import sys

import mlflow
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
from mlflow.models.signature import infer_signature
import tensorflow as tf
tf.config.set_visible_devices([], 'GPU')
mlflow.set_experiment("s444380")


epochs = int(sys.argv[1])


def main(epochs):
    mlflow.log_param("epochs", epochs)
    mlflow.log_param("test", 100)

    # Read and split data
    train_data = pd.read_csv("crime_train.csv")
    val_data = pd.read_csv("crime_dev.csv")
    test_data = pd.read_csv("crime_test.csv")

    x_columns = ["DISTRICT", "STREET", "YEAR", "MONTH", "DAY_OF_WEEK", "HOUR", "Lat", "Long"]
    y_column = "OFFENSE_CODE_GROUP"

    x_train = train_data[x_columns]
    y_train = train_data[y_column]
    x_val = val_data[x_columns]
    y_val = val_data[y_column]
    x_test = test_data[x_columns]
    y_test = test_data[y_column]

    num_categories = len(y_train.unique())
    num_features = len(x_columns)

    # Train label encoders for categorical data
    encoder_y = LabelEncoder()
    encoder_day = LabelEncoder()
    encoder_dist = LabelEncoder()
    encoder_street = LabelEncoder()
    encoder_y.fit(y_train)
    encoder_day.fit(x_train["DAY_OF_WEEK"])
    encoder_dist.fit(x_train["DISTRICT"])
    encoder_street.fit(pd.concat([x_val["STREET"], x_test["STREET"], x_train["STREET"]], axis=0))


    # Encode train categorical data
    y_train = encoder_y.transform(y_train)
    x_train["DAY_OF_WEEK"] = encoder_day.transform(x_train["DAY_OF_WEEK"])
    x_train["DISTRICT"] = encoder_dist.transform(x_train["DISTRICT"])
    x_train["STREET"] = encoder_street.transform(x_train["STREET"])

    # Encode train categorical data
    y_val = encoder_y.transform(y_val)
    x_val["DAY_OF_WEEK"] = encoder_day.transform(x_val["DAY_OF_WEEK"])
    x_val["DISTRICT"] = encoder_dist.transform(x_val["DISTRICT"])
    x_val["STREET"] = encoder_street.transform(x_val["STREET"])

    # Encode train categorical data
    y_test = encoder_y.transform(y_test)
    x_test["DAY_OF_WEEK"] = encoder_day.transform(x_test["DAY_OF_WEEK"])
    x_test["DISTRICT"] = encoder_dist.transform(x_test["DISTRICT"])
    x_test["STREET"] = encoder_street.transform(x_test["STREET"])

    # Define model
    model = Sequential()
    model.add(Dense(32, activation='relu', input_dim=num_features))
    model.add(Dense(64, activation='relu'))
    model.add(Dense(128, activation='relu'))
    model.add(Dense(num_categories, activation='softmax'))
    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'sparse_categorical_accuracy'])

    # Train model
    history = model.fit(x_train, y_train, epochs=int(epochs), validation_data=(x_val, y_val))

    # Make predictions
    y_pred = model.predict(x_test)
    output = [np.argmax(pred) for pred in y_pred]
    output_text = encoder_y.inverse_transform(list(output))

    # Save predictions
    data_to_save = pd.concat([test_data[x_columns], test_data[y_column]], axis = 1)
    data_to_save["PREDICTED"] = output_text
    data_to_save.to_csv("out.csv")

    # Save model
    model.save("model")
    signature = infer_signature(x_train, y_train)
    input_example = np.array([x_test.values[0]])
    mlflow.keras.log_model(model, "model", signature=signature, input_example=input_example)

    # Log metrics
    mlflow.log_param("test", 33)
    mlflow.log_metric("loss", history.history["loss"])
    mlflow.log_metric("accuracy", history.history["accuracy"])
    mlflow.log_metric("val_loss", history.history["val_loss"])
    mlflow.log_metric("val_accuracy", history.history["val_accuracy"])