#!/usr/bin/env python3
"""
Example usage of the Advanced Magnus Chess Model from Hugging Face
"""

import torch
import chess
import yaml
import json
from pathlib import Path
import sys

# Add current directory to path to import the model
sys.path.append(".")


def load_model_from_hf():
    """Load the Advanced Magnus model"""
    try:
        from advanced_magnus_predictor import AdvancedMagnusPredictor

        # Initialize predictor - it will automatically find the model files
        predictor = AdvancedMagnusPredictor()

        if predictor.model is None:
            raise Exception("Failed to load model")

        print("✅ Advanced Magnus Chess Model loaded successfully!")
        print(f"   Device: {predictor.device}")
        print(f"   Vocabulary size: {predictor.vocab_size}")
        print(
            f"   Parameters: {sum(p.numel() for p in predictor.model.parameters()):,}"
        )

        return predictor

    except Exception as e:
        print(f"❌ Failed to load model: {e}")
        return None


def demo_predictions(predictor):
    """Demonstrate model predictions on various positions"""

    print("\n🎯 Magnus Style Move Predictions Demo")
    print("=" * 50)

    # Test positions
    positions = [
        {
            "name": "Opening - King's Pawn",
            "fen": "rnbqkbnr/pppppppp/8/8/4P3/8/PPPP1PPP/RNBQKBNR b KQkq e3 0 1",
            "description": "Black to move after 1.e4",
        },
        {
            "name": "Sicilian Defense",
            "fen": "rnbqkbnr/pp1ppppp/8/2p5/4P3/8/PPPP1PPP/RNBQKBNR w KQkq c6 0 2",
            "description": "White to move after 1.e4 c5",
        },
        {
            "name": "Queen's Gambit",
            "fen": "rnbqkbnr/ppp1pppp/8/3p4/2PP4/8/PP2PPPP/RNBQKBNR b KQkq c3 0 2",
            "description": "Black to move after 1.d4 d5 2.c4",
        },
    ]

    for pos in positions:
        print(f"\n📍 {pos['name']}")
        print(f"   {pos['description']}")
        print(f"   FEN: {pos['fen']}")

        try:
            board = chess.Board(pos["fen"])
            predictions = predictor.predict_moves(board, top_k=3)

            print("   🧠 Magnus-style predictions:")
            for i, pred in enumerate(predictions[:3], 1):
                move = pred["move"]
                confidence = pred["confidence"]
                san = board.san(chess.Move.from_uci(move))
                print(f"      {i}. {san} ({move}) - {confidence:.3f} confidence")

        except Exception as e:
            print(f"   ❌ Error predicting for this position: {e}")


def show_model_info():
    """Display model information"""
    print("\n📊 Model Information")
    print("=" * 30)

    # Load config if available
    if Path("config.yaml").exists():
        with open("config.yaml", "r") as f:
            config = yaml.safe_load(f)

        print(f"Architecture: {config['model']['architecture']}")
        print(f"Version: {config['model']['version']}")
        print(f"Parameters: {config['training']['total_params']:,}")
        print(f"Vocabulary: {config['training']['vocab_size']} moves")
        print(
            f"Training time: {config['metrics']['training_time_minutes']:.1f} minutes"
        )
        print(f"Test accuracy: {config['metrics']['test_accuracy']:.4f}")
        print(f"Top-3 accuracy: {config['metrics']['test_top3_accuracy']:.4f}")
        print(f"Top-5 accuracy: {config['metrics']['test_top5_accuracy']:.4f}")

    # Load version info if available
    if Path("version.json").exists():
        with open("version.json", "r") as f:
            version = json.load(f)

        print(f"\nModel ID: {version['model_id']}")
        print(f"Timestamp: {version['timestamp']}")
        print(f"Hash: {version['model_hash'][:16]}...")


def main():
    """Main demo function"""
    print("🎯 Advanced Magnus Chess Model - Demo")
    print("🏆 Trained on Magnus Carlsen's games")
    print("=" * 60)

    # Show model info
    show_model_info()

    # Load the model
    predictor = load_model_from_hf()

    if predictor is None:
        print("Failed to load model. Please ensure all files are present.")
        return

    # Run demo predictions
    demo_predictions(predictor)

    print("\n" + "=" * 60)
    print("✨ Demo completed! Try your own positions with the predictor.")


if __name__ == "__main__":
    main()