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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( | |
""" | |
<style> | |
@font-face {font-family: 'Quicksand'; | |
src: url('font/Quicksand-VariableFont_wght.ttf') format('truetype'); | |
} | |
body { | |
color: white; | |
background-color: #1e1e1e; | |
font-family: 'Quicksand', sans-serif; | |
} | |
.st-df-header, .st-df-body, .st-df-caption { | |
color: #f8f9fa; /* Bootstrap table header text color */ | |
} | |
.st-eb { | |
background-color: #343a40; /* Streamlit exception box background color */ | |
} | |
</style> | |
""", | |
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 = """ | |
<style> | |
@font-face {font-family: 'Quicksand'; | |
src: url('font/Quicksand-VariableFont_wght.ttf') format('truetype'); | |
} | |
body {{ | |
font-family: 'Quicksand', sans-serif; | |
}} | |
</style> | |
<span style='color: gray; font-size: 50px;'>aval</span> | |
<span style='color: white; font-size: 50px;'>ia</span> | |
<span style='color: gray; font-size: 50px;'>.NEXUS</span> | |
""" | |
# Set font to 'Quicksand' for factor_html | |
factor_html = """ | |
<style> | |
@font-face {font-family: 'Quicksand'; | |
src: url('font/Quicksand-VariableFont_wght.ttf') format('truetype'); | |
} | |
body {{ | |
font-family: 'Quicksand', sans-serif; | |
}} | |
</style> | |
<a href='https://huggingface.co/spaces/DavidSB/avaliaFACTOR' target='_blank' style='text-decoration: none; color: inherit;'> | |
<span style='color: gray; font-size: 20px;'>aval</span> | |
<span style='color: white; font-size: 20px;'>ia</span> | |
<span style='color: gray; font-size: 20px;'>.FACTOR</span> | |
""" | |
# Set font to 'Quicksand' for evo_html | |
evo_html = """ | |
<style> | |
@font-face {font-family: 'Quicksand'; | |
src: url('font/Quicksand-VariableFont_wght.ttf') format('truetype'); | |
} | |
body {{ | |
font-family: 'Quicksand', sans-serif; | |
}} | |
</style> | |
<a href='https://huggingface.co/spaces/DavidSB/avalia.EVO' target='_blank' style='text-decoration: none; color: inherit;'> | |
<span style='color: gray; font-size: 20px;'>aval</span> | |
<span style='color: white; font-size: 20px;'>ia</span> | |
<span style='color: gray; font-size: 20px;'>.EVO</span> | |
""" | |
# 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 = 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.iterrows(): | |
distance_all = calculate_distance(row['latitude'], row['longitude'], data['latitude'].mean(), data['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['Atotal'].min()), float(data['Atotal'].max()), (float(data['Atotal'].min()), float(data['Atotal'].max()))) | |
apriv_range = st.slider('Área Privativa', float(data['Apriv'].min()), float(data['Apriv'].max()), (float(data['Apriv'].min()), float(data['Apriv'].max()))) | |
dorm_range = st.slider('Dormitórios', float(data['Dorm'].min()), float(data['Dorm'].max()), (float(data['Dorm'].min()), float(data['Dorm'].max()))) | |
banho_range = st.slider('Banheiros', float(data['Banheiro'].min()), float(data['Banheiro'].max()), (float(data['Banheiro'].min()), float(data['Banheiro'].max()))) | |
vaga_range = st.slider('Vaga de estacionamento', float(data['Vaga'].min()), float(data['Vaga'].max()), (float(data['Vaga'].min()), float(data['Vaga'].max()))) | |
# Add checkboxes for dummy features | |
elev_checkbox = st.checkbox('Elevador') | |
churr_checkbox = st.checkbox('Churrasqueira') | |
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 = data[(data['Tipo'] == selected_tipo) & | |
(data['Atotal'] == atotal_range) & | |
(data['Apriv'] == apriv_range) & | |
(data['Dorm'] == dorm_range) & | |
(data['Banheiro'] == banho_range) & | |
(data['Vaga'] == vaga_range) & | |
(data['Elevador'] == elev_value) & | |
(data['Churrasq'] == churr_value) & | |
(data['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[data.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"""<style> | |
.map {{ | |
width: 100%; | |
height: 100vh; | |
}} | |
</style>""", 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']]) |