#! /usr/bin/python3
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense
from sklearn.metrics import accuracy_score, classification_report
import pandas as pd
from sklearn.model_selection import train_test_split
import numpy as np
import requests
url = 'https://git.wmi.amu.edu.pl/s434695/ium_434695/raw/commit/2301fb86e434734376f73503307a8f3255a75cc6/vgsales.csv'
r = requests.get(url, allow_redirects=True)

open('vgsales.csv', 'wb').write(r.content)
df = pd.read_csv('vgsales.csv')



def regression_model():
    model = Sequential()
    model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
    model.add(Dense(64,activation = "relu"))
    model.add(Dense(1,activation = "relu"))
    
    model.compile(optimizer = "adam", loss = "mean_squared_error")
    return model

df['Nintendo'] = df['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
df = df.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
df

y = df.Nintendo

df=((df-df.min())/(df.max()-df.min()))

x = df.drop(['Nintendo'],axis = 1)

x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)

model = regression_model()
model.fit(x_train, y_train, epochs = 600, verbose = 1)

y_pred = model.predict(x_test)

y_pred[:5]

y_pred = np.around(y_pred, decimals=0)

y_pred[:5]

print(accuracy_score(y_test, y_pred))

print(classification_report(y_test,y_pred)) 

pd.DataFrame(y_pred).to_csv("preds.csv")