Update app.py

app.py

@@ -12,19 +12,22 @@ def calculate_distance(lat1, lon1, lat2, lon2):
     coords_2 = (lat2, lon2)
     return geodesic(coords_1, coords_2).meters
 
-# Function to apply KNN and return V_oferta values
 def knn_predict(df, target_column, features_columns, k=5):
     # Separate features and target variable
     X = df[features_columns]
     y = df[target_column]
 
+    # Check if there is enough data for prediction
+    if len(X) < k:
+        return np.zeros(len(X))  # Return an array of zeros if there isn't enough data
+
     # Create KNN regressor
     knn = KNeighborsRegressor(n_neighbors=k)
 
     # Fit the model
     knn.fit(X, y)
 
-    # Use the model to predict V_oferta for the filtered_data
+    # Use the model to predict target_column for the filtered_data
     predictions = knn.predict(df[features_columns])
 
     return predictions
@@ -223,23 +226,18 @@ filtered_data['Predicted_target'] = predicted_target
 with st.container():
     st.map(filtered_data, zoom=zoom_level, use_container_width=True)
     st.write("Dados:", filtered_data)  # Debug: Print filtered_data
-    
-# Set the threshold (k) for the number of data points
-k_threshold = 5  # Adjust the threshold as needed
 
-# 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
+# Apply KNN and get predicted Predicted_target values
+predicted_target = knn_predict(filtered_data, 'Predicted_target', ['latitude', 'longitude', 'area_feature'])  # Update with your features
 
+# Check if there are predictions to display
+if 'Predicted_target' in filtered_data.columns and not np.all(predicted_target == 0):
     # 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:
 else:
     st.warning(f"Dados insuficientes para inferência do valor. Mínimo necessário: {k_threshold}")