Update app.py

app.py

@@ -17,63 +17,67 @@ from utils.helper import *
 # Streamlit interface
 st.title('Stock Forecasting App')
 
-stock = st.text_input('Enter Stock Ticker', 'AAPL')
-start_date = st.date_input('Start Date', dt.date(2020, 1, 1))
-end_date = st.date_input('End Date', dt.date.today())
+with st.sidebar:
+    stock = st.text_input('Enter Stock Ticker', 'AAPL')
+    start_date = st.date_input('Start Date', dt.date(2020, 1, 1))
+    end_date = st.date_input('End Date', dt.date.today())
+    num_of_epochs = st.number_input('Insert a number', step=1, min_value=2, max_value=100, value=10)
+
 look_back = 50
 
 if st.button('Train Models'):
-    data = download_data(stock, start_date, end_date)
-
-    # Setting 80 percent data for training
-    training_data_len = math.ceil(len(data) * .8)
-
-    # Splitting the dataset
-    train_data = data[:training_data_len].iloc[:,:5]
-    test_data = data[training_data_len:].iloc[:,:5]
-
-    # Selecting Open Price values
-    dataset_train = train_data.Close.values
-
-    # Reshaping 1D to 2D array
-    dataset_train = np.reshape(dataset_train, (-1,1)) 
-
-    # scaling dataset
-    scaler = MinMaxScaler(feature_range=(0,1))
-    scaled_train = scaler.fit_transform(dataset_train)
-
-    # Selecting Open Price values
-    dataset_test = test_data.Close.values
-
-    # Reshaping 1D to 2D array
-    dataset_test = np.reshape(dataset_test, (-1,1))
-
-    # Normalizing values between 0 and 1
-    scaled_test = scaler.fit_transform(dataset_test) 
-
-    # Split train and test
-    X_train, y_train = create_datasets(scaled_train, look_back)
-    X_test, y_test = create_datasets(scaled_test, look_back)
-
-    # Create models
-    rnn_model = create_rnn_model((look_back, 1))
-    lstm_model = create_lstm_model((look_back, 1))
-    gru_model = create_gru_model((look_back, 1))
-
-    # Training
-    rnn_model.fit(X_train, y_train, epochs=50, batch_size=1)
-    lstm_model.fit(X_train, y_train, epochs=50, batch_size=1)
-    gru_model.fit(X_train, y_train, epochs=50, batch_size=1)
-
-    # Prediction
-    y_rnn_pred = rnn_model.predict(X_test)
-    y_lstm_pred = lstm_model.predict(X_test)
-    y_gru_pred = gru_model.predict(X_test)
-
-    # Recover the price data
-    y_rnn_pred = scaler.inverse_transform(y_rnn_pred)
-    y_lstm_pred = scaler.inverse_transform(y_lstm_pred)
-    y_gru_pred = scaler.inverse_transform(y_gru_pred)
-
-    # Visualization
-    plot_predictions(train_data, test_data, y_rnn_pred, y_lstm_pred, y_gru_pred)
+    with st.spinner('Wait for it...'):
+        data = download_data(stock, start_date, end_date)
+    
+        # Setting 80 percent data for training
+        training_data_len = math.ceil(len(data) * .8)
+    
+        # Splitting the dataset
+        train_data = data[:training_data_len].iloc[:,:5]
+        test_data = data[training_data_len:].iloc[:,:5]
+    
+        # Selecting Open Price values
+        dataset_train = train_data.Close.values
+    
+        # Reshaping 1D to 2D array
+        dataset_train = np.reshape(dataset_train, (-1,1)) 
+    
+        # scaling dataset
+        scaler = MinMaxScaler(feature_range=(0,1))
+        scaled_train = scaler.fit_transform(dataset_train)
+    
+        # Selecting Open Price values
+        dataset_test = test_data.Close.values
+    
+        # Reshaping 1D to 2D array
+        dataset_test = np.reshape(dataset_test, (-1,1))
+    
+        # Normalizing values between 0 and 1
+        scaled_test = scaler.fit_transform(dataset_test) 
+    
+        # Split train and test
+        X_train, y_train = create_datasets(scaled_train, look_back)
+        X_test, y_test = create_datasets(scaled_test, look_back)
+    
+        # Create models
+        rnn_model = create_rnn_model((look_back, 1))
+        lstm_model = create_lstm_model((look_back, 1))
+        gru_model = create_gru_model((look_back, 1))
+    
+        # Training
+        rnn_model.fit(X_train, y_train, epochs=num_of_epochs, batch_size=1)
+        lstm_model.fit(X_train, y_train, epochs=num_of_epochs, batch_size=1)
+        gru_model.fit(X_train, y_train, epochs=num_of_epochs, batch_size=1)
+    
+        # Prediction
+        y_rnn_pred = rnn_model.predict(X_test)
+        y_lstm_pred = lstm_model.predict(X_test)
+        y_gru_pred = gru_model.predict(X_test)
+    
+        # Recover the price data
+        y_rnn_pred = scaler.inverse_transform(y_rnn_pred)
+        y_lstm_pred = scaler.inverse_transform(y_lstm_pred)
+        y_gru_pred = scaler.inverse_transform(y_gru_pred)
+    
+        # Visualization
+        plot_predictions(train_data, test_data, y_rnn_pred, y_lstm_pred, y_gru_pred)