Spaces:
Sleeping
Sleeping
import streamlit as st | |
import pandas as pd | |
import numpy as np | |
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") | |
# Set dark theme | |
st.markdown( | |
""" | |
<style> | |
body { | |
color: white; | |
background-color: #1e1e1e; | |
} | |
.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_ter_fim_PEDÓ.xlsx') | |
# Initialize variables to avoid NameError | |
selected_coords = 'Custom' | |
radius_visible = True | |
custom_lat = data['latitude'].mean() | |
custom_lon = data['longitude'].mean() | |
radius_in_meters = 1000 | |
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(15 - np.log10(max_distance)) | |
# Create a sidebar for controls | |
with st.sidebar: | |
# Display a title | |
st.title('avalia.se') | |
# Dropdown to select specific coordinates | |
selected_coords = st.selectbox('Selecione Coordenadas', ['Random', 'Custom']) | |
if selected_coords == 'Custom': | |
custom_lat = st.number_input('Enter Latitude', value=filtered_data['latitude'].mean()) | |
custom_lon = st.number_input('Enter Longitude', value=filtered_data['longitude'].mean()) | |
radius_visible = True # Show radius slider for custom coordinates | |
else: | |
custom_lat, custom_lon = data['latitude'].mean(), data['longitude'].mean() | |
radius_visible = False # Hide radius slider for random coordinates | |
# Slider for setting the zoom level | |
if selected_coords == 'Custom': | |
zoom_level = st.slider('Nível de zoom', min_value=1, max_value=15, value=zoom_level) | |
else: | |
# Find the maximum distance between coordinates for the entire dataset | |
max_distance_all = 0 | |
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) | |
# 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() # 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) | |
# Check if KNN should be applied | |
if selected_coords == 'Custom' and radius_visible: | |
# Apply KNN and get predicted Vunit values | |
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 | |
# Display the map and filtered_data | |
with st.container(): | |
if selected_coords == 'Custom': | |
st.map(filtered_data, zoom=zoom_level, use_container_width=True) | |
elif selected_coords == 'Random': | |
st.map(data, zoom=zoom_level, use_container_width=True) | |
# Display the predicted Vunit values if applicable | |
if 'Predicted_Vunit' in filtered_data.columns: | |
st.write("Valores (R$/m²) previstos com algoritmo KNN:") | |
st.write(filtered_data[['latitude', 'longitude', 'Vunit', 'Predicted_Vunit']]) | |