#!/usr/bin/env python3
"""
Test CNN Integration

This script tests if the CNN adapter is working properly and identifies issues.
"""

import logging
import sys
import os
from datetime import datetime

# Setup logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

def test_cnn_adapter():
    """Test CNN adapter initialization and basic functionality"""
    try:
        logger.info("Testing CNN adapter initialization...")
        
        # Test 1: Import CNN adapter
        from core.enhanced_cnn_adapter import EnhancedCNNAdapter
        logger.info("✅ CNN adapter import successful")
        
        # Test 2: Initialize CNN adapter
        cnn_adapter = EnhancedCNNAdapter(checkpoint_dir="models/enhanced_cnn")
        logger.info("✅ CNN adapter initialization successful")
        
        # Test 3: Check adapter attributes
        logger.info(f"CNN adapter model: {cnn_adapter.model}")
        logger.info(f"CNN adapter device: {cnn_adapter.device}")
        logger.info(f"CNN adapter model_name: {cnn_adapter.model_name}")
        
        # Test 4: Check metrics tracking
        logger.info(f"Inference count: {cnn_adapter.inference_count}")
        logger.info(f"Training count: {cnn_adapter.training_count}")
        logger.info(f"Training data length: {len(cnn_adapter.training_data)}")
        
        # Test 5: Test simple training sample addition
        cnn_adapter.add_training_sample("ETH/USDT", "BUY", 0.1)
        logger.info(f"✅ Training sample added, new length: {len(cnn_adapter.training_data)}")
        
        # Test 6: Test training if we have enough samples
        if len(cnn_adapter.training_data) >= 2:
            # Add another sample to have minimum for training
            cnn_adapter.add_training_sample("ETH/USDT", "SELL", -0.05)
            
            # Try training
            training_result = cnn_adapter.train(epochs=1)
            logger.info(f"✅ Training successful: {training_result}")
            
            # Check if metrics were updated
            logger.info(f"Last training time: {cnn_adapter.last_training_time}")
            logger.info(f"Last training loss: {cnn_adapter.last_training_loss}")
            logger.info(f"Training count: {cnn_adapter.training_count}")
        else:
            logger.info("⚠️ Not enough training samples for training test")
        
        return True
        
    except Exception as e:
        logger.error(f"❌ CNN adapter test failed: {e}")
        import traceback
        traceback.print_exc()
        return False

def test_base_data_input():
    """Test BaseDataInput creation"""
    try:
        logger.info("Testing BaseDataInput creation...")
        
        # Test 1: Import BaseDataInput
        from core.data_models import BaseDataInput, OHLCVBar, COBData
        logger.info("✅ BaseDataInput import successful")
        
        # Test 2: Create sample OHLCV bars
        sample_bars = []
        for i in range(10):  # Create 10 sample bars
            bar = OHLCVBar(
                symbol="ETH/USDT",
                timestamp=datetime.now(),
                open=3500.0 + i,
                high=3510.0 + i,
                low=3490.0 + i,
                close=3505.0 + i,
                volume=1000.0,
                timeframe="1s"
            )
            sample_bars.append(bar)
        
        logger.info(f"✅ Created {len(sample_bars)} sample OHLCV bars")
        
        # Test 3: Create BaseDataInput
        base_data = BaseDataInput(
            symbol="ETH/USDT",
            timestamp=datetime.now(),
            ohlcv_1s=sample_bars,
            ohlcv_1m=sample_bars,
            ohlcv_1h=sample_bars,
            ohlcv_1d=sample_bars,
            btc_ohlcv_1s=sample_bars
        )
        
        logger.info("✅ BaseDataInput created successfully")
        
        # Test 4: Validate BaseDataInput
        is_valid = base_data.validate()
        logger.info(f"BaseDataInput validation: {is_valid}")
        
        # Test 5: Get feature vector
        feature_vector = base_data.get_feature_vector()
        logger.info(f"✅ Feature vector created, shape: {feature_vector.shape}")
        
        return base_data
        
    except Exception as e:
        logger.error(f"❌ BaseDataInput test failed: {e}")
        import traceback
        traceback.print_exc()
        return None

def test_cnn_prediction():
    """Test CNN prediction with BaseDataInput"""
    try:
        logger.info("Testing CNN prediction...")
        
        # Get CNN adapter and base data
        from core.enhanced_cnn_adapter import EnhancedCNNAdapter
        cnn_adapter = EnhancedCNNAdapter(checkpoint_dir="models/enhanced_cnn")
        
        base_data = test_base_data_input()
        if not base_data:
            logger.error("❌ Cannot test prediction without valid BaseDataInput")
            return False
        
        # Test prediction
        model_output = cnn_adapter.predict(base_data)
        logger.info(f"✅ Prediction successful: {model_output.predictions['action']} ({model_output.confidence:.3f})")
        
        # Check if metrics were updated
        logger.info(f"Inference count after prediction: {cnn_adapter.inference_count}")
        logger.info(f"Last inference time: {cnn_adapter.last_inference_time}")
        logger.info(f"Last prediction output: {cnn_adapter.last_prediction_output}")
        
        return True
        
    except Exception as e:
        logger.error(f"❌ CNN prediction test failed: {e}")
        import traceback
        traceback.print_exc()
        return False

def main():
    """Run all tests"""
    logger.info("🧪 Starting CNN Integration Tests")
    
    # Test 1: CNN Adapter
    if not test_cnn_adapter():
        logger.error("❌ CNN adapter test failed - stopping")
        return False
    
    # Test 2: CNN Prediction
    if not test_cnn_prediction():
        logger.error("❌ CNN prediction test failed - stopping")
        return False
    
    logger.info("✅ All CNN integration tests passed!")
    logger.info("🎯 The CNN adapter should now work properly in the dashboard")
    
    return True

if __name__ == "__main__":
    success = main()
    sys.exit(0 if success else 1)