import streamlit as st import pandas as pd import numpy as np from sklearn.neighbors import KNeighborsRegressor from geopy.distance import geodesic import googlemaps from geopy.exc import GeocoderTimedOut # 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 V_oferta 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 V_oferta for the filtered_data predictions = knn.predict(df[features_columns]) return predictions # Set wide mode st.set_page_config(layout="wide") # Set dark theme st.markdown( """ """, unsafe_allow_html=True ) # Create a DataFrame with sample data data = pd.read_excel('ven_fim_PEDÓ_nov_23.xlsx') # Initialize variables to avoid NameError selected_coords = 'Direcionada' radius_visible = True custom_address_initial = 'Centro, Lajeado - RS, Brazil' # Initial custom address custom_lat = data['latitude'].median() custom_lon = data['longitude'].median() radius_in_meters = 1500 filtered_data = data # Initialize with the entire dataset # Find the maximum distance between coordinates max_distance = 0 for index, row in data.iterrows(): distance = calculate_distance(row['latitude'], row['longitude'], data['latitude'].mean(), data['longitude'].mean()) if distance > max_distance: max_distance = distance # Calculate a zoom level based on the maximum distance zoom_level = round(17 - np.log10(max_distance)) # Set font to 'Quicksand' for title_html title_html = """ aval ia .NEXUS """ # Set font to 'Quicksand' for factor_html factor_html = """ aval ia .FACTOR """ # Set font to 'Quicksand' for evo_html evo_html = """ aval ia .EVO """ # Create a sidebar for controls 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()) data_tipo = data[data['Tipo'] == selected_tipo] selected_coords = st.selectbox('Selecione o tipo de pesquisa', ['Ampla', 'Direcionada']) if selected_coords == 'Direcionada': custom_address = st.text_input('Informe o endereço', custom_address_initial) radius_visible = True # Show radius slider for custom coordinates # No need to initialize max_distance_all here else: custom_address = "Lajeado, Rio Grande do Sul, Brazil" # Default address radius_visible = False # Hide radius slider for random coordinates max_distance_all = 0 # Initialize max_distance_all here max_distance_all = 0 # Initialize max_distance_all here # Geocode the custom address using the Google Maps API gmaps = googlemaps.Client(key='AIzaSyDoJ6C7NE2CHqFcaHTnhreOfgJeTk4uSH0') # Replace with your API key try: location = gmaps.geocode(custom_address)[0]['geometry']['location'] custom_lat, custom_lon = location['lat'], location['lng'] except (IndexError, GeocoderTimedOut): st.error("Erro: Não foi possível geocodificar o endereço fornecido. Por favor, verifique e tente novamente.") # Slider for setting the zoom level if selected_coords == 'Direcionada': zoom_level = st.slider('Nível de zoom', min_value=1, max_value=15, value=zoom_level) else: for index, row in data_tipo.iterrows(): distance_all = calculate_distance(row['latitude'], row['longitude'], data_tipo['latitude'].mean(), data_tipo['longitude'].mean()) if distance_all > max_distance_all: max_distance_all = distance_all # Calculate a zoom level based on the maximum distance of the entire dataset zoom_level_all = round(15 - np.log10(max_distance_all)) # Slider for setting the zoom level based on the entire dataset zoom_level = st.slider('Nível de zoom', min_value=1, max_value=15, value=zoom_level_all) # Conditionally render the radius slider if radius_visible: radius_in_meters = st.slider('Selecione raio (em metros)', min_value=100, max_value=5000, value=1000) # Add sliders to filter data based atotal_range = st.slider('Área Total', float(data_tipo['Atotal'].min()), float(data_tipo['Atotal'].max()), (float(data_tipo['Atotal'].min()), float(data_tipo['Atotal'].max())), step=.1) apriv_range = st.slider('Área Privativa', float(data_tipo['Apriv'].min()), float(data_tipo['Apriv'].max()), (float(data_tipo['Apriv'].min()), float(data_tipo['Apriv'].max())), step=.1) if int(data_tipo['Dorm'].min()) != 0 and int(data_tipo['Dorm'].max()) != 0: dorm_range = st.slider('Dormitórios', int(data_tipo['Dorm'].min()), int(data_tipo['Dorm'].max()), (int(data_tipo['Dorm'].min()), int(data_tipo['Dorm'].max())), step=1) if int(data_tipo['Banheiro'].min()) != 0 and int(data_tipo['Banheiro'].max()) != 0: banho_range = st.slider('Banheiros', int(data_tipo['Banheiro'].min()), int(data_tipo['Banheiro'].max()), (int(data_tipo['Banheiro'].min()), int(data_tipo['Banheiro'].max())), step=1) if int(data_tipo['Vaga'].min()) != 0 and int(data_tipo['Vaga'].max()) != 0: vaga_range = st.slider('Vaga de estacionamento', int(data_tipo['Vaga'].min()), int(data_tipo['Vaga'].max()), (int(data_tipo['Vaga'].min()), int(data_tipo['Vaga'].max())), step=1) # Initialize checkbox variables elev_checkbox = False churr_checkbox = False esq_checkbox = False # Add checkboxes for dummy features if int(data_tipo['Elevador'].min()) != 0 and int(data_tipo['Elevador'].max()) != 0: elev_checkbox = st.checkbox('Elevador') if int(data_tipo['Churrasq'].min()) != 0 and int(data_tipo['Churrasq'].max()) != 0: churr_checkbox = st.checkbox('Churrasqueira') if int(data_tipo['Lot_pos'].min()) != 0 and int(data_tipo['Lot_pos'].max()) != 0: esq_checkbox = st.checkbox('Duas ou mais frentes') # Transform checkbox values into 1s and 0s elev_value = 1 if elev_checkbox else 0 churr_value = 1 if churr_checkbox else 0 esq_value = 1 if esq_checkbox else 0 data_tipo = data_tipo[(data_tipo['Atotal'].between(atotal_range[0], atotal_range[1])) & (data_tipo['Apriv'].between(apriv_range[0], apriv_range[1])) & (data_tipo['Dorm'].between(dorm_range[0], dorm_range[1])) & (data_tipo['Banheiro'].between(banho_range[0], banho_range[1])) & (data_tipo['Vaga'].between(vaga_range[0], vaga_range[1])) & (data_tipo['Elevador'] == elev_value) & (data_tipo['Churrasq'] == churr_value) & (data_tipo['Lot_pos'] == esq_value)] # Links to other apps at the bottom of the sidebar st.sidebar.markdown(factor_html, unsafe_allow_html=True) st.sidebar.markdown(evo_html, unsafe_allow_html=True) # Filter data based on the radius if selected_coords == 'Direcionada': filtered_data = data_tipo[data_tipo.apply(lambda x: calculate_distance(x['latitude'], x['longitude'], custom_lat, custom_lon), axis=1) <= radius_in_meters] filtered_data = filtered_data.dropna() # Drop rows with NaN values # Add a custom CSS class to the map container st.markdown(f"""""", unsafe_allow_html=True) # Determine which area feature to use for prediction filtered_data['area_feature'] = np.where(filtered_data['Apriv'] != 0, filtered_data['Apriv'], filtered_data['Atotal']) # Check if KNN should be applied if selected_coords == 'Direcionada' and radius_visible: # Apply KNN and get predicted V_oferta values predicted_V_oferta = knn_predict(filtered_data, 'V_oferta', ['latitude', 'longitude', 'area_feature']) # 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(): if selected_coords == 'Direcionada': st.map(filtered_data, zoom=zoom_level, use_container_width=True) elif selected_coords == 'Ampla': st.map(data, zoom=zoom_level, use_container_width=True) # 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', 'V_oferta', 'Predicted_V_oferta']])