Update app.py

app.py

@@ -2,6 +2,7 @@ import streamlit as st
 import pandas as pd
 import numpy as np
 import openpyxl
+from sklearn.neighbors import KNeighborsRegressor
 from geopy.distance import geodesic
 
 # Set wide mode
@@ -83,4 +84,41 @@ st.markdown(f"""<style>
 
 # Wrap the map in a container with the custom CSS class
 with st.container():
-    st.map(filtered_data, zoom=zoom_level, use_container_width=True)
+    st.map(filtered_data, zoom=zoom_level, use_container_width=True)
+
+# Function to apply KNN and return Vunit values
+def knn_predict(df, target_column, features_columns, k=5):
+    # Separate features and target variable
+    X = df[features_columns]
+    y = df[target_column]
+
+    # Create KNN regressor
+    knn = KNeighborsRegressor(n_neighbors=k)
+
+    # Fit the model
+    knn.fit(X, y)
+
+    # Use the model to predict Vunit for the filtered_data
+    predictions = knn.predict(filtered_data[features_columns])
+
+    return predictions
+
+# Features columns for KNN
+knn_features = ['latitude', 'longitude', 'Area']  # Add other relevant features
+
+# Check if KNN should be applied
+if selected_coords == 'Custom' and radius_visible:
+    # Apply KNN and get predicted Vunit values
+    predicted_vunit = knn_predict(data, 'Vunit', knn_features)
+
+    # Add predicted Vunit values to filtered_data
+    filtered_data['Predicted_Vunit'] = predicted_vunit
+
+# Display the map and filtered_data
+with st.container():
+    st.map(filtered_data, zoom=zoom_level, use_container_width=True)
+
+# Display the predicted Vunit values if applicable
+if 'Predicted_Vunit' in filtered_data.columns:
+    st.write("Predicted Vunit Values:")
+    st.write(filtered_data[['latitude', 'longitude', 'Vunit', 'Predicted_Vunit']])