Update app.py

app.py

@@ -227,8 +227,18 @@ with st.container():
 # Set the threshold (k) for the number of data points
 k_threshold = 5  # Adjust the threshold as needed
 
-# Display the predicted Predicted_target values if applicable and above the threshold
+# Check if there is data for prediction
 if 'Predicted_target' in filtered_data.columns and len(filtered_data) > k_threshold:
+    # Determine which area feature to use for prediction
+    filtered_data['area_feature'] = np.where(filtered_data['Apriv'] != 0, filtered_data['Apriv'], filtered_data['Atotal'])
+
+    # Apply KNN and get predicted Predicted_target values
+    predicted_target = knn_predict(filtered_data, 'Predicted_target', ['latitude', 'longitude', 'area_feature'])  # Update with your features
+
+    # Add predicted Predicted_target values to filtered_data
+    filtered_data['Predicted_target'] = predicted_target
+
+    # Display the predicted Predicted_target values
     st.write("Valores (R$/m²) previstos com algoritmo KNN:")
     st.write(filtered_data[['Localização', 'Atotal', 'Apriv', 'Vunit_total', 'Vunit_priv', 'Predicted_target']])
 else: