import json
from rtu.RTUAnomalizer import RTUAnomalizer
from rtu.RTUPipeline import RTUPipeline
import paho.mqtt.client as mqtt


        
def main():
    rtu_data_pipeline = RTUPipeline(scaler_path="rtu/models/scaler_1.pkl")
    rtu_anomalizer = RTUAnomalizer(
        prediction_model_path="rtu/models/lstm_4rtu_smooth_02.keras",
        clustering_model_paths=[
            "rtu/models/kmeans_model1.pkl",
            "rtu/models/kmeans_model2.pkl",
            "rtu/models/kmeans_model3.pkl",
            "rtu/models/kmeans_model4.pkl",
        ],
        num_inputs=rtu_data_pipeline.num_inputs,
        num_outputs=rtu_data_pipeline.num_outputs
    )
    
    def on_message(client, userdata, message):
        print(json.loads(message.payload.decode()))
        df_new, df_trans = rtu_data_pipeline.fit(message)
        out = rtu_anomalizer.predict(df_new, df_trans, rtu_data_pipeline.scaler)
        print(out)
    
    broker_address = "localhost"
    broker_port = 1883
    topic = "sensor_data"
    client = mqtt.Client(mqtt.CallbackAPIVersion.VERSION1)
    client.on_message = on_message
    client.connect(broker_address, broker_port)
    client.subscribe(topic)
    client.loop_forever()
    
    
if __name__=='__main__':
    # 
    main()