"""
Iris Flower Classifier - Aplicación Gradio
============================================
Interfaz interactiva para clasificar flores Iris usando XGBoost.
"""

import gradio as gr
import joblib
import numpy as np
import pandas as pd
import json
import matplotlib.pyplot as plt
import seaborn as sns
import os

# Cargar modelo y metadata
model = joblib.load("model.joblib")
le = joblib.load("label_encoder.joblib")

with open("model_info.json") as f:
    model_info = json.load(f)

# Cargar datos para exploración
df = pd.read_csv("data/IRIS.csv")

# Rangos de features para los sliders
FEATURE_RANGES = {
    "sepal_length": (4.0, 8.0, 5.8),
    "sepal_width": (2.0, 4.5, 3.0),
    "petal_length": (1.0, 7.0, 3.8),
    "petal_width": (0.1, 2.5, 1.2),
}

SPECIES_EMOJI = {
    "Iris-setosa": "🌸",
    "Iris-versicolor": "🌺",
    "Iris-virginica": "🌻",
}


def predict(sepal_length, sepal_width, petal_length, petal_width):
    """Predecir especie de Iris."""
    features = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
    proba = model.predict_proba(features)[0]
    result = {
        f"{SPECIES_EMOJI.get(cls, '')} {cls}": float(p)
        for cls, p in zip(le.classes_, proba)
    }
    return result


def create_eda_plot(column, plot_type):
    """Generar gráfico EDA interactivo."""
    fig, ax = plt.subplots(figsize=(10, 6))

    if plot_type == "Histograma":
        for species in df['species'].unique():
            subset = df[df['species'] == species]
            ax.hist(subset[column], alpha=0.6, label=species, bins=15)
        ax.legend()
    elif plot_type == "Boxplot":
        sns.boxplot(x='species', y=column, data=df, ax=ax)
    elif plot_type == "Violin":
        sns.violinplot(x='species', y=column, data=df, ax=ax)
    elif plot_type == "Scatter (vs petal_length)":
        for species in df['species'].unique():
            subset = df[df['species'] == species]
            ax.scatter(subset[column], subset['petal_length'], alpha=0.7, label=species)
        ax.set_ylabel("petal_length")
        ax.legend()

    ax.set_title(f"{plot_type} de {column}")
    ax.set_xlabel(column)
    plt.tight_layout()
    return fig


def show_correlation():
    """Mostrar matriz de correlación."""
    fig, ax = plt.subplots(figsize=(8, 6))
    numeric_df = df.select_dtypes(include=[np.number])
    corr = numeric_df.corr()
    mask = np.triu(np.ones_like(corr, dtype=bool))
    sns.heatmap(corr, mask=mask, annot=True, fmt=".2f", cmap="coolwarm", center=0, ax=ax)
    ax.set_title("Matriz de Correlación")
    plt.tight_layout()
    return fig


def show_pairplot():
    """Generar pairplot."""
    fig = sns.pairplot(df, hue='species', diag_kind='kde', height=2.2)
    return fig.figure


# ============================================================
# INTERFAZ GRADIO
# ============================================================
numeric_cols = ["sepal_length", "sepal_width", "petal_length", "petal_width"]

with gr.Blocks(theme=gr.themes.Soft(), title="Iris Flower Classifier") as demo:
    gr.Markdown(
        """
        # 🌺 Iris Flower Classifier
        Clasificador de flores Iris usando **XGBoost** entrenado con el
        [dataset de Kaggle](https://www.kaggle.com/datasets/sims22/irisflowerdatasets).
        """
    )

    with gr.Tab("🔮 Predicción"):
        with gr.Row():
            with gr.Column():
                sl = gr.Slider(*FEATURE_RANGES["sepal_length"], label="Sepal Length (cm)")
                sw = gr.Slider(*FEATURE_RANGES["sepal_width"], label="Sepal Width (cm)")
                pl = gr.Slider(*FEATURE_RANGES["petal_length"], label="Petal Length (cm)")
                pw = gr.Slider(*FEATURE_RANGES["petal_width"], label="Petal Width (cm)")
                predict_btn = gr.Button("Clasificar", variant="primary")
            with gr.Column():
                output_label = gr.Label(num_top_classes=3, label="Predicción")

        predict_btn.click(predict, inputs=[sl, sw, pl, pw], outputs=output_label)

        gr.Examples(
            examples=[
                [5.1, 3.5, 1.4, 0.2],   # Setosa
                [6.2, 2.9, 4.3, 1.3],   # Versicolor
                [7.7, 3.0, 6.1, 2.3],   # Virginica
            ],
            inputs=[sl, sw, pl, pw],
            label="Ejemplos por especie",
        )

    with gr.Tab("📊 EDA Interactivo"):
        with gr.Row():
            col_selector = gr.Dropdown(
                choices=numeric_cols, value="petal_length", label="Feature"
            )
            plot_type = gr.Dropdown(
                choices=["Histograma", "Boxplot", "Violin", "Scatter (vs petal_length)"],
                value="Histograma",
                label="Tipo de gráfico",
            )
        eda_plot = gr.Plot(label="Visualización")
        col_selector.change(create_eda_plot, [col_selector, plot_type], eda_plot)
        plot_type.change(create_eda_plot, [col_selector, plot_type], eda_plot)

        with gr.Row():
            corr_btn = gr.Button("Matriz de Correlación")
            pair_btn = gr.Button("Pairplot")
        extra_plot = gr.Plot(label="Análisis")
        corr_btn.click(show_correlation, outputs=extra_plot)
        pair_btn.click(show_pairplot, outputs=extra_plot)

    with gr.Tab("📋 Datos"):
        gr.Markdown("### Dataset Iris (150 muestras)")
        gr.DataFrame(value=df, label="Dataset completo")
        gr.Markdown(f"### Estadísticas descriptivas")
        gr.DataFrame(value=df.describe().reset_index(), label="Estadísticas")

    with gr.Tab("📈 Métricas del Modelo"):
        gr.Markdown(f"""
        ### Rendimiento del modelo XGBoost
        - **Test Accuracy**: {model_info['metrics']['test_accuracy']:.4f}
        - **CV Accuracy**: {model_info['metrics']['cv_accuracy_mean']:.4f} ± {model_info['metrics']['cv_accuracy_std']:.4f}
        - **Clases**: {', '.join(model_info['classes'])}
        """)
        with gr.Row():
            if os.path.exists("outputs/confusion_matrix.png"):
                gr.Image(value="outputs/confusion_matrix.png", label="Confusion Matrix")
            if os.path.exists("outputs/feature_importance.png"):
                gr.Image(value="outputs/feature_importance.png", label="Feature Importance")
        gr.JSON(value=model_info, label="Metadata del modelo")


if __name__ == "__main__":
    demo.launch()