Update app.py

app.py

@@ -1,10 +1,32 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
-import openpyxl
 from sklearn.neighbors import KNeighborsRegressor
 from geopy.distance import geodesic
 
+# 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 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(df[features_columns])
+
+    return predictions
+
 # Set wide mode
 st.set_page_config(layout="wide")
 
@@ -30,11 +52,13 @@ st.markdown(
 # Create a DataFrame with sample data
 data = pd.read_excel('ven_ter_fim_PEDÓ.xlsx')
 
-# 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
+# Initialize variables to avoid NameError
+selected_coords = 'Custom'
+radius_visible = True
+custom_lat = -29.45086
+custom_lon = -51.9847
+radius_in_meters = 1000
+filtered_data = data  # Initialize with the entire dataset
 
 # Find the maximum distance between coordinates
 max_distance = 0
@@ -68,17 +92,10 @@ with st.sidebar:
     if radius_visible:
         radius_in_meters = st.slider('Selecione raio (em metros)', min_value=100, max_value=5000, value=1000)
 
-if selected_coords == 'Custom' and radius_visible:
-    # Filter data based on the radius and drop NaN values
+# Filter data based on the radius
+if selected_coords == 'Custom':
     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()
-
-    # Apply KNN and get predicted Vunit values
-    predicted_vunit = knn_predict(filtered_data, 'Vunit', knn_features)
-
-    # Add predicted Vunit values to filtered_data
-    filtered_data['Predicted_Vunit'] = predicted_vunit
-
+    filtered_data = filtered_data.dropna()  # Drop rows with NaN values
 
 # Add a custom CSS class to the map container
 st.markdown(f"""<style>
@@ -88,35 +105,10 @@ st.markdown(f"""<style>
 }}
 </style>""", unsafe_allow_html=True)
 
-# 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)
-
-# 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)
-
+    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
 
@@ -128,4 +120,4 @@ with st.container():
 # 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']])
+    st.write(filtered_data[['latitude', 'longitude', 'Vunit', 'Predicted_Vunit']])