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']])