Create app.py

app.py

@@ -0,0 +1,65 @@
+# #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)}%"