import tensorflow as tf
import sys
from tf.keras import layers
# from keras.layers import Flatten,Dense,Dropout, GlobalAveragePooling2D
from tf.keras.optimizers import Adam
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error

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 = sys.argv[1]

model = tf.keras.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))

model.compile(loss='mean_absolute_error', optimizer=Adam(learning_rate))

model.fit(
    x = tf.convert_to_tensor(x_train, np.float32),
    y = y_train,
    verbose=0, epochs=99)
	
model.save('model_movies')