from BitcoinPricePredictor import BitcoinPricePredictor
from tensorflow.keras.models import load_model
from sklearn.metrics import confusion_matrix

if __name__ == "__main__":
    model = load_model('models/model_2025-01-21_04-49-43.h5')
    predictor = BitcoinPricePredictor(model=model, db_path='databases/bitcoin_historical_data.db')

    missing_data = predictor.load_new_data_from_model()

    print(f"missing data {len(missing_data)}")
    if not missing_data.empty:
        predictions, reality = predictor.make_predictions_w_reality(missing_data)
        print(f"predictions {len(predictions)}")

        cm = confusion_matrix(reality, predictions[1:])
        print("Confusion Matrix:")
        print(cm)
    else:
        print("No new data found.")