Create app.py

app.py

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+import joblib
+import pandas as pd
+import matplotlib.pyplot as plt
+import streamlit as st
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from scipy.cluster.hierarchy import fcluster
+
+# ================== 加載保存的模型 ==================
+scaler = joblib.load('/content/gdrive/My Drive/scaler.sav')                # 標準化模型
+pca = joblib.load('/content/gdrive/My Drive/pca_model.sav')                # PCA 模型
+kmeans = joblib.load('/content/gdrive/My Drive/kmeans_model.sav')          # K-means 模型
+linked = joblib.load('/content/gdrive/My Drive/hierarchical_model.sav')    # 階層式聚類模型
+dbscan = joblib.load('/content/gdrive/My Drive/dbscan_model.sav')          # DBSCAN 模型
+
+# 定義繪圖函數
+def plot_clusters(data, labels, title):
+    plt.figure(figsize=(8, 6))
+    plt.scatter(data['PC1'], data['PC2'], c=labels, cmap='viridis', s=50)
+    plt.title(title)
+    plt.xlabel('Principal Component 1 (PC1)')
+    plt.ylabel('Principal Component 2 (PC2)')
+    plt.colorbar()
+    plt.savefig('plot.png')
+    plt.close()
+    return 'plot.png'
+
+# 處理上傳的資料
+def process_data(file):
+    # 讀取新資料
+    new_data = pd.read_csv(file)
+    # 移除 'Time' 欄位
+    new_numerical_data = new_data.drop(columns=['Time'])
+
+    # 數據預處理
+    scaled_new_data = scaler.transform(new_numerical_data)      # 標準化數據
+    pca_new_data = pca.transform(scaled_new_data)               # 使用已保存的 PCA 模型進行轉換
+
+    # 創建包含主成分的 DataFrame
+    pca_new_df = pd.DataFrame(pca_new_data, columns=['PC1', 'PC2'])
+
+    # 使用加載的模型進行聚類
+    kmeans_new_labels = kmeans.predict(pca_new_df)
+    hclust_new_labels = fcluster(linked, 3, criterion='maxclust')
+    dbscan_new_labels = dbscan.fit_predict(pca_new_df)
+
+    # 可視化結果
+    kmeans_plot = plot_clusters(pca_new_df, kmeans_new_labels, 'K-means Clustering')
+    hclust_plot = plot_clusters(pca_new_df, hclust_new_labels, 'Hierarchical Clustering')
+    dbscan_plot = plot_clusters(pca_new_df, dbscan_new_labels, 'DBSCAN Clustering')
+
+    return kmeans_new_labels, hclust_new_labels, dbscan_new_labels, kmeans_plot, hclust_plot, dbscan_plot
+
+# Streamlit 應用程式
+st.title("聚類模型應用")
+
+# 文件上傳
+uploaded_file = st.file_uploader("上傳 CSV 檔案", type=["csv"])
+
+if uploaded_file is not None:
+    kmeans_labels, hclust_labels, dbscan_labels, kmeans_plot, hclust_plot, dbscan_plot = process_data(uploaded_file)
+
+    # 顯示 K-means 標籤
+    st.subheader("K-means Labels")
+    st.text(kmeans_labels)
+
+    # 顯示 Hierarchical 標籤
+    st.subheader("Hierarchical Clustering Labels")
+    st.text(hclust_labels)
+
+    # 顯示 DBSCAN 標籤
+    st.subheader("DBSCAN Labels")
+    st.text(dbscan_labels)
+
+    # 顯示圖像
+    st.subheader("K-means Clustering Plot")
+    st.image(kmeans_plot)
+
+    st.subheader("Hierarchical Clustering Plot")
+    st.image(hclust_plot)
+
+    st.subheader("DBSCAN Clustering Plot")
+    st.image(dbscan_plot)