Update app.py

app.py

@@ -1,24 +1,33 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
-import seaborn as sns
-import matplotlib.pyplot as plt
 from sklearn.neighbors import KNeighborsRegressor
 from geopy.distance import geodesic
 import googlemaps
 from geopy.exc import GeocoderTimedOut
 from streamlit_folium import st_folium
-from streamlit_folium import folium_static
 import folium
-
-# Function to plot heatmap
-def plot_heatmap(data, title):
-    plt.figure(figsize=(8, 6))
-    sns.heatmap(data, annot=True, cmap="YlGnBu", fmt=".2f", linewidths=.5)
-    plt.title(title)
-    plt.xlabel("Longitude")
-    plt.ylabel("Latitude")
-    st.pyplot()
+from branca.colormap import LinearColormap
+
+# Function to add heatmap layer to folium map
+def add_heatmap_layer(map_obj, data, column_name, colormap_name, radius=15):
+    heat_data = data[['latitude', 'longitude', column_name]].dropna()
+    heat_layer = folium.FeatureGroup(name=f'Heatmap - {column_name}')
+
+    cmap = LinearColormap(colors=['blue', 'white', 'red'], vmin=heat_data[column_name].min(), vmax=heat_data[column_name].max())
+
+    for index, row in heat_data.iterrows():
+        folium.CircleMarker(
+            location=[row['latitude'], row['longitude']],
+            radius=radius,
+            fill=True,
+            fill_color=cmap(row[column_name]),
+            fill_opacity=0.7,
+            color='black',
+            weight=0.5,
+        ).add_to(heat_layer)
+
+    heat_layer.add_to(map_obj)
     
 # Function to calculate distance in meters between two coordinates
 def calculate_distance(lat1, lon1, lat2, lon2):
@@ -246,9 +255,26 @@ predicted_target = knn_predict(filtered_data, 'target_column', ['latitude', 'lon
 # Add predicted target values to filtered_data
 filtered_data['Predicted_target'] = predicted_target
 
+# Display the map and filtered_data
+#with st.container():
+    #st.map(filtered_data, zoom=zoom_level, use_container_width=True)
+    #st.write("Dados:", filtered_data)  # Debug: Print filtered_data
+
 # Display the map and filtered_data
 with st.container():
-    st.map(filtered_data, zoom=zoom_level, use_container_width=True)
+    folium_map = folium.Map(location=[custom_lat, custom_lon], zoom_start=zoom_level, control_scale=True)
+    
+    # Add heatmap layers for 'Valor_Urb', 'Valor_Eqp', and 'RENDA'
+    add_heatmap_layer(folium_map, filtered_data, 'Valor_Urb', 'RdBu_r')
+    add_heatmap_layer(folium_map, filtered_data, 'Valor_Eqp', 'RdBu_r')
+    add_heatmap_layer(folium_map, filtered_data, 'RENDA', 'RdBu_r')
+
+    # Add layer control
+    folium.LayerControl().add_to(folium_map)
+
+    # Display the map
+    folium_static(folium_map)
+
     st.write("Dados:", filtered_data)  # Debug: Print filtered_data
 
 k_threshold = 5
@@ -286,14 +312,4 @@ if 'Predicted_target' in filtered_data.columns and not np.all(predicted_target =
     st.write(f"Valor médio (Reais/m²) para as características selecionadas: ${mean_value:.2f}$ Reais")
     st.write(f"Os valores podem variar entre ${lower_bound:.2f}$ e ${higher_bound:.2f}$ Reais, dependendo das características dos imóveis.")
 else:
-    st.warning(f"**Dados insuficientes para inferência do valor. Mínimo necessário:** {k_threshold}")
-
-# Plot heatmaps for 'Valor_Urb', 'Valor_Eqp', and 'RENDA'
-st.subheader("Mapa de Calor para 'Valor_Urb'")
-plot_heatmap(filtered_data.pivot_table(index='latitude', columns='longitude', values='Valor_Urb', aggfunc='mean'), 'Valor_Urb')
-
-st.subheader("Mapa de Calor para 'Valor_Eqp'")
-plot_heatmap(filtered_data.pivot_table(index='latitude', columns='longitude', values='Valor_Eqp', aggfunc='mean'), 'Valor_Eqp')
-
-st.subheader("Mapa de Calor para 'RENDA'")
-plot_heatmap(filtered_data.pivot_table(index='latitude', columns='longitude', values='RENDA', aggfunc='mean'), 'RENDA')
+    st.warning(f"**Dados insuficientes para inferência do valor. Mínimo necessário:** {k_threshold}")