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

model = keras.models.load_model('model')
data = pd.read_csv("data_dev", sep=',', error_bad_lines=False,
                   skip_blank_lines=True, nrows=527, names=["video_id", "last_trending_date",
                                                                        "publish_date", "publish_hour", "category_id",
                                                                        "channel_title", "views", "likes", "dislikes",
                                                                        "comment_count"]).dropna()
X_test = data.loc[:,data.columns == "views"].astype(int)
y_test = data.loc[:,data.columns == "likes"].astype(int)

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

min_val_like = np.min(y_test)
max_val_like = np.max(y_test)
print(min_val_like)
print(max_val_like)

prediction = model.predict(X_test)

prediction_denormalized = []
for pred in prediction:
    denorm = pred[0] * (max_val_like[0] - min_val_like[0]) + min_val_like[0]
    prediction_denormalized.append(denorm)

f = open("predictions.txt", "w")
for (pred, test) in zip(prediction_denormalized, y_test.values):
    f.write("predicted: %s expected: %s\n" % (str(pred), str(test[0])))

error = mean_squared_error(y_test, prediction_denormalized)
print(error)