Update diagnosis function and Gradio UI for improved clarity and error handling

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import pandas as pd
 import json
 
-# --- 閾値診断関数 ---
+# --- 状態判定関数 ---
 def judge_status(value, ll, l, h, hh):
     if pd.notna(ll) and value < ll:
         return "LOW-LOW"
@@ -15,38 +15,37 @@ def judge_status(value, ll, l, h, hh):
     else:
         return "OK"
 
+# --- 閾値診断処理 ---
 def diagnose_process_range(csv_file, excel_file, process_name, datetime_str, window_minutes):
     try:
-        # CSV読み込み（3行ヘッダー）
+        # CSV読み込み
         df = pd.read_csv(csv_file.name, header=[0, 1, 2])
         timestamp_col = df.iloc[:, 0]
         df = df.drop(df.columns[0], axis=1)
         df.insert(0, "timestamp", timestamp_col)
         df["timestamp"] = pd.to_datetime(df["timestamp"], errors="coerce")
 
-        # 閾値テーブル
+        # 閾値テーブル読み込み
         thresholds_df = pd.read_excel(excel_file.name)
         thresholds_df["Important"] = thresholds_df["Important"].astype(str).str.upper().map({"TRUE": True, "FALSE": False})
         for col in ["LL", "L", "H", "HH"]:
             if col in thresholds_df.columns:
                 thresholds_df[col] = pd.to_numeric(thresholds_df[col], errors="coerce")
 
-        # 入力日時
+        # --- 入力日時処理 ---
         target_time = pd.to_datetime(datetime_str)
         start_time = target_time - pd.Timedelta(minutes=window_minutes)
         end_time = target_time
-
-        # 対象期間の抽出
         df_window = df[(df["timestamp"] >= start_time) & (df["timestamp"] <= end_time)]
+
         if df_window.empty:
-            return None, None, None, f"⚠ 指定時間幅にデータが見つかりません。", "{}"
+            return None, "⚠ 指定時間幅にデータなし", None
 
-        # 閾値対象
         proc_thresholds = thresholds_df[thresholds_df["ProcessNo_ProcessName"] == process_name]
         if proc_thresholds.empty:
-            return None, None, None, f"⚠ プロセス {process_name} の閾値が設定されていません。", "{}"
+            return None, f"⚠ プロセス {process_name} の閾値がありません", None
 
-        # 判定処理
+        # --- 判定処理 ---
         all_results = []
         for _, row in df_window.iterrows():
             for _, thr in proc_thresholds.iterrows():
@@ -61,63 +60,59 @@ def diagnose_process_range(csv_file, excel_file, process_name, datetime_str, win
                     "重要項目": bool(thr.get("Important", False))
                 })
 
-        # 全項目集計
-        total = len(all_results)
+        if not all_results:
+            return None, f"⚠ データ不足", None
+
+        # --- 集計（全項目） ---
         status_counts = pd.Series([r["判定"] for r in all_results]).value_counts().reindex(
             ["LOW-LOW", "LOW", "OK", "HIGH", "HIGH-HIGH"], fill_value=0
         )
-        status_ratio = (status_counts / total * 100).round(1)
+        status_ratio = (status_counts / len(all_results) * 100).round(1)
         result_df_all = pd.DataFrame({"状態": status_counts.index, "件数": status_counts.values, "割合(%)": status_ratio.values})
 
-        # 重要項目全体集計
+        # --- 集計（重要項目全体） ---
         important_results = [r for r in all_results if r["重要項目"]]
         if important_results:
-            total_imp = len(important_results)
             status_counts_imp = pd.Series([r["判定"] for r in important_results]).value_counts().reindex(
                 ["LOW-LOW", "LOW", "OK", "HIGH", "HIGH-HIGH"], fill_value=0
             )
-            status_ratio_imp = (status_counts_imp / total_imp * 100).round(1)
+            status_ratio_imp = (status_counts_imp / len(important_results) * 100).round(1)
             result_df_imp = pd.DataFrame({"状態": status_counts_imp.index, "件数": status_counts_imp.values, "割合(%)": status_ratio_imp.values})
         else:
             result_df_imp = pd.DataFrame(columns=["状態", "件数", "割合(%)"])
 
-        # 重要項目ごと集計
+        # --- 集計（重要項目ごと） ---
         result_per_item = []
-        for item in [r["ItemName"] for r in important_results]:
+        for item in set([r["ItemName"] for r in important_results]):
             item_results = [r for r in important_results if r["ItemName"] == item]
             if not item_results:
                 continue
-            total_item = len(item_results)
             status_counts_item = pd.Series([r["判定"] for r in item_results]).value_counts().reindex(
                 ["LOW-LOW", "LOW", "OK", "HIGH", "HIGH-HIGH"], fill_value=0
             )
-            status_ratio_item = (status_counts_item / total_item * 100).round(1)
+            status_ratio_item = (status_counts_item / len(item_results) * 100).round(1)
             for s, c, r in zip(status_counts_item.index, status_counts_item.values, status_ratio_item.values):
-                result_per_item.append({"ItemName": item, "状態": s, "件数": c, "割合(%)": r})
+                result_per_item.append({"ItemName": item, "状態": s, "件数": int(c), "割合(%)": float(r)})
         result_df_imp_items = pd.DataFrame(result_per_item)
 
-        # サマリー
-        summary = (
-            f"✅ {process_name} の診断完了（{start_time} ～ {end_time}）\n"
-            + "[全項目] " + " / ".join([f"{s}:{r:.1f}%" for s, r in status_ratio.items()]) + "\n"
-            + "[重要項目全体] " + (
-                " / ".join([f"{s}:{r:.1f}%" for s, r in status_ratio_imp.items()])
-                if not result_df_imp.empty else "対象データなし"
-            )
-        )
-
-        # --- 軽量 JSON（集計結果のみ） ---
+        # --- JSON出力（集計のみ） ---
         summary_stats = {
-            "全項目割合": status_ratio.to_dict(),
-            "重要項目全体割合": status_ratio_imp.to_dict() if not result_df_imp.empty else {},
+            "全項目割合": {k: float(v) for k, v in status_ratio.to_dict().items()},
+            "重要項目全体割合": {k: float(v) for k, v in status_ratio_imp.to_dict().items()} if not result_df_imp.empty else {},
             "重要項目ごと割合": result_per_item
         }
         result_json = json.dumps({"集計結果": summary_stats}, ensure_ascii=False, indent=2)
 
+        # --- サマリー ---
+        summary = (
+            f"✅ {process_name} の診断完了（{start_time} ～ {end_time}）\n"
+            + "[全項目] " + " / ".join([f"{s}:{r:.1f}%" for s, r in status_ratio.items()])
+        )
+
         return result_df_all, result_df_imp, result_df_imp_items, summary, result_json
 
     except Exception as e:
-        return None, None, None, f"❌ エラー: {str(e)}", "{}"
+        return None, None, None, f"❌ エラー: {str(e)}", None
 
 
 # --- Gradio UI ---
@@ -126,26 +121,29 @@ with gr.Blocks() as demo:
 
     with gr.Row():
         csv_input = gr.File(label="CSVファイルをアップロード", type="filepath")
-        excel_input = gr.File(label="閾値テーブル (Excel)", type="filepath")
+        excel_input = gr.File(label="閾値テーブルExcelをアップロード", type="filepath")
 
-    with gr.Row():
-        process_input = gr.Textbox(label="工程名（例: E018-A012_除害RO）")
-        datetime_input = gr.Textbox(label="診断日時 (YYYY/MM/DD HH:MM)")
-        window_input = gr.Number(label="時間幅 (分)", value=60)
-
-    run_btn = gr.Button("診断実行")
-
-    result_all = gr.Dataframe(label="全項目の状態集計結果")
-    result_imp = gr.Dataframe(label="重要項目全体の状態集計結果")
-    result_imp_items = gr.Dataframe(label="重要項目ごとの状態集計結果")
-    summary_out = gr.Textbox(label="診断サマリー")
-    json_out = gr.JSON(label="集計結果 JSON")
-
-    run_btn.click(
-        diagnose_process_range,
-        inputs=[csv_input, excel_input, process_input, datetime_input, window_input],
-        outputs=[result_all, result_imp, result_imp_items, summary_out, json_out]
+    process_name = gr.Textbox(label="プロセス名", value="E018-A012_除害RO")
+    datetime_str = gr.Textbox(label="日時 (例: 2025/8/1 1:05)")
+    window_minutes = gr.Number(label="さかのぼる時間幅 (分)", value=60)
+
+    run_button = gr.Button("診断を実行")
+
+    with gr.Tab("全項目集計"):
+        result_all = gr.Dataframe(label="全項目の状態集計結果")
+    with gr.Tab("重要項目全体集計"):
+        result_imp = gr.Dataframe(label="重要項目全体の状態集計結果")
+    with gr.Tab("重要項目ごと集計"):
+        result_imp_items = gr.Dataframe(label="重要項目ごとの状態集計結果")
+
+    summary_output = gr.Textbox(label="サマリー")
+    json_output = gr.JSON(label="JSON出力")
+
+    run_button.click(
+        fn=diagnose_process_range,
+        inputs=[csv_input, excel_input, process_name, datetime_str, window_minutes],
+        outputs=[result_all, result_imp, result_imp_items, summary_output, json_output]
     )
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", share=False, ssr_mode=False)
+    demo.launch(server_name="0.0.0.0", share=False)