Update app.py

app.py

@@ -48,9 +48,38 @@ scaler = MinMaxScaler(feature_range=(0,1))
 
 data_training_array = scaler.fit_transform(data_training)
 
-#load my model 
-model = load_model('stock_model.h5')
+x_train = []
+y_train = []
 
+for i in range(100, data_training_array.shape[0]):
+    x_train.append(data_training_array[i-100: i])
+    y_train.append(data_training_array[i, 0])
+
+x_train, y_train = np.array(x_train), np.array(y_train) 
+
+import tensorflow as tf
+from tensorflow import keras
+from keras.layers import Dense, Dropout, LSTM
+from keras.models import Sequential
+
+model = Sequential()
+model.add(LSTM(units = 50, activation = 'relu', return_sequences = True, 
+               input_shape = (x_train.shape[1], 1)))
+model.add(Dropout(0.2))
+
+model.add(LSTM(units = 60, activation = 'relu', return_sequences = True))
+model.add(Dropout(0.3))
+
+model.add(LSTM(units = 80, activation = 'relu', return_sequences = True))
+model.add(Dropout(0.4))
+
+model.add(LSTM(units = 120, activation = 'relu'))
+model.add(Dropout(0.5))
+
+model.add(Dense(units = 1))
+
+model.compile(optimizer='adam', loss = 'mean_squared_error', metrics='accuracy')
+model.fit(x_train, y_train, epochs = 10)
 
 
 #testing part