Update app.py

app.py

@@ -202,7 +202,10 @@ predicted_target = knn_predict(filtered_data, 'target_column', ['latitude', 'lon
 filtered_data['Predicted_target'] = predicted_target
 
 
-with st.container():
+# Set custom width for columns
+tab1, tab2, tab3, tab4 = st.tabs(["Mapa", "Planilha", "Variáveis", "Inteligência Artificial"])
+
+with tab1:
     # Define a PyDeck view state for the initial map view
     view_state = pdk.ViewState(latitude=filtered_data['latitude'].mean(), longitude=filtered_data['longitude'].mean(), zoom=zoom_level)
 
@@ -221,6 +224,8 @@ with st.container():
     # Display the map in Streamlit
     st.pydeck_chart(deck_map)
     #st.map(filtered_data, zoom=zoom_level, use_container_width=True)
+
+with tab2:
     st.write("Dados:", filtered_data)  # Debug: Print filtered_data
 
     if st.button('Baixar planilha'):
@@ -247,7 +252,7 @@ with st.container():
         download_placeholder = st.empty()
         download_placeholder.markdown(href, unsafe_allow_html=True)
 
-    
+with tab3:    
     folium_layermap = folium.Map(location=[custom_lat, custom_lon], tiles="Cartodb Positron", zoom_start=14)
 
     # Add heatmap layers for 'Valor_Urb', 'Valor_Eqp', and 'RENDA'
@@ -261,39 +266,40 @@ with st.container():
     # Display the map using st_folium
     st_folium(folium_layermap, width=1200, height=400)
 
-k_threshold = 5
+with tab4:
+    k_threshold = 5
 
-# Function to perform bootstrap on the predicted target values
-def bootstrap_stats(bound_data, num_samples=1000):
-    # Reshape the predicted_target array
-    bound_data = np.array(bound_data).reshape(-1, 1)
+    # Function to perform bootstrap on the predicted target values
+    def bootstrap_stats(bound_data, num_samples=1000):
+        # Reshape the predicted_target array
+        bound_data = np.array(bound_data).reshape(-1, 1)
 
-    # Bootstrap resampling
-    bootstrapped_means = []
-    for _ in range(num_samples):
-        bootstrap_sample = np.random.choice(bound_data.flatten(), len(bound_data), replace=True)
-        bootstrapped_means.append(np.mean(bootstrap_sample))
+        # Bootstrap resampling
+        bootstrapped_means = []
+        for _ in range(num_samples):
+            bootstrap_sample = np.random.choice(bound_data.flatten(), len(bound_data), replace=True)
+            bootstrapped_means.append(np.mean(bootstrap_sample))
 
-    # Calculate lower and higher bounds
-    lower_bound = np.percentile(bootstrapped_means, 16.)
-    higher_bound = np.percentile(bootstrapped_means, 84.)
+        # Calculate lower and higher bounds
+        lower_bound = np.percentile(bootstrapped_means, 16.)
+        higher_bound = np.percentile(bootstrapped_means, 84.)
 
-    return lower_bound, higher_bound
+        return lower_bound, higher_bound
 
-# Apply KNN and get predicted Predicted_target values
-predicted_target = knn_predict(filtered_data, 'Predicted_target', ['latitude', 'longitude', 'area_feature'])
+    # Apply KNN and get predicted Predicted_target values
+    predicted_target = knn_predict(filtered_data, 'Predicted_target', ['latitude', 'longitude', 'area_feature'])
 
-# Check if there are predictions to display
-if 'Predicted_target' in filtered_data.columns and not np.all(predicted_target == 0):
+    # Check if there are predictions to display
+    if 'Predicted_target' in filtered_data.columns and not np.all(predicted_target == 0):
 
-    # Apply bootstrap - bounds
-    lower_bound, higher_bound = bootstrap_stats(filtered_data['target_column'])
+        # Apply bootstrap - bounds
+        lower_bound, higher_bound = bootstrap_stats(filtered_data['target_column'])
 
-    mean_value = np.mean(filtered_data['Predicted_target'])
+        mean_value = np.mean(filtered_data['Predicted_target'])
 
-    # Display the results with custom styling
-    st.markdown("## **Algoritmo KNN (K-nearest neighbors)**")
-    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}")
+        # Display the results with custom styling
+        st.markdown("## **Algoritmo KNN (K-nearest neighbors)**")
+        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}")