import mlflow
import mlflow.keras
import pandas as pd
import numpy as np
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
from sklearn.preprocessing import MinMaxScaler
import sys

# Parameters from the command line
epochs = int(sys.argv[1])
batch_size = int(sys.argv[2])

mlflow.start_run()

train_data = pd.read_csv('./data/car_prices_train.csv')
train_data.dropna(inplace=True)
y_train = train_data['sellingprice'].astype(np.float32)
X_train = train_data[['year', 'condition', 'transmission']]

scaler_x = MinMaxScaler()
X_train['condition'] = scaler_x.fit_transform(X_train[['condition']])

scaler_y = MinMaxScaler()
y_train = scaler_y.fit_transform(y_train.values.reshape(-1, 1))
X_train = pd.get_dummies(X_train, columns=['transmission'])

model = Sequential([Dense(64, activation='relu'), Dense(32, activation='relu'), Dense(1)])
model.compile(optimizer='adam', loss='mean_squared_error')

# Training the model with MLflow tracking
model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size)

mlflow.keras.log_model(model, "model")

mlflow.log_param("epochs", epochs)
mlflow.log_param("batch_size", batch_size)

mlflow.end_run()