"""Movie Box Office Revenue Predictor - Gradio Web Application."""

from __future__ import annotations

from pathlib import Path
from typing import Any

import gradio as gr

from components import create_input_form, get_example_data, predict_revenue_from_form
from src.preprocess import load_model, parse_feature_options

# Configuration
MODEL_PATH = Path("model/final_model.pkl")

# Global state
MODEL: Any | None = None
MODEL_ERROR: str | None = None
FEATURE_OPTIONS: dict[str, list[str]] = {}

# Initialize model
try:
    MODEL = load_model(MODEL_PATH)
    FEATURE_OPTIONS = parse_feature_options(list(MODEL.feature_names_in_))
except Exception as exc:
    MODEL_ERROR = str(exc)


def build_app() -> gr.Blocks:
    """Build and configure the Gradio interface."""
    
    with gr.Blocks(
        title="🎬 Movie Revenue Predictor",
    ) as app:
        
        # Header
        with gr.Row():
            gr.Markdown(
                """
                # 🎬 Movie Box Office Revenue Predictor
                
                Predict movie revenue using machine learning trained on historical box office data.
                Enter movie details below and get instant revenue predictions with profitability analysis.
                """,
                elem_classes=["header"]
            )
        
        # Model status
        if MODEL is None:
            gr.Warning(f"⚠️ Model loading error: {MODEL_ERROR}")
        
        # Main content
        with gr.Row():
            with gr.Column(scale=3):
                # Input form
                input_dict, input_list = create_input_form(FEATURE_OPTIONS)
                
                # Action buttons
                with gr.Row():
                    predict_btn = gr.Button(
                        "🎯 Predict Revenue",
                        variant="primary",
                        scale=2,
                        size="lg"
                    )
                    clear_btn = gr.ClearButton(
                        components=input_list,
                        value="🔄 Clear",
                        scale=1,
                        size="lg"
                    )
                
                # Examples
                gr.Markdown("### 📝 Quick Examples")
                gr.Examples(
                    examples=get_example_data(FEATURE_OPTIONS),
                    inputs=input_list,
                    label="Click an example to auto-fill the form",
                )
            
            with gr.Column(scale=2):
                gr.Markdown("### 📊 Prediction Results")
                
                # Output displays
                prediction_output = gr.Markdown(
                    "💡 Fill in the form and click **Predict Revenue** to see results.",
                    elem_classes=["output-box"]
                )
                
                profitability_output = gr.Markdown(
                    "",
                    elem_classes=["output-box"]
                )
        
        # Event handlers
        predict_btn.click(
            fn=lambda *args: predict_revenue_from_form(MODEL, *args),
            inputs=input_list,
            outputs=[prediction_output, profitability_output],
        )
    
    return app


def main():
    """Launch the application."""
    theme = gr.themes.Default(
        primary_hue="zinc",
        secondary_hue="slate",
        neutral_hue="slate",
    )
    
    app = build_app()
    app.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        theme=theme,
    )


if __name__ == "__main__":
    main()