import tensorflow as tf
import pandas as pd

train_data = pd.read_csv('olympics-124-years-datasettill-2020/Athletes_winter_games.csv')

X_train = train_data[['Sex']]
y_train = train_data['Medal']

X_train.loc[:, 'Sex'] = X_train['Sex'].map({'M': 0, 'F': 1})
y_train = y_train.map({'Bronze': 0, 'Silver': 1, 'Gold': 1}).fillna(0).astype('float32')

X_train = X_train.astype('float32')
y_train = y_train.astype('float32')

model = tf.keras.Sequential([
    tf.keras.layers.Dense(16, activation='relu', input_shape=(X_train.shape[1],)),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

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

model.fit(X_train, y_train, epochs=10)

model.save('model.h5')


test_data = pd.read_csv('olympics-124-years-datasettill-2020/Athletes_winter_games.csv')

test_data.loc[:, 'Sex'] = test_data['Sex'].map({'M': 0, 'F': 1})
test_data = test_data[['Sex']].astype('float32')

predictions = model.predict(test_data)

pd.DataFrame(predictions).to_csv('predictions.csv', index=False, header=False)