app.py

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

# Generate the datasets
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.normal(center, 10, (num_samples, 2)) for center in traffic_centers]
nature_data = [np.random.normal(center, 10, (num_samples, 2)) for center in nature_centers]
population_data = [np.random.normal(center, 10, (num_samples, 2)) for center in population_centers]

traffic_df = pd.DataFrame(np.vstack(traffic_data), columns=["x", "y"])
nature_df = pd.DataFrame(np.vstack(nature_data), columns=["x", "y"])
population_df = pd.DataFrame(np.vstack(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 generate_data():
    global traffic_df, nature_df, population_df
    
    # 랜덤데이터 생성
    traffic_data = np.random.uniform(0, 100, (num_samples, 2))
    nature_data = np.random.uniform(0, 100, (num_samples, 2))
    population_data = np.random.uniform(0, 100, (num_samples, 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 main():
    st.title("K-means Clustering Simulator")
    
    if st.button("Initialize Datasets"):
        generate_data()    
    datasets = st.multiselect("Choose datasets:", ["교통접근성", "자연환경", "인구밀집도"])
    k_value = st.slider("Select k value:", 1, 10)
    
    dataset_mapping = {
        "교통접근성": traffic_df,
        "자연환경": nature_df,
        "인구밀집도": population_df
    }
    
    # 아무 값도 없을때
    if datasets:
        combined_data = pd.concat([dataset_mapping[dataset_name] for dataset_name in datasets])
        
        fig, ax = plt.subplots(figsize=(8, 8))
        
        centroids, labels = apply_kmeans(combined_data.values, k_value)
        ax.scatter(combined_data['x'], combined_data['y'], c=labels, cmap='viridis')
        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 clustering result (k={k_value})")
        st.pyplot(fig)

if __name__ == "__main__":
    main()