Update app.py

app.py

@@ -55,11 +55,11 @@ st.markdown(
 data = pd.read_excel('ven_ter_fim_PEDÓ.xlsx')
 
 # Initialize variables to avoid NameError
-selected_coords = 'Custom'
+selected_coords = 'Direcionada'
 radius_visible = True
 custom_address_initial = 'Av. Senador Alberto Pasqualini, 177 - Centro, Lajeado - RS, 95900-034'  # Initial custom address
-custom_lat = data['latitude'].mean()
-custom_lon = data['longitude'].mean()
+custom_lat = data['latitude'].median()
+custom_lon = data['longitude'].median()
 radius_in_meters = 1500
 filtered_data = data  # Initialize with the entire dataset
 
@@ -71,15 +71,15 @@ for index, row in data.iterrows():
         max_distance = distance
 
 # Calculate a zoom level based on the maximum distance
-zoom_level = round(15 - np.log10(max_distance))
+zoom_level = round(16 - np.log10(max_distance))
 
 # Create a sidebar for controls
 with st.sidebar:
     st.title('avalia.se')
 
-    selected_coords = st.selectbox('Selecione Coordenadas', ['Random', 'Custom'])
+    selected_coords = st.selectbox('Selecione o tipo de pesquisa', ['Ampla', 'Direcionada'])
 
-    if selected_coords == 'Custom':
+    if selected_coords == 'Direcionada':
         custom_address = st.text_input('Informe o endereço', custom_address_initial)
         radius_visible = True  # Show radius slider for custom coordinates
         # No need to initialize max_distance_all here
@@ -100,7 +100,7 @@ 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':
+    if selected_coords == 'Direcionada':
         zoom_level = st.slider('Nível de zoom', min_value=1, max_value=15, value=zoom_level)
     else:
         for index, row in data.iterrows():
@@ -119,7 +119,7 @@ with st.sidebar:
         radius_in_meters = st.slider('Selecione raio (em metros)', min_value=100, max_value=5000, value=1000)
 
 # Filter data based on the radius
-if selected_coords == 'Custom':
+if selected_coords == 'Direcionada':
     filtered_data = data[data.apply(lambda x: calculate_distance(x['latitude'], x['longitude'], custom_lat, custom_lon), axis=1) <= radius_in_meters]
     filtered_data = filtered_data.dropna()  # Drop rows with NaN values
 
@@ -132,7 +132,7 @@ st.markdown(f"""<style>
 </style>""", unsafe_allow_html=True)
 
 # Check if KNN should be applied
-if selected_coords == 'Custom' and radius_visible:
+if selected_coords == 'Direcionada' and radius_visible:
     # Apply KNN and get predicted Vunit values
     predicted_vunit = knn_predict(filtered_data, 'Vunit', ['latitude', 'longitude', 'Area'])  # Update with your features
     # Add predicted Vunit values to filtered_data
@@ -140,9 +140,9 @@ if selected_coords == 'Custom' and radius_visible:
 
 # Display the map and filtered_data
 with st.container():
-    if selected_coords == 'Custom':
+    if selected_coords == 'Direcionada':
         st.map(filtered_data, zoom=zoom_level, use_container_width=True)
-    elif selected_coords == 'Random':
+    elif selected_coords == 'Ampla':
         st.map(data, zoom=zoom_level, use_container_width=True)
 
 # Display the predicted Vunit values if applicable