import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
from sklearn.cluster import KMeans
import matplotlib.font_manager as fm

font_path = '/home/user/app/NanumGothic-Regular.ttf'
fontprop = fm.FontProperties(fname=font_path, size=12)


# Data Generation
np.random.seed(42)
num_samples = 30
traffic_centers = [(20, 20), (80, 80)]
nature_centers = [(0, 80), (80, 0)]
population_centers = [(0, 0), (50, 50), (100, 100)]

traffic_data = np.random.uniform(0, 100, (num_samples * len(traffic_centers), 2))
nature_data = np.random.uniform(0, 100, (num_samples * len(nature_centers), 2))
population_data = np.random.uniform(0, 100, (num_samples * len(population_centers), 2))

traffic_df = pd.DataFrame(traffic_data, columns=["x", "y"])
nature_df = pd.DataFrame(nature_data, columns=["x", "y"])
population_df = pd.DataFrame(population_data, columns=["x", "y"])

def apply_kmeans(data, k):
    kmeans = KMeans(n_clusters=k, random_state=42).fit(data)
    centroids = kmeans.cluster_centers_
    labels = kmeans.labels_
    return centroids, labels

def main():
    st.title("K-means 클러스터링 시뮬레이터")
    
    # Global variables declaration
    global traffic_df, nature_df, population_df

    if st.button("데이터셋 초기화"):
        traffic_data = np.random.uniform(0, 100, (num_samples * len(traffic_centers), 2))
        nature_data = np.random.uniform(0, 100, (num_samples * len(nature_centers), 2))
        population_data = np.random.uniform(0, 100, (num_samples * len(population_centers), 2))

        traffic_df = pd.DataFrame(traffic_data, columns=["x", "y"])
        nature_df = pd.DataFrame(nature_data, columns=["x", "y"])
        population_df = pd.DataFrame(population_data, columns=["x", "y"])
    
    datasets = st.multiselect("데이터셋 선택:", ["교통접근성", "자연환경", "인구밀집도"])
    k_value = st.slider("k 값 선택:", 1, 10)
    
    dataset_mapping = {
        "교통접근성": (traffic_df, 'o'),
        "자연환경": (nature_df, 'x'),
        "인구밀집도": (population_df, '^')
    }

    if datasets:
        combined_data = pd.concat([dataset_mapping[dataset_name][0] for dataset_name in datasets])
        
        centroids, labels = apply_kmeans(combined_data.values, k_value)
        fig, ax = plt.subplots(figsize=(8, 8))
        
        for dataset_name in datasets:
            data, marker = dataset_mapping[dataset_name]
            subset_labels = labels[:len(data)]
            ax.scatter(data['x'], data['y'], c=subset_labels, cmap='viridis', marker=marker, label=dataset_name)
            labels = labels[len(data):]
            
        ax.scatter(centroids[:, 0], centroids[:, 1], s=200, c='red', marker='X')
        ax.set_xlim(0, 100)
        ax.set_ylim(0, 100)
        ax.set_title(f"K-means 클러스터링 결과 (k={k_value})", fontproperties=fontprop)
        ax.legend(prop=fontprop)
        st.pyplot(fig)

if __name__ == "__main__":
    main()