Delete app_V1.py.txt

app_V1.py.txt

@@ -1,74 +0,0 @@
-import streamlit as st
-import pandas as pd
-import matplotlib.pyplot as plt
-import joblib
-from sklearn.decomposition import PCA
-from sklearn.preprocessing import StandardScaler
-from sklearn.cluster import KMeans, DBSCAN
-from scipy.cluster.hierarchy import fcluster, linkage
-
-# 讀取保存的模型
-scaler = joblib.load('scaler.sav')
-pca = joblib.load('pca_model.sav')
-kmeans = joblib.load('kmeans_model.sav')
-linked = joblib.load('hierarchical_model.sav')
-dbscan = joblib.load('dbscan_model.sav')
-
-# 標題和簡介
-st.title("聚類分析 - KMeans, Hierarchical Clustering 和 DBSCAN")
-st.write("上傳 CSV 文件並查看聚類結果")
-
-# 上傳文件
-uploaded_file = st.file_uploader("上傳 CSV 文件", type=["csv"])
-
-if uploaded_file is not None:
-    # 讀取上傳的 CSV 文件
-    data = pd.read_csv(uploaded_file)
-    
-    # 移除 'Time' 欄位
-    numerical_data = data.drop(columns=['Time'])
-    
-    # 標準化數據
-    scaled_data = scaler.transform(numerical_data)
-    
-    # 使用 PCA 進行降維
-    pca_data = pca.transform(scaled_data)
-    
-    # 創建包含主成分的 DataFrame
-    pca_df = pd.DataFrame(pca_data, columns=['PC1', 'PC2'])
-    
-    # 使用保存的 K-means 模型進行聚類
-    kmeans_labels = kmeans.predict(pca_df)
-    
-    # 使用保存的階層式聚類結果
-    hclust_labels = fcluster(linked, 3, criterion='maxclust')
-    
-    # 使用保存的 DBSCAN 模型進行聚類
-    dbscan_labels = dbscan.fit_predict(pca_df)
-    
-    # ================== K-means 聚類圖表 ==================
-    st.subheader("K-means 聚類結果")
-    fig_kmeans, ax_kmeans = plt.subplots()
-    ax_kmeans.scatter(pca_df['PC1'], pca_df['PC2'], c=kmeans_labels, cmap='viridis')
-    ax_kmeans.set_title('K-means Clustering')
-    ax_kmeans.set_xlabel('PC1')
-    ax_kmeans.set_ylabel('PC2')
-    st.pyplot(fig_kmeans)
-    
-    # ================== 階層式聚類圖表 ==================
-    st.subheader("階層式聚類結果")
-    fig_hclust, ax_hclust = plt.subplots()
-    ax_hclust.scatter(pca_df['PC1'], pca_df['PC2'], c=hclust_labels, cmap='viridis')
-    ax_hclust.set_title('Hierarchical Clustering')
-    ax_hclust.set_xlabel('PC1')
-    ax_hclust.set_ylabel('PC2')
-    st.pyplot(fig_hclust)
-    
-    # ================== DBSCAN 聚類圖表 ==================
-    st.subheader("DBSCAN 聚類結果")
-    fig_dbscan, ax_dbscan = plt.subplots()
-    ax_dbscan.scatter(pca_df['PC1'], pca_df['PC2'], c=dbscan_labels, cmap='viridis')
-    ax_dbscan.set_title('DBSCAN Clustering')
-    ax_dbscan.set_xlabel('PC1')
-    ax_dbscan.set_ylabel('PC2')
-    st.pyplot(fig_dbscan)