import sys
from tensorflow.keras import layers, Sequential
# from keras.layers import Flatten,Dense,Dropout, GlobalAveragePooling2D
from tensorflow.keras.optimizers import Adam
from tensorflow import convert_to_tensor
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from sacred.observers import FileStorageObserver, MongoObserver
from sacred import Experiment
from sacred.observers import MongoObserver
from datetime import datetime
import mlflow

with mlflow.start_run():
	learning_rate = float(sys.argv[1])

	movies_train = pd.read_csv('movies_train.csv')

	x_train = movies_train.copy()
	y_train = x_train.pop('rottentomatoes_audience_score')
	x_train.pop('Unnamed: 0')

	learning_rate = float(sys.argv[1])
	model = Sequential()
	model.add(layers.Input(shape=(22,)))
	model.add(layers.Dense(64))
	model.add(layers.Dense(64))
	model.add(layers.Dense(32))
	model.add(layers.Dense(1))
	
	mlflow.log_param("learning_rate", learning_rate)
	
	model.compile(loss='mean_absolute_error', optimizer=Adam(learning_rate))
	
	
	history = model.fit(
		x = convert_to_tensor(x_train, np.float32),
		y = y_train,
		verbose=0, epochs=99)
		
	loss = history.history['loss'][-1]
	
	mlflow.log_metric("Loss", loss)
	model.save('model_movies.h5')