Delete app.py

app.py

@@ -1,65 +0,0 @@
-# #1
-import pandas as pd
-import numpy as np
-from datasets import load_dataset
-from tensorflow import keras
-from keras.layers import Dense, Dropout, BatchNormalization
-from keras.optimizers import Adam
-from keras.callbacks import EarlyStopping
-from sklearn.model_selection import train_test_split
-
-# #2
-# Загрузка данных
-heart = load_dataset("MaxJalo/CardioAI", split = 'train')
-
-# #3
-data = pd.DataFrame(heart,
-                    columns=["age", "gender", "height", "weight", "ap_hi", "ap_lo", "cholesterol", "gluc", "smoke",
-                             "alco", "active", 'cardio'])
-
-# #4
-X_for_train = data.drop(['cardio'], axis=1).values
-X_min = np.min(X_for_train, axis=0)
-X_max = np.max(X_for_train, axis=0)
-X_normalized = (X_for_train - X_min) / (X_max - X_min)
-
-y_normalized = data['cardio'].values
-
-X_train, X_test, y_train, y_test = train_test_split(X_normalized, y_normalized, test_size=0.1, random_state=77)
-print(X_train)
-
-# #5
-model = Sequential()
-model.add(Dense(1, input_dim=X_train.shape[1], activation='linear', kernel_regularizer='l2'))
-# model.add(Dense(16, activation='elu', kernel_regularizer='l2'))
-# model.add(Dense(16, activation='elu', kernel_regularizer='l2'))
-model.add(Dense(1, activation='linear'))
-
-model.compile(optimizer='adam', loss='mse')
-
-# #6
-early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)
-
-history = model.fit(X_train, y_train, epochs=100, batch_size=50, validation_split=0.1, callbacks=[early_stopping],
-                    verbose=1)
-
-# #8
-test_loss = model.evaluate(X_test, y_test)
-print(f'Test loss (MSE): {test_loss}')
-
-
-# #9
-def webai(user_input):
-    user_input_clear = user_input
-    input_data = [user_input_clear]
-    input_data_scaled = (input_data - X_min) / (X_max - X_min)
-    print(input_data_scaled)
-    # Получаем предсказание от модели
-    predicted_result_scaled = model.predict(input_data_scaled)
-    print(predicted_result_scaled[0][0] * 100)
-    # 35 0 190 75 120 80 1 1 0 0 1
-    # 35 0 170 90 130 90 1 1 0 0 0
-    # 39 0 156 45 110 80 2 1 0 0 0
-    # 47 1 168 87 120 80 2 1 1 1 1
-    # 37 0 185 75 120 80 2 1 1 1 0
-    return f"{round(predicted_result_scaled[0][0] * 100, 2)}%"