up

run.py

@@ -27,7 +27,9 @@ labels = dbscan.fit_predict(features_normalized)
 data['labels'] = labels
 
 # Split the data into training and testing sets for regression
-X_train, X_test, y_train, y_test = train_test_split(features_normalized, labels, test_size=0.2, random_state=42)
+X_train, X_test, y_train, y_test = train_test_split(
+    features_normalized, labels, test_size=0.2, random_state=42
+)
 
 model = keras.Sequential([
     layers.Dense(128, activation='relu', input_shape=(1,)),
@@ -43,19 +45,19 @@ model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mae'])
 # Train the updated model
 model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.2)
 
-# Streamlit App
-st.title('Predict Real Values with Streamlit')
+# Function to predict and display results
+def predict_values(input_data):
+    new_data_normalized = scaler.transform(np.array(input_data).reshape(-1, 1))
+    real_value_predictions = model.predict(new_data_normalized)
+    return real_value_predictions.flatten()
 
-# Input for new data
-st.sidebar.title('New Data')
-new_data_input = st.sidebar.text_area('Enter new data (comma-separated)', '2.8, 6.55, 1.1, 1.06, 1.88, 1.89, 2.36, 8.23, 1.76, 1.68, 1.44, 1.35, 2.56, 1.49, 3.03, 1.82, 1.69, 7.81, 3.8')
+# Streamlit UI
+st.title('Prediction App')
 
-# Convert input to numpy array
-new_data = np.array([float(x.strip()) for x in new_data_input.split(',')])
+st.write("Enter new data:")
+input_data = st.text_input("Enter comma-separated values:", value="2.8, 6.55, 1.1, 1.06, 1.88, 1.89, 2.36, 8.23, 1.76, 1.68, 1.44, 1.35, 2.56, 1.49, 3.03, 1.82, 1.69, 7.81, 3.8")
+input_data = [float(x.strip()) for x in input_data.split(',')]
 
-# Predict real values for new_data
-new_data_normalized = scaler.transform(new_data.reshape(-1, 1))
-real_value_predictions = model.predict(new_data_normalized)
-
-# Display predictions
-st.write("Predicted Real Values for New Data:", real_value_predictions.flatten())
+if st.button('Predict'):
+    predictions = predict_values(input_data)
+    st.write("Predicted Real Values for New Data:", predictions)