Update app.py

app.py

@@ -12,7 +12,7 @@ def calculate_distance(lat1, lon1, lat2, lon2):
     coords_2 = (lat2, lon2)
     return geodesic(coords_1, coords_2).meters
 
-# Function to apply KNN and return Vunit values
+# 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]
@@ -24,7 +24,7 @@ def knn_predict(df, target_column, features_columns, k=5):
     # Fit the model
     knn.fit(X, y)
 
-    # Use the model to predict Vunit for the filtered_data
+    # Use the model to predict V_oferta for the filtered_data
     predictions = knn.predict(df[features_columns])
 
     return predictions
@@ -56,8 +56,9 @@ st.markdown(
     unsafe_allow_html=True
 )
 
+
 # Create a DataFrame with sample data
-data = pd.read_excel('ven_ter_fim_PEDÓ.xlsx')
+data = pd.read_excel('ven_fim_PEDÓ_nov_23.xlsx)
 
 # Initialize variables to avoid NameError
 selected_coords = 'Direcionada'
@@ -80,7 +81,7 @@ zoom_level = round(17 - np.log10(max_distance))
 
 # Set font to 'Quicksand' for title_html
 title_html = """
-    <style>
+        <style>
         @font-face {font-family: 'Quicksand';
         src: url('font/Quicksand-VariableFont_wght.ttf') format('truetype');
         }
@@ -91,7 +92,7 @@ title_html = """
     <span style='color: gray; font-size: 50px;'>aval</span>
     <span style='color: white; font-size: 50px;'>ia</span>
     <span style='color: gray; font-size: 50px;'>.NEXUS</span>
-"""
+    """
 
 # Set font to 'Quicksand' for factor_html
 factor_html = """
@@ -131,6 +132,9 @@ with st.sidebar:
     #st.title('avalia.se')
 
     st.sidebar.markdown(title_html, unsafe_allow_html=True)
+
+    # Add a dropdown for filtering "Tipo"
+    selected_tipo = st.selectbox('Filtrar por Tipo', data['Tipo'].unique())
     
     selected_coords = st.selectbox('Selecione o tipo de pesquisa', ['Ampla', 'Direcionada'])
 
@@ -193,10 +197,10 @@ st.markdown(f"""<style>
 
 # Check if KNN should be applied
 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
-    filtered_data['Predicted_Vunit'] = predicted_vunit
+    # Apply KNN and get predicted V_oferta values
+    predicted_V_oferta = knn_predict(filtered_data, 'V_oferta', ['latitude', 'longitude', 'Area'])  # 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():
@@ -205,7 +209,7 @@ with st.container():
     elif selected_coords == 'Ampla':
         st.map(data, zoom=zoom_level, use_container_width=True)
 
-# Display the predicted Vunit values if applicable
-if 'Predicted_Vunit' in filtered_data.columns:
+# 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:")
-    st.write(filtered_data[['latitude', 'longitude', 'Vunit', 'Predicted_Vunit']])
+    st.write(filtered_data[['latitude', 'longitude', 'V_oferta', 'Predicted_V_oferta']])