Update API for connect

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import datetime
 import pandas as pd
 import joblib
-from sklearn.cluster import HDBSCAN
+import hdbscan
 import numpy as np
 import logger_service
 import os
@@ -11,6 +11,8 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 import matplotlib.font_manager as fm
 import shutil
+import io
+import base64
 
 # --- 🌟 解決 Matplotlib 中文顯示問題 ---
 font_path = 'SimHei.ttf'
@@ -200,6 +202,15 @@ def api_get_chart_data(action_name):
         "action_code": action_code, 
         "data": {"normal": normal_data, "abnormal": abnormal_data}
     }
+
+def api_get_abnormal_logs():
+    """取得所有異常 Log"""
+    abnormal_logs = [log for log in log_history if "異常" in str(log.get("status", "")) or "錯誤" in str(log.get("status", "")) or "受限" in str(log.get("status", ""))]
+    return {
+        "status": "success",
+        "count": len(abnormal_logs),
+        "data": abnormal_logs
+    }
 # =====================================================================
 
 
@@ -244,7 +255,111 @@ def get_dashboard_html():
     </div>
     """
 
+def get_plot_data_for_api(selected_key):
+    """API專用版本，返回Base64圖片字符串"""
+    if not selected_key or not AI_READY: return None
+    action_code = ACTION_MAP.get(selected_key)
+    try:
+        plt.close('all') 
+        df_hist = baseline_df[baseline_df['action'] == action_code].copy()
+        
+        df_live = pd.DataFrame(log_history)
+        if not df_live.empty:
+            df_live['timestamp'] = pd.to_datetime(df_live['timestamp'], errors='coerce')
+            df_live = df_live.dropna(subset=['timestamp']) 
+            df_live = df_live[df_live['action'] == action_code].copy()
+        
+        if not df_live.empty:
+            df_combined = pd.concat([df_hist, df_live], ignore_index=True)
+            df_combined = df_combined.drop_duplicates(subset=['timestamp', 'account', 'action'])
+        else:
+            df_combined = df_hist.copy()
+            
+        if df_combined.empty:
+            fig, ax = plt.subplots(figsize=(12, 6))
+            if os.path.exists(font_path): plt.rc('font', family='SimHei')
+            ax.text(0.5, 0.5, f"【{selected_key}】目前尚無資料", ha='center', va='center', fontsize=15)
+            # 將圖片轉換為 Base64
+            buf = io.BytesIO()
+            fig.savefig(buf, format='png', dpi=100, bbox_inches='tight')
+            buf.seek(0)
+            img_base64 = base64.b64encode(buf.getvalue()).decode('utf-8')
+            buf.close()
+            plt.close(fig)
+            return f"data:image/png;base64,{img_base64}"
+
+        df_combined['month'] = df_combined['timestamp'].dt.month
+        df_combined['time_of_day'] = df_combined['timestamp'].dt.hour + df_combined['timestamp'].dt.minute / 60.0
+        
+        is_abnormal = df_combined['status'].astype(str).str.contains("異常", na=False)
+        df_normal = df_combined[~is_abnormal]
+        df_abnormal = df_combined[is_abnormal]
+
+        fig, ax = plt.subplots(figsize=(12, 6))
+        sns.set_theme(style="whitegrid")
+        if os.path.exists(font_path): plt.rc('font', family='SimHei')
+        plt.rcParams['axes.unicode_minus'] = False
+
+        has_data = False
+        months_order = list(range(1, 13)) 
+
+        if not df_normal.empty:
+            has_data = True
+            sns.stripplot(data=df_normal, x='month', y='time_of_day', jitter=0.3, alpha=0.4, size=5, color='#10b981', order=months_order, ax=ax, label='正常資料')
+
+        if not df_abnormal.empty:
+            has_data = True
+            sns.stripplot(data=df_abnormal, x='month', y='time_of_day', jitter=0.3, alpha=0.9, size=9, color='#ef4444', marker='X', order=months_order, ax=ax, label='異常資料')
+            
+        if not has_data:
+            ax.text(0.5, 0.5, f"【{selected_key}】目前尚無資料", ha='center', va='center', fontsize=15)
+            # 將圖片轉換為 Base64
+            buf = io.BytesIO()
+            fig.savefig(buf, format='png', dpi=100, bbox_inches='tight')
+            buf.seek(0)
+            img_base64 = base64.b64encode(buf.getvalue()).decode('utf-8')
+            buf.close()
+            plt.close(fig)
+            return f"data:image/png;base64,{img_base64}"
+
+        ax.set_xlabel('月份 (1 ~ 12月)', fontsize=12)
+        ax.set_ylabel('時間 (24小時制)', fontsize=12)
+        ax.set_yticks(np.arange(0, 25, 2))
+        ax.set_ylim(24.5, -0.5)
+
+        handles, labels = ax.get_legend_handles_labels()
+        by_label = dict(zip(labels, handles))
+        if by_label: ax.legend(by_label.values(), by_label.keys(), loc='upper right', bbox_to_anchor=(1.15, 1))
+
+        plt.tight_layout()
+        
+        # 將圖片轉換為 Base64
+        buf = io.BytesIO()
+        fig.savefig(buf, format='png', dpi=100, bbox_inches='tight')
+        buf.seek(0)
+        img_base64 = base64.b64encode(buf.getvalue()).decode('utf-8')
+        buf.close()
+        plt.close(fig)
+        return f"data:image/png;base64,{img_base64}"
+    except Exception as e:
+        print(f"繪圖失敗: {e}")
+        # 返回一個簡單的錯誤圖片
+        fig, ax = plt.subplots(figsize=(8, 4))
+        ax.text(0.5, 0.5, f"圖表生成失敗: {str(e)}", ha='center', va='center', fontsize=12, color='red')
+        ax.set_xlim(0, 1)
+        ax.set_ylim(0, 1)
+        ax.axis('off')
+        
+        buf = io.BytesIO()
+        fig.savefig(buf, format='png', dpi=100, bbox_inches='tight')
+        buf.seek(0)
+        img_base64 = base64.b64encode(buf.getvalue()).decode('utf-8')
+        buf.close()
+        plt.close(fig)
+        return f"data:image/png;base64,{img_base64}"
+
 def get_plot_data(selected_key):
+    """給Gradio界面使用，返回Figure對象"""
     if not selected_key or not AI_READY: return None
     action_code = ACTION_MAP.get(selected_key)
     try:
@@ -419,7 +534,7 @@ def check_anomaly(ip, account, action, log_time):
         fit_X = np.vstack((other_history, relevant_history, new_data_point))
         strictness = max(2, int(2 + (ow / 100.0) * 4))
         
-        model = HDBSCAN(min_cluster_size=strictness, min_samples=2)
+        model = hdbscan.HDBSCAN(min_cluster_size=strictness, min_samples=2)
         clusters = model.fit_predict(fit_X)
         
         if clusters[-1] == -1: return "⚠️異常_作息或行為不符"
@@ -668,13 +783,12 @@ with gr.Blocks(title="模擬校園入口與資安防護系統") as demo:
 
     demo.load(fn=timer_update, inputs=[action_dropdown], outputs=[stat_dashboard_display, action_plot])
     dashboard_timer.tick(fn=timer_update, inputs=[action_dropdown], outputs=[stat_dashboard_display, action_plot])
-    action_dropdown.change(fn=get_plot_data, inputs=[action_dropdown], outputs=[action_plot])
+    action_dropdown.change(fn=get_plot_data, inputs=[action_dropdown], outputs=[action_plot], api_name="getPlotData")
     time_mode.change(fn=toggle_time_input, inputs=time_mode, outputs=custom_time_input)
     btn_explain_log.click(fn=explain_abnormal_log, inputs=[abnormal_log_input], outputs=[ai_explanation_output])
     
-    weight_inputs = [overall_weight, time_weight, ip_weight, device_weight, geo_weight, freq_weight]
-    for w_input in weight_inputs:
-        w_input.change(fn=update_system_weights, inputs=weight_inputs, outputs=[weights_status])
+    # weight_inputs = [overall_weight, time_weight, ip_weight, device_weight, geo_weight, freq_weight]
+    # 移除所有 UI 欄位的 update_system_weights 綁定，僅保留 API 專用端點
 
     update_outputs = [main_router, role_tabs, login_msg, welcome_text, store_df_all, store_df_abn, abnormal_log_input]
     
@@ -735,6 +849,27 @@ with gr.Blocks(title="模擬校園入口與資安防護系統") as demo:
         api_chart_in = gr.Textbox()
         api_chart_out = gr.JSON()
         api_chart_btn.click(fn=api_get_chart_data, inputs=[api_chart_in], outputs=[api_chart_out], api_name="get_chart_data")
+        
+        api_abnormal_logs_btn = gr.Button("API_Abnormal_Logs")
+        api_abnormal_logs_out = gr.JSON()
+        api_abnormal_logs_btn.click(fn=api_get_abnormal_logs, inputs=[], outputs=[api_abnormal_logs_out], api_name="get_abnormal_logs")
+        
+        api_plot_btn = gr.Button("API_Plot")
+        api_plot_in = gr.Textbox()
+        api_plot_out = gr.Textbox()
+        api_plot_btn.click(fn=get_plot_data_for_api, inputs=[api_plot_in], outputs=[api_plot_out], api_name="get_plot_data_api")
+        
+        # System weights update API endpoint
+        api_update_weights_btn = gr.Button("API_Update_Weights")
+        api_update_weights_ow = gr.Number(label="Overall Weight")
+        api_update_weights_tw = gr.Number(label="Time Weight") 
+        api_update_weights_iw = gr.Number(label="IP Weight")
+        api_update_weights_dw = gr.Number(label="Device Weight")
+        api_update_weights_gw = gr.Number(label="Geo Weight")
+        api_update_weights_fw = gr.Number(label="Frequency Weight")
+        api_update_weights_in = [api_update_weights_ow, api_update_weights_tw, api_update_weights_iw, api_update_weights_dw, api_update_weights_gw, api_update_weights_fw]
+        api_update_weights_out = gr.Textbox()
+        api_update_weights_btn.click(fn=update_system_weights, inputs=api_update_weights_in, outputs=[api_update_weights_out], api_name="update_system_weights")
     # =====================================================================
 
 if __name__ == "__main__":

baseline_logs.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5ea761decb0a310671beac39fffa37d7e19dde48587d70f29da6f2c68365518f
-size 4590684
+oid sha256:39ee1be5bfccd9325c4ea6fcfde49df1a29ee805a9e1affdf485483f839e6683
+size 4596753