import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error

from tensorflow import keras
import sys

def normalize_data(data):
    return (data - np.min(data)) / (np.max(data) - np.min(data))


data = pd.read_csv("data_train", sep=',',  skip_blank_lines=True, nrows=1087, error_bad_lines=False,
                   names=["video_id", "last_trending_date", "publish_date", "publish_hour", "category_id",
                          "channel_title", "views", "likes", "dislikes", "comment_count"]).dropna()

X = data.loc[:,data.columns == "views"].astype(int)
y = data.loc[:,data.columns == "likes"].astype(int)

min_val_sub = np.min(X)
max_val_sub = np.max(X)
X = (X - min_val_sub) / (max_val_sub - min_val_sub)
print(min_val_sub)
print(max_val_sub)

min_val_like = np.min(y)
max_val_like = np.max(y)
y = (y - min_val_like) / (max_val_like - min_val_like)

print(min_val_like)
print(max_val_like)


model = keras.Sequential([
    keras.layers.Dense(512,input_dim = X.shape[1], activation='relu'),
    keras.layers.Dense(256, activation='relu'),
    keras.layers.Dense(256, activation='relu'),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(1,activation='linear'),
])

model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absolute_error'])

model.fit(X, y, epochs=int(sys.argv[1]), validation_split = 0.3)

model.save('model')