main.py

import os
from src.data_preprocessor import DataPreprocessor
from src.model_builder import ModelBuilder
from src.model_trainer import ModelTrainer
from src.model_evaluator import ModelEvaluator
from src.predictor import Predictor
from src.visualizer import Visualizer

def main():
    model_filepath = './data/saved_model.keras'  # Updated path to use the Keras format

    try:
        # Data preprocessing
        preprocessor = DataPreprocessor(data_path='./data/dataset.csv')
        preprocessor.load_data()
        preprocessor.prepare_data()  # Ensure this method prepares data correctly
        preprocessor.split_data()

        # Model building
        model_builder = ModelBuilder()
        if os.path.exists(model_filepath):
            # Load the pre-trained model if it exists
            model_builder.model = ModelTrainer.load_model(model_filepath, preprocessor.x_train, preprocessor.y_train).model
            print("Loaded pre-trained model.")
        else:
            # Build and compile a new model
            model_builder.build_model()
            model_builder.compile_model()  # Ensure model is compiled

        # Model training
        trainer = ModelTrainer(model=model_builder.model, x_train=preprocessor.x_train, y_train=preprocessor.y_train)
        if not os.path.exists(model_filepath):
            # Train the model and save it, overwriting if it already exists
            trainer.train_model(epochs=500)
            trainer.save_model(model_filepath)
            print("Trained and saved new model.")

        # Model evaluation
        evaluator = ModelEvaluator(model=model_builder.model, x_test=preprocessor.x_test, y_test=preprocessor.y_test)
        evaluator.evaluate_model()

        # Visualization
        visualizer = Visualizer(data=preprocessor.data)
        visualizer.plot_time_vs_temperature()
        visualizer.plot_temperature_histogram()
        visualizer.plot_time_vs_temperature_line()
        visualizer.plot_temperature_vs_dc_power()
        visualizer.plot_temperature_vs_dc_power_line()
        visualizer.plot_scatter_temperature_vs_dc_power()
        visualizer.plot_dc_power_histogram()

        # Prediction
        predictor = Predictor(model=model_builder.model)
        try:
            temperature = float(input("Please give the Temperature value: "))
            predicted_power = predictor.predict(temperature)
            visualizer.plot_temperature_vs_dc_power_with_prediction(temperature,predicted_power)
            visualizer.plot_temperature_vs_dc_power_bar_with_prediction(temperature,predicted_power)
            visualizer.plot_dc_power_histogram_with_prediction(predicted_power)
        except ValueError:
            print("Invalid temperature value. Please enter a numeric value.")

    except FileNotFoundError:
        print("Error: The specified dataset file was not found.")
    except Exception as e:
        print(f"An unexpected error occurred: {e}")

if __name__ == "__main__":
    main()