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#!/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()
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