Update app.py

app.py

@@ -105,8 +105,25 @@ with st.sidebar:
         radius_visible = False  # Hide radius slider for random coordinates
         max_distance_all = 0  # Initialize max_distance_all here
 
-        max_distance_all = 0  # Initialize max_distance_all here
+    # Slider for setting the radius
+    if radius_visible:
+        radius_in_meters = st.slider('Selecione raio (em metros)', min_value=100, max_value=5000, value=1000)
+
+        # Calculate a zoom level based on the radius
+        radius_zoom_mapping = {
+            500: 15,
+            1000: 14,
+            2000: 13,
+            4000: 12,
+            5000: 11
+        }
+
+        # Find the closest radius in the mapping
+        closest_radius = min(radius_zoom_mapping.keys(), key=lambda x: abs(x - radius_in_meters))
 
+        # Set the zoom level based on the mapping
+        zoom_level = radius_zoom_mapping[closest_radius]
+    
     # Geocode the custom address using the Google Maps API
     gmaps = googlemaps.Client(key='AIzaSyDoJ6C7NE2CHqFcaHTnhreOfgJeTk4uSH0')  # Replace with your API key
 
@@ -117,9 +134,9 @@ with st.sidebar:
         st.error("Erro: Não foi possível geocodificar o endereço fornecido. Por favor, verifique e tente novamente.")
 
     # Slider for setting the zoom level
-    if selected_coords == 'Custom':
-        # The zoom level slider is no longer needed if radius is used for zoom adjustment
-        st.markdown("O nível de zoom é ajustado automaticamente com base no raio.")
+    if selected_coords == 'Custom' and not radius_visible:
+        # Use the zoom level slider based on the entire dataset
+        zoom_level = st.slider('Nível de zoom', min_value=1, max_value=15, value=zoom_level_all)
     else:
         for index, row in data.iterrows():
             distance_all = calculate_distance(row['latitude'], row['longitude'], data['latitude'].mean(), data['longitude'].mean())
@@ -166,4 +183,4 @@ with st.container():
 # Display the predicted Vunit values if applicable
 if 'Predicted_Vunit' in filtered_data.columns:
     st.write("Valores (R$/m²) previstos com algoritmo KNN:")
-    st.write(filtered_data[['latitude', 'longitude', 'Vunit', 'Predicted_Vunit']])
+    st.write(filtered_data[['latitude', 'longitude', 'Vunit', 'Predicted_Vunit']])