Update app.py

app.py

@@ -60,34 +60,11 @@ for i in range(100, data_training_arr.shape[0]):
     y_train.append(data_training_arr[i, 0])
 x_train, y_train = np.array(x_train), np.array(y_train)
 
-from keras.layers import Dense, Dropout, LSTM
-from keras.models import Sequential
-
-model = Sequential()
-model.add(LSTM(units=50, activation="linear", return_sequences=True, input_shape = (x_train.shape[1],1)))
-model.add(Dropout(0.2))
-
-model.add(LSTM(units=60, activation="linear", return_sequences=True))
-model.add(Dropout(0.3))
-
-model.add(LSTM(units=80, activation="linear", return_sequences=True))
-model.add(Dropout(0.4))
-
-model.add(LSTM(units=120, activation="linear"))
-model.add(Dropout(0.5))
-
-model.add(Dense(units=1))
-
-model.compile(optimizer="adam", loss="mean_squared_error")
-model.fit(x_train, y_train, epochs=50)
-
-model.save('keras_model1.h5')
-
 #Load the model
-#model = load_model("keras_model1.h5")
+model = load_model("keras_model1.h5")
 
 past_100_days = data_training.tail(100)
-final_test_df = past_100_days._append(data_testing, ignore_index=True)
+final_test_df=pd.concat([past_100_days,data_testing],ignore_index=True)
 print("Final_test_df")
 print(final_test_df)
 input_data = scaler.fit_transform(final_test_df)