Update app.py

app.py

@@ -6,12 +6,6 @@ from geopy.distance import geodesic
 import googlemaps
 from geopy.exc import GeocoderTimedOut
 
-# Function to calculate distance in meters between two coordinates
-def calculate_distance(lat1, lon1, lat2, lon2):
-    coords_1 = (lat1, lon1)
-    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
@@ -61,7 +55,6 @@ st.markdown(
 data = pd.read_excel('ven_fim_PEDÓ_nov_23.xlsx')
 
 # Initialize variables to avoid NameError
-selected_coords = 'Direcionada'
 radius_visible = True
 custom_address_initial = 'Centro, Lajeado - RS, Brazil'  # Initial custom address
 custom_lat = data['latitude'].median()
@@ -69,15 +62,8 @@ custom_lon = data['longitude'].median()
 radius_in_meters = 1500
 filtered_data = data  # Initialize with the entire dataset
 
-# Find the maximum distance between coordinates
-max_distance = 0
-for index, row in data.iterrows():
-    distance = calculate_distance(row['latitude'], row['longitude'], data['latitude'].mean(), data['longitude'].mean())
-    if distance > max_distance:
-        max_distance = distance
-
 # Calculate a zoom level based on the maximum distance
-zoom_level = round(17 - np.log10(max_distance))
+zoom_level = 14
 
 # Set font to 'Quicksand' for title_html
 title_html = """
@@ -129,8 +115,6 @@ evo_html = """
 
 # Create a sidebar for controls
 with st.sidebar:
-    #st.title('avalia.se')
-
     st.sidebar.markdown(title_html, unsafe_allow_html=True)
 
     # Add a dropdown for filtering "Tipo"
@@ -138,18 +122,8 @@ with st.sidebar:
 
     data_tipo = data[data['Tipo'] == selected_tipo]
     
-    selected_coords = st.selectbox('Selecione o tipo de pesquisa', ['Ampla', 'Direcionada'])
-
-    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
-    else:
-        custom_address = "Lajeado, Rio Grande do Sul, Brazil"  # Default address
-        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
+    custom_address = st.text_input('Informe o endereço', custom_address_initial)
+    radius_visible = True  # Show radius slider for custom coordinates
 
     # Geocode the custom address using the Google Maps API
     gmaps = googlemaps.Client(key='AIzaSyDoJ6C7NE2CHqFcaHTnhreOfgJeTk4uSH0')  # Replace with your API key
@@ -161,19 +135,7 @@ 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 == 'Direcionada':
-        zoom_level = st.slider('Nível de zoom', min_value=1, max_value=15, value=zoom_level)
-    else:
-        for index, row in data_tipo.iterrows():
-            distance_all = calculate_distance(row['latitude'], row['longitude'], data_tipo['latitude'].mean(), data_tipo['longitude'].mean())
-            if distance_all > max_distance_all:
-                max_distance_all = distance_all
-
-        # Calculate a zoom level based on the maximum distance of the entire dataset
-        zoom_level_all = round(15 - np.log10(max_distance_all))
-
-        # Slider for setting the zoom level based on the entire dataset
-        zoom_level = st.slider('Nível de zoom', min_value=1, max_value=15, value=zoom_level_all)
+    zoom_level = st.slider('Nível de zoom', min_value=1, max_value=15, value=zoom_level)
 
     # Conditionally render the radius slider
     if radius_visible:
@@ -228,10 +190,8 @@ with st.sidebar:
     st.sidebar.markdown(evo_html, unsafe_allow_html=True)
 
 
-# Filter data based on the radius
-if selected_coords == 'Direcionada':
-    filtered_data = data_tipo[data_tipo.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
+filtered_data = data_tipo[data_tipo.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
 
 # Add a custom CSS class to the map container
 st.markdown(f"""<style>
@@ -244,26 +204,16 @@ st.markdown(f"""<style>
 # Determine which area feature to use for prediction
 filtered_data['area_feature'] = np.where(filtered_data['Apriv'] != 0, filtered_data['Apriv'], filtered_data['Atotal'])
 
-# Check if KNN should be applied
-if selected_coords == 'Direcionada' and radius_visible:
-    # Apply KNN and get predicted V_oferta values
-    predicted_V_oferta = knn_predict(filtered_data, 'V_oferta', ['latitude', 'longitude', 'area_feature'])  # Update with your features
-    # Add predicted V_oferta values to filtered_data
-    filtered_data['Predicted_V_oferta'] = predicted_V_oferta
+# Apply KNN and get predicted V_oferta values
+predicted_V_oferta = knn_predict(filtered_data, 'V_oferta', ['latitude', 'longitude', 'area_feature'])  # Update with your features
+# Add predicted V_oferta values to filtered_data
+filtered_data['Predicted_V_oferta'] = predicted_V_oferta
 
 # Display the map and filtered_data
 with st.container():
-    if selected_coords == 'Direcionada':
-        st.write("Filtered Data:", filtered_data)  # Debug: Print filtered_data
-        st.write("Zoom Level:", zoom_level)  # Debug: Print zoom_level
-        st.write("Custom Coordinates:", custom_lat, custom_lon)  # Debug: Print custom coordinates
-        st.map(filtered_data, zoom=zoom_level, use_container_width=True)
-    elif selected_coords == 'Ampla':
-        st.write("Complete Data:", data)  # Debug: Print complete data
-        st.write("Zoom Level:", zoom_level)  # Debug: Print zoom_level
-        st.map(data, zoom=zoom_level, use_container_width=True)
-
-
+    st.map(filtered_data, zoom=zoom_level, use_container_width=True)
+    st.write("Dados:", filtered_data)  # Debug: Print filtered_data
+    
 # Display the predicted V_oferta values if applicable
 if 'Predicted_V_oferta' in filtered_data.columns:
     st.write("Valores (R$/m²) previstos com algoritmo KNN:")