var)}"
    )
    fig_map.update_traces(marker={"size": 6})
    fig_map.update_layout(mapbox_style="open-street-map")
    
    return dbc.Row([
        dbc.Col([
            dcc.Graph(figure=fig_map, config={"displayModeBar": True})
        ], md=12),
    ], className="g-3")

def render_temporal_analysis_simple(df_f, var):
    """Series temporales simplificada con selector de año"""
    
    # Control de año
    year_selector = dbc.Card([
        dbc.CardBody([
            dbc.Row([
                dbc.Col([
                    dbc.Label("Seleccionar Año para Análisis:"),
                    dcc.Dropdown(
                        id="temporal-year-selector",
                        options=[{"label": f"Año {y}", "value": y} for y in sorted(df_f['year'].unique())],
                        value=sorted(df_f['year'].unique())[-1],  # Último año por defecto
                        clearable=False
                    )
                ], md=4),
                dbc.Col([
                    html.Div(id="temporal-year-info", className="mt-3")
                ], md=8)
            ])
        ])
    ], className="mb-4")
    
    return html.Div([
        year_selector,
        html.Div(id="temporal-analysis-content")
    ])

def create_variable_classes(df_f, var):
    """Crea clases para la variable basada en cuartiles"""
    var_data = df_f[var].dropna()
    
    # Crear clases basadas en cuartiles
    q25 = var_data.quantile(0.25)
    q50 = var_data.quantile(0.50)
    q75 = var_data.quantile(0.75)
    
    def classify_value(value):
        if pd.isna(value):
            return 'Sin datos'
        elif value <= q25:
            return 'Bajo'
        elif value <= q50:
            return 'Medio-Bajo'
        elif value <= q75:
            return 'Medio-Alto'
        else:
            return 'Alto'
    
    return df_f[var].apply(classify_value), {'q25': q25, 'q50': q50, 'q75': q75}

def render_tree_classification(df_f, var):
    """Pestaña dedicada solo al árbol de clasificación"""
    
    try:
        # Crear clases de la variable objetivo
        df_tree = df_f.copy()
        df_tree['target_class'], quartiles = create_variable_classes(df_tree, var)
        
        # Seleccionar características para el modelo
        numeric_vars = [v for v in NUM_VARS if v != var and v in df_tree.columns]
        if len(numeric_vars) < 2:
            return dbc.Alert("No hay suficientes variables para crear el árbol de clasificación.", color="warning")
        
        # Preparar características
        df_tree['month_sin'] = np.sin(2 * np.pi * df_tree['month'] / 12)
        df_tree['month_cos'] = np.cos(2 * np.pi * df_tree['month'] / 12)
        
        # Encoding de variables categóricas
        le_prov = LabelEncoder()
        df_tree['provincia_encoded'] = le_prov.fit_transform(df_tree['provincia'].astype(str))
        
        le_season = LabelEncoder()
        df_tree['season_encoded'] = le_season.fit_transform(df_tree['season'].astype(str))
        
        # Seleccionar características finales
        feature_cols = numeric_vars[:6] + ['month_sin', 'month_cos', 'provincia_encoded', 'season_encoded']
        feature_cols = [col for col in feature_cols if col in df_tree.columns]
        
        # Crear nombres legibles para las características
        feature_names_readable = []
        for col in feature_cols:
            if col in LABELS:
                feature_names_readable.append(LABELS[col])
            elif col == 'provincia_encoded':
                feature_names_readable.append('Provincia')
            elif col == 'season_encoded':
                feature_names_readable.append('Estación')
            elif col == 'month_sin':
                feature_names_readable.append('Mes (Sin)')
            elif col == 'month_cos':
                feature_names_readable.append('Mes (Cos)')
            else:
                feature_names_readable.append(col)
        
        # Preparar datos
        X = df_tree[feature_cols].fillna(df_tree[feature_cols].median())
        y = df_tree['target_class']
        
        # Filtrar datos válidos
        valid_mask = y != 'Sin datos'
        X = X[valid_mask]
        y = y[valid_mask]
        
        if len(X) < 100:
            return dbc.Alert("No hay suficientes datos válidos para entrenar el árbol.", color="warning")
        
        # Dividir datos
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
        
        # Entrenar árbol de clasificación
        tree_model = DecisionTreeClassifier(
            max_depth=5, 
            min_samples_split=50, 
            min_samples_leaf=20,
            random_state=42
        )
        tree_model.fit(X_train, y_train)
        
        # Predicciones
        y_pred = tree_model.predict(X_test)
        
        # Crear visualización del árbol
        plt.figure(figsize=(25, 15))
        plot_tree(
            tree_model,
            feature_names=feature_names_readable,
            class_names=['Alto', 'Bajo', 'Medio-Alto', 'Medio-Bajo'],
            filled=True,
            rounded=True,
            fontsize=12,
            proportion=True,
            impurity=True
        )
        plt.title(f"Árbol de Clasificación - {LABELS.get(var, var)}", fontsize=20, fontweight='bold', pad=20)
        
        # Guardar como imagen base64
        buffer = io.BytesIO()
        plt.savefig(buffer, format='png', dpi=150, bbox_inches='tight')
        buffer.seek(0)
        tree_plot_url = base64.b64encode(buffer.getvalue()).decode()
        plt.close()
        
        # Métricas del modelo
        accuracy = accuracy_score(y_test, y_pred)
        
        # Matriz de confusión
        cm = confusion_matrix(y_test, y_pred, labels=['Bajo', 'Medio-Bajo', 'Medio-Alto', 'Alto'])
        
        # Crear gráfico de matriz de confusión
        fig_cm = px.imshow(
            cm,
            labels=dict(x="Predicho", y="Real", color="Frecuencia"),
            x=['Bajo', 'Medio-Bajo', 'Medio-Alto', 'Alto'],
            y=['Bajo', 'Medio-Bajo', 'Medio-Alto', 'Alto'],
            title="Matriz de Confusión",
            color_continuous_scale="Blues",
            text_auto=True
        )
        
        # Importancia de características
        importance_df = pd.DataFrame({
            'Variable': feature_names_readable,
            'Importancia': tree_model.feature_importances_
        }).sort_values('Importancia', ascending=True)
        
        fig_importance = px.bar(
            importance_df.tail(10), x='Importancia', y='Variable',
            title="Importancia de Variables en el Árbol",
            orientation='h',
            color='Importancia',
            color_continuous_scale='Viridis'
        )
        
        # Información de las clases
        class_info = dbc.Card([
            dbc.CardHeader("Información de las Clases"),
            dbc.CardBody([
                html.P(f"Bajo: ≤ {quartiles['q25']:.3f} {UNITS.get(var, '')}", className="mb-2"),
                html.P(f"Medio-Bajo: {quartiles['q25']:.3f} - {quartiles['q50']:.3f} {UNITS.get(var, '')}", className="mb-2"),
                html.P(f"Medio-Alto: {quartiles['q50']:.3f} - {quartiles['q75']:.3f} {UNITS.get(var, '')}", className="mb-2"),
                html.P(f"Alto: > {quartiles['q75']:.3f} {UNITS.get(var, '')}", className="mb-0"),
            ])
        ])
        
        # Métricas del modelo
        metrics_card = dbc.Card([
            dbc.CardHeader("Métricas del Modelo"),
            dbc.CardBody([
                html.H4(f"Precisión: {accuracy:.3f}", className="text-primary mb-3"),
                html.P(f"Datos de entrenamiento: {len(X_train):,}", className="mb-2"),
                html.P(f"Datos de prueba: {len(X_test):,}", className="mb-2"),
                html.P(f"Profundidad del árbol: {tree_model.get_depth()}", className="mb-0"),
            ])
        ])
        
        return dbc.Row([
            # Información y métricas
            dbc.Col([
                class_info,
                html.Br(),
                metrics_card
            ], md=3),
            
            # Visualización del árbol
            dbc.Col([
                dbc.Card([
                    dbc.CardHeader("Visualización del Árbol de Clasificación"),
                    dbc.CardBody([
                        html.Img(src=f"data:image/png;base64,{tree_plot_url}", style={"width": "100%", "height": "auto"}),
                        html.Hr(),
                        dbc.Alert([
                            html.H6("Interpretación:", className="mb-2"),
                            html.P("• Cada nodo muestra la condición de división y las muestras", className="mb-1"),
                            html.P("• Los colores representan las diferentes clases", className="mb-1"),
                            html.P("• Las hojas muestran la clasificación final", className="mb-0"),
                        ], color="info")
                    ])
                ])
            ], md=9),
            
            # Matriz de confusión
            dbc.Col([
                dcc.Graph(figure=fig_cm, config={"displayModeBar": True})
            ], md=6),
            
            # Importancia de variables
            dbc.Col([
                dcc.Graph(figure=fig_importance, config={"displayModeBar": True})
            ], md=6)
        ], className="g-3")
        
    except Exception as e:
        return dbc.Alert(f"Error al crear el árbol de clasificación: {str(e)}", color="danger")

def render_detailed_statistics(df_f, var):
    """Estadísticas detalladas mejoradas"""
    
    var_data = df_f[var].dropna()
    
    if len(var_data) == 0:
        return dbc.Alert("No hay datos disponibles para análisis estadístico.", color="warning")
    
    # Estadísticas básicas
    stats = {
        'Media': var_data.mean(),
        'Mediana': var_data.median(),
        'Moda': var_data.mode().iloc[0] if not var_data.mode().empty else var_data.median(),
        'Desviación Estándar': var_data.std(),
        'Varianza': var_data.var(),
        'Mínimo': var_data.min(),
        'Máximo': var_data.max(),
        'Rango': var_data.max() - var_data.min(),
        'Q1 (Percentil 25)': var_data.quantile(0.25),
        'Q3 (Percentil 75)': var_data.quantile(0.75),
        'IQR': var_data.quantile(0.75) - var_data.quantile(0.25),
        'Coef. Variación %': (var_data.std() / var_data.mean()) * 100 if var_data.mean() != 0 else 0
    }
    
    stats_df = pd.DataFrame([
        {'Estadística': k, 'Valor': f"{v:.3f}", 'Unidad': UNITS.get(var, '')}
        for k, v in stats.items()
    ])
    
    # Estadísticas por provincia
    if 'provincia' in df_f.columns:
        prov_stats = df_f.groupby('provincia')[var].agg([
            'count', 'mean', 'median', 'std', 'min', 'max'
        ]).round(3).reset_index()
        prov_stats.columns = ['Provincia', 'Registros', 'Media', 'Mediana', 'Desv. Std', 'Mínimo', 'Máximo']
    else:
        prov_stats = pd.DataFrame()
    
    # Estadísticas por estación (Ecuador: Verano/Invierno)
    season_stats = df_f.groupby('season')[var].agg([
        'count', 'mean', 'median', 'std', 'min', 'max'
    ]).round(3).reset_index()
    season_stats.columns = ['Estación', 'Registros', 'Media', 'Mediana', 'Desv. Std', 'Mínimo', 'Máximo']
    season_stats['Período'] = season_stats['Estación'].map({
        'Verano': 'Jun-Nov (Seco)',
        'Invierno': 'Dic-May (Lluvioso)'
    })
    
    # Gráficos
    fig_box = px.box(
        df_f, x='provincia', y=var,
        title=f"Distribución por Provincia - {LABELS.get(var, var)}",
        labels={var: f"{LABELS.get(var, var)} ({UNITS.get(var, '')})", 'provincia': 'Provincia'}
    )
    fig_box.update_xaxes(tickangle=45)
    
    # Distribución vs normal
    fig_hist_normal = go.Figure()
    fig_hist_normal.add_trace(go.Histogram(
        x=var_data, nbinsx=40, name='Datos Observados',
        marker_color='lightblue', opacity=0.7, histnorm='probability density'
    ))
    
    # Curva normal teórica
    x_norm = np.linspace(var_data.min(), var_data.max(), 100)
    from scipy import stats as scipy_stats
    y_norm = scipy_stats.norm.pdf(x_norm, var_data.mean(), var_data.std())
    
    fig_hist_normal.add_trace(go.Scatter(
        x=x_norm, y=y_norm, mode='lines',
        name='Distribución Normal Teórica', 
        line=dict(color='red', width=3)
    ))
    
    fig_hist_normal.update_layout(
        title=f"Distribución vs Normal Teórica - {LABELS.get(var, var)}",
        xaxis_title=f"{LABELS.get(var, var)} ({UNITS.get(var, '')})",
        yaxis_title="Densidad de Probabilidad"
    )
    
    # Correlaciones
    numeric_cols = [col for col in NUM_VARS if col in df_f.columns and col != var]
    if len(numeric_cols) > 1:
        corr_data = df_f[[var] + numeric_cols[:8]].corr()[var].drop(var)
        corr_df = pd.DataFrame({
            'Variable': [LABELS.get(col, col) for col in corr_data.index],
            'Correlación': corr_data.values
        }).sort_values('Correlación', key=abs, ascending=False)
        
        fig_corr = px.bar(
            corr_df.head(8), x='Correlación', y='Variable',
            title=f"Correlaciones más Fuertes con {LABELS.get(var, var)}",
            orientation='h',
            color='Correlación', 
            color_continuous_scale='RdBu_r',
            range_color=[-1, 1]
        )
    else:
        fig_corr = go.Figure()
    
    # Variación estacional específica para Ecuador
    fig_seasonal = px.box(
        df_f, x='season', y=var, color='season',
        title=f"Variación Estacional Ecuador - {LABELS.get(var, var)}",
        labels={var: f"{LABELS.get(var, var)} ({UNITS.get(var, '')})", 'season': 'Estación'},
        color_discrete_map={'Verano': '#ff7f0e', 'Invierno': '#1f77b4'}
    )
    fig_seasonal.update_layout(
        annotations=[
            dict(text="Verano: Junio-Noviembre (época seca)", xref="paper", yref="paper",
                 x=0.02, y=0.98, showarrow=False, font_size=11, bgcolor="rgba(255,255,255,0.8)"),
            dict(text="Invierno: Diciembre-Mayo (época lluviosa)", xref="paper", yref="paper",
                 x=0.02, y=0.93, showarrow=False, font_size=11, bgcolor="rgba(255,255,255,0.8)")
        ]
    )
    
    return dbc.Row([
        # Estadísticas básicas
        dbc.Col([
            dbc.Card([
                dbc.CardHeader([
                    html.I(className="fas fa-chart-pie me-2"),
                    f"Estadísticas Descriptivas - {LABELS.get(var, var)}"
                ]),
                dbc.CardBody([
                    dash_table.DataTable(
                        data=stats_df.to_dict('records'),
                        columns=[{"name": i, "id": i} for i in stats_df.columns],
                        style_cell={'textAlign': 'left', 'fontSize': '14px'},
                        style_header={'backgroundColor': '#f8f9fa', 'fontWeight': 'bold'},
                        style_data_conditional=[
                            {
                                'if': {'row_index': 'odd'},
                                'backgroundColor': '#f8f9fa'
                            }
                        ]
                    )
                ])
            ], style=CARD_STYLE)
        ], md=6),
        
        # Distribución vs normal
        dbc.Col([
            dcc.Graph(figure=fig_hist_normal, config={"displayModeBar": True})
        ], md=6),
        
        # Box plot por provincia
        dbc.Col([
            dcc.Graph(figure=fig_box, config={"displayModeBar": True})
        ], md=6),
        
        # Correlaciones
        dbc.Col([
            dcc.Graph(figure=fig_corr, config={"displayModeBar": True})
        ], md=6),
        
        # Variación estacional
        dbc.Col([
            dcc.Graph(figure=fig_seasonal, config={"displayModeBar": True})
        ], md=12),
        
        # Estadísticas por estación
        dbc.Col([
            dbc.Card([
                dbc.CardHeader([
                    html.I(className="fas fa-leaf me-2"),
                    "Estadísticas por Estación Climática (Ecuador)"
                ]),
                dbc.CardBody([
                    dash_table.DataTable(
                        data=season_stats.to_dict('records') if not season_stats.empty else [],
                        columns=[{"name": i, "id": i} for i in season_stats.columns] if not season_stats.empty else [],
                        style_cell={'textAlign': 'center', 'fontSize': '12px'},
                        style_header={'backgroundColor': '#e8f5e8', 'fontWeight': 'bold'},
                        style_data_conditional=[
                            {
                                'if': {'filter_query': '{Estación} = Verano'},
                                'backgroundColor': '#fff3cd',
                            },
                            {
                                'if': {'filter_query': '{Estación} = Invierno'},
                                'backgroundColor': '#d1ecf1',
                            }
                        ]
                    ) if not season_stats.empty else html.P("No hay datos estacionales disponibles", className="text-muted")
                ])
            ], style=CARD_STYLE)
        ], md=6),
        
        # Estadísticas por provincia
        dbc.Col([
            dbc.Card([
                dbc.CardHeader([
                    html.I(className="fas fa-map me-2"),
                    "Estadísticas por Provincia"
                ]),
                dbc.CardBody([
                    dash_table.DataTable(
                        data=prov_stats.to_dict('records') if not prov_stats.empty else [],
                        columns=[{"name": i, "id": i} for i in prov_stats.columns] if not prov_stats.empty else [],
                        style_cell={'textAlign': 'center', 'fontSize': '11px'},
                        style_header={'backgroundColor': '#e3f2fd', 'fontWeight': 'bold'},
                        style_data_conditional=[
                            {
                                'if': {'row_index': 'odd'},
                                'backgroundColor': '#f8f9fa'
                            }
                        ],
                        page_size=10,
                        sort_action="native"
                    ) if not prov_stats.empty else html.P("No hay datos provinciales disponibles", className="text-muted")
                ])
            ], style=CARD_STYLE)
        ], md=6),
    ], className="g-3")

# Callbacks

@app.callback(
    Output("sel-cantons", "options"),
    Output("sel-cantons", "value"),
    Input("sel-provinces", "value"),
)
def actualizar_cantones(provinces):
    if not provinces:
        todos = sorted(df["canton"].dropna().unique().tolist())
        return [{"label": c, "value": c} for c in todos], []
    
    cantones = sorted(df.loc[df["provincia"].isin(provinces), "canton"].dropna().unique().tolist())
    return [{"label": c, "value": c} for c in cantones], []

@app.callback(
    Output("main-kpis", "children"),
    [Input("sel-var", "value"),
     Input("sel-years", "value"),
     Input("sel-provinces", "value"),
     Input("sel-cantons", "value"),
     Input("sel-season", "value"),
     Input("sel-dates", "start_date"),
     Input("sel-dates", "end_date")]
)
def actualizar_kpis(var, years, provinces, cantons, season, start_date, end_date):
    if not var:
        return html.Div()
    
    years = years or YEARS
    df_f = filtrar_datos(df, var, years, provinces, cantons, season, (start_date, end_date))
    return crear_kpis_principales(df_f, var)

# Callback para análisis temporal por año
@app.callback(
    [Output("temporal-analysis-content", "children"),
     Output("temporal-year-info", "children")],
    [Input("temporal-year-selector", "value")],
    [State("sel-var", "value"),
     State("sel-provinces", "value"),
     State("sel-cantons", "value"),
     State("sel-season", "value")]
)
def update_temporal_analysis(selected_year, var, provinces, cantons, season):
    if not selected_year or not var:
        return html.Div(), html.Div()
    
    # Filtrar datos para el año seleccionado
    df_year = df[df['year'] == selected_year].copy()
    
    # Aplicar filtros adicionales
    if provinces:
        df_year = df_year[df_year["provincia"].isin(provinces)]
    if cantons:
        df_year = df_year[df_year["canton"].isin(cantons)]
    if season:
        df_year = df_year[df_year["season"].isin(season)]
    
    if df_year.empty:
        return dbc.Alert(f"No hay datos para el año {selected_year} con los filtros seleccionados.", color="warning"), html.Div()
    
    # Información del año
    year_info = dbc.Alert([
        html.H6(f"Análisis del Año {selected_year}", className="mb-2"),
        html.P(f"Registros encontrados: {len(df_year):,}", className="mb-1"),
        html.P(f"Rango de fechas: {df_year['time'].min().strftime('%d/%m/%Y')} - {df_year['time'].max().strftime('%d/%m/%Y')}", className="mb-0")
    ], color="info")
    
    # Gráficos del año seleccionado
    
    # 1. Serie temporal mensual
    df_monthly = df_year.groupby(df_year['time'].dt.month)[var].agg(['mean', 'std', 'count']).reset_index()
    df_monthly['month_name'] = df_monthly['time'].map({
        1: 'Ene', 2: 'Feb', 3: 'Mar', 4: 'Abr', 5: 'May', 6: 'Jun',
        7: 'Jul', 8: 'Ago', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dic'
    })
    
    fig_monthly = go.Figure()
    fig_monthly.add_trace(go.Scatter(
        x=df_monthly['month_name'], 
        y=df_monthly['mean'],
        mode='lines+markers',
        name='Promedio Mensual',
        line=dict(color='#1f77b4', width=3),
        marker=dict(size=8),
        error_y=dict(type='data', array=df_monthly['std'], visible=True)
    ))
    
    fig_monthly.update_layout(
        title=f"Evolución Mensual {selected_year} - {LABELS.get(var, var)}",
        xaxis_title="Mes",
        yaxis_title=f"{LABELS.get(var, var)} ({UNITS.get(var, '')})",
        hovermode='x unified'
    )
    
    # 2. Distribución por estaciones
    df_seasonal = df_year.groupby('season')[var].agg(['mean', 'std', 'count']).reset_index()
    
    fig_seasonal = px.bar(
        df_seasonal, x='season', y='mean',
        error_y='std',
        title=f"Comparación Estacional {selected_year} - {LABELS.get(var, var)}",
        labels={'mean': f"{LABELS.get(var, var)} ({UNITS.get(var, '')})", 'season': 'Estación'},
        color='season',
        color_discrete_map={'Verano': '#ff7f0e', 'Invierno': '#1f77b4'}
    )
    
    # 3. Distribución de valores
    fig_dist = px.histogram(
        df_year, x=var, nbins=30,
        title=f"Distribución de Valores {selected_year} - {LABELS.get(var, var)}",
        labels={var: f"{LABELS.get(var, var)} ({UNITS.get(var, '')})"}
    )
    
    # 4. Evolución diaria (si hay suficientes datos)
    if len(df_year) > 50:
        df_daily = df_year.groupby('time')[var].mean().reset_index()
        
        fig_daily = px.line(
            df_daily, x='time', y=var,
            title=f"Evolución Diaria {selected_year} - {LABELS.get(var, var)}",
            labels={'time': 'Fecha', var: f"{LABELS.get(var, var)} ({UNITS.get(var, '')})"}
        )
        fig_daily.update_traces(line_color='#2ca02c')
        
        daily_graph = dbc.Col([
            dcc.Graph(figure=fig_daily, config={"displayModeBar": True})
        ], md=12)
    else:
        daily_graph = html.Div()
    
    content = dbc.Row([
        dbc.Col([
            dcc.Graph(figure=fig_monthly, config={"displayModeBar": True})
        ], md=6),
        dbc.Col([
            dcc.Graph(figure=fig_seasonal, config={"displayModeBar": True})
        ], md=6),
        dbc.Col([
            dcc.Graph(figure=fig_dist, config={"displayModeBar": True})
        ], md=6),
        dbc.Col([
            # Estadísticas del año
            dbc.Card([
                dbc.CardHeader(f"Estadísticas {selected_year}"),
                dbc.CardBody([
                    html.P(f"Promedio: {df_year[var].mean():.3f} {UNITS.get(var, '')}", className="mb-2"),
                    html.P(f"Mediana: {df_year[var].median():.3f} {UNITS.get(var, '')}", className="mb-2"),
                    html.P(f"Desv. Estándar: {df_year[var].std():.3f}", className="mb-2"),
                    html.P(f"Mínimo: {df_year[var].min():.3f} {UNITS.get(var, '')}", className="mb-2"),
                    html.P(f"Máximo: {df_year[var].max():.3f} {UNITS.get(var, '')}", className="mb-0"),
                ])
            ])
        ], md=6),
        daily_graph
    ], className="g-3")
    
    return content, year_info

@app.callback(
    Output("tab-content", "children"),
    [Input("main-tabs", "value"),
     Input("sel-var", "value"),
     Input("sel-years", "value"),
     Input("sel-provinces", "value"),
     Input("sel-cantons", "value"),
     Input("sel-season", "value"),
     Input("sel-dates", "start_date"),
     Input("sel-dates", "end_date")]
)
def render_tab_content(active_tab, var, years, provinces, cantons, season, start_date, end_date):
    if not var:
        return dbc.Alert("Por favor selecciona una variable para continuar.", color="info")
    
    years = years or YEARS
    df_f = filtrar_datos(df, var, years, provinces, cantons, season, (start_date, end_date))
    
    if df_f.empty:
        return dbc.Alert("No hay datos disponibles con los filtros seleccionados.", color="warning")
    
    if active_tab == "dashboard":
        return render_dashboard(df_f, var)
    elif active_tab == "spatial":
        return render_spatial_analysis(df_f, var)
    elif active_tab == "temporal":
        return render_temporal_analysis_simple(df_f, var)
    elif active_tab == "tree_classification":
        return render_tree_classification(df_f, var)
    elif active_tab == "statistics":
        return render_detailed_statistics(df_f, var)

# CSS personalizado mejorado
app.index_string = '''
<!DOCTYPE html>
<html>
    <head>
        {%metas%}
        <title>{%title%}</title>
        {%favicon%}
        {%css%}
        <style>
            .custom-tab {
                background-color: #f8f9fa !important;
                border: 1px solid #dee2e6 !important;
                border-radius: 8px 8px 0 0 !important;
                margin-right: 4px !important;
                font-weight: 500 !important;
                transition: all 0.3s ease !important;
                padding: 12px 20px !important;
            }
            .custom-tab:hover {
                background-color: #e9ecef !important;
                transform: translateY(-2px) !important;
            }
            .custom-tab--selected {
                background-color: #007bff !important;
                color: white !important;
                border-color: #007bff !important;
                box-shadow: 0 4px 8px rgba(0,123,255,0.3) !important;
            }
            .card {
                transition: transform 0.2s ease, box-shadow 0.2s ease !important;
            }
            .card:hover {
                transform: translateY(-2px) !important;
                box-shadow: 0 8px 25px rgba(0, 0, 0, 0.15) !important;
            }
            body {
                font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif !important;
            }
            .fas {
                color: #007bff !important;
            }
            
            /* Estilos para las predicciones */
            .prediction-highlight {
                background: linear-gradient(45deg, #28a745, #20c997) !important;
                color: white !important;
                border-radius: 8px !important;
                padding: 10px !important;
                margin: 5px 0 !important;
            }
            
            /* Mejorar visualización de tablas */
            .dash-table-container .dash-spreadsheet-container .dash-spreadsheet-inner table {
                border-collapse: separate !important;
                border-spacing: 0 !important;
            }
            
            .dash-table-container .dash-spreadsheet-container .dash-spreadsheet-inner table th,
            .dash-table-container .dash-spreadsheet-container .dash-spreadsheet-inner table td {
                border: 1px solid #dee2e6 !important;
                border-top: none !important;
                border-left: none !important;
            }
            
            .dash-table-container .dash-spreadsheet-container .dash-spreadsheet-inner table th:first-child,
            .dash-table-container .dash-spreadsheet-container .dash-spreadsheet-inner table td:first-child {
                border-left: 1px solid #dee2e6 !important;
            }
            
            .dash-table-container .dash-spreadsheet-container .dash-spreadsheet-inner table tr:first-child th {
                border-top: 1px solid #dee2e6 !important;
            }
            
            /* Animaciones suaves */
            .loading-spinner {
                animation: spin 1s linear infinite !important;
            }
            
            @keyframes spin {
                0% { transform: rotate(0deg); }
                100% { transform: rotate(360deg); }
            }
            
            /* Responsive mejoras */
            @media (max-width: 768px) {
                .custom-tab {
                    font-size: 12px !important;
                    padding: 8px 12px !important;
                }
            }
        </style>
    </head>
    <body>
        {%app_entry%}
        <footer>
            {%config%}
            {%scripts%}
            {%renderer%}
        </footer>
    </body>
</html>
'''

if __name__ == "__main__":
    port = int(os.environ.get("PORT", 7860))
    app.run_server(
        host="0.0.0.0", 
        port=port, 
        debug=False
    )