import tensorflow as tf
import os
import pandas as pd
import numpy as np
import csv
from sklearn.model_selection import train_test_split

os.system("kaggle datasets download -d tamber/steam-video-games")
os.system("unzip -o steam-video-games.zip")

steam=pd.read_csv('steam-200k.csv',usecols=[0,1,2,3],names=['userId','game','behavior','hoursPlayed'])
steam.isnull().values.any()
steam['userId'] = steam.userId.astype(str)
purchaseCount = steam[steam["behavior"] != "play"]["game"].value_counts()
playCount = steam[steam["behavior"] != "purchase"]["game"].value_counts()

playerPurchaseCount = steam[steam["behavior"] != "play"]["userId"].value_counts()
playerPlayCount = steam[steam["behavior"] != "purchase"]["userId"].value_counts()

steam = steam[steam['behavior'] != 'purchase']
steam = steam.groupby("game").filter(lambda x: len(x)>10)
size=int(len(steam)/10)

meanGame = steam[steam["behavior"] != "purchase"].groupby("game").mean()
meanGame = meanGame.to_dict()
meanGame = meanGame['hoursPlayed']

purchaseCount = purchaseCount.to_dict()
playCount = playCount.to_dict()
playerPurchaseCount = playerPurchaseCount.to_dict()
playerPlayCount = playerPlayCount.to_dict()

steam['meanTime'] = 0;
steam['purchaseCount'] = 0;
steam['playCount'] = 0;
steam['playerPurchaseCount'] =0;
steam['playerPlayCount'] =0;
steam['playPercent'] =0;

for i in steam.index:
    steam.at[i,'meanTime'] = meanGame[steam.at[i,'game']]
    steam.at[i,'purchaseCount'] = purchaseCount[steam.at[i,'game']]
    steam.at[i,'playCount'] = playCount[steam.at[i,'game']]
    steam.at[i,'playerPurchaseCount'] = playerPurchaseCount[steam.at[i,'userId']]
    steam.at[i,'playerPlayCount'] = playerPlayCount[steam.at[i,'userId']]
    steam.at[i,'playPercent'] = playerPlayCount[steam.at[i,'userId']]/playerPurchaseCount[steam.at[i,'userId']]


steam_train, steam_test = train_test_split(steam, test_size=size, random_state=1, stratify=steam["game"])
steam_train, steam_dev = train_test_split(steam_train, test_size=size, random_state=1, stratify=steam_train["game"])

print(steam)

games = {}
for i in steam['game']:
    games[i] = 0

j=0
for key,game in games.items():
    games[key]=j
    j=j+1

for i in steam['game']:
    i = games[i]
    
invGames = {v: k for k, v in games.items()}

x_train = steam_train[['hoursPlayed','purchaseCount','playCount','playerPlayCount','playerPurchaseCount']]
y_train = steam_train['game']

x_test = steam_test[['hoursPlayed','purchaseCount','playCount','playerPlayCount','playerPurchaseCount']]
y_test = steam_test['game']

x_train = np.array(x_train)
y_train = np.array(y_train)
x_test = np.array(x_test)
y_test = np.array(y_test)

for i,j in enumerate(y_train):
    y_train[i] = games[j]
    
for i,j in enumerate(y_test):
    y_test[i] = games[j]



model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(5,1)),
  tf.keras.layers.Dense(256, activation='relu'),
  tf.keras.layers.Dropout(0.01),
  tf.keras.layers.Dense(1000, activation='softmax')
])



model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

y_train = np.array(y_train).astype(np.float32)
y_test = np.array(y_test).astype(np.float32)



model.fit(x_train, y_train, epochs=100)
model.evaluate(x_test, y_test)
prediction = model.predict(x_test)
classes_x=np.argmax(prediction,axis=1)

rows = []

for j,i in enumerate(classes_x):
    row = [invGames[i],invGames[y_test[j]]]
    rows.append(row)
with open('results.csv','w',encoding='UTF-8',newline='') as f:
        writer = csv.writer(f)
        writer.writerow(["predicted", "expected"])
        for row in rows:
            writer.writerow(row)