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import streamlit as st | |
import pandas as pd | |
import numpy as np | |
import openpyxl | |
from sklearn.neighbors import KNeighborsRegressor | |
from geopy.distance import geodesic | |
# 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') | |
# 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 | |
# 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=-29.45086) | |
custom_lon = st.number_input('Enter Longitude', value=-51.9847) | |
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 | |
zoom_level = st.slider('Nível de zoom', min_value=1, max_value=15, value=zoom_level) | |
# 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) | |
if selected_coords == 'Custom' and radius_visible: | |
# Filter data based on the radius and drop NaN values | |
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() | |
# Apply KNN and get predicted Vunit values | |
predicted_vunit = knn_predict(filtered_data, 'Vunit', knn_features) | |
# Add predicted Vunit values to filtered_data | |
filtered_data['Predicted_Vunit'] = predicted_vunit | |
# Add a custom CSS class to the map container | |
st.markdown(f"""<style> | |
.map {{ | |
width: 100%; | |
height: 100vh; | |
}} | |
</style>""", unsafe_allow_html=True) | |
# Wrap the map in a container with the custom CSS class | |
with st.container(): | |
st.map(filtered_data, zoom=zoom_level, use_container_width=True) | |
# 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(filtered_data[features_columns]) | |
return predictions | |
# Features columns for KNN | |
knn_features = ['latitude', 'longitude', 'Area'] # Add other relevant features | |
# Check if KNN should be applied | |
if selected_coords == 'Custom' and radius_visible: | |
# Apply KNN and get predicted Vunit values | |
predicted_vunit = knn_predict(data, 'Vunit', knn_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) | |
# Display the predicted Vunit values if applicable | |
if 'Predicted_Vunit' in filtered_data.columns: | |
st.write("Predicted Vunit Values:") | |
st.write(filtered_data[['latitude', 'longitude', 'Vunit', 'Predicted_Vunit']]) |