Enhance diagnosis function to return detailed results and update Gradio UI for improved output display

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import pandas as pd
 import json
 
+# --- 状態判定関数 ---
 def judge_status(value, ll, l, h, hh):
     if pd.notna(ll) and value < ll:
         return "LOW-LOW"
@@ -14,9 +15,10 @@ def judge_status(value, ll, l, h, hh):
     else:
         return "OK"
 
+# --- 診断関数 ---
 def diagnose_app(csv_file, excel_file, process_name, datetime_str, window_minutes):
     if csv_file is None or excel_file is None:
-        return "⚠ CSVとExcelファイルを両方アップロードしてください", None, None
+        return "⚠ CSVとExcelを両方アップロードしてください", None, None, None, None
 
     # CSV読み込み（3行ヘッダー）
     df = pd.read_csv(csv_file, header=[0, 1, 2])
@@ -36,18 +38,19 @@ def diagnose_app(csv_file, excel_file, process_name, datetime_str, window_minute
     try:
         target_time = pd.to_datetime(datetime_str)
     except Exception:
-        return f"⚠ 入力した日時 {datetime_str} が無効です", None, None
+        return f"⚠ 入力した日時 {datetime_str} が無効です", None, None, None, None
 
     start_time = target_time - pd.Timedelta(minutes=window_minutes)
     end_time = target_time
     df_window = df[(df["timestamp"] >= start_time) & (df["timestamp"] <= end_time)]
     if df_window.empty:
-        return "⚠ 指定した時間幅にデータがありません", None, None
+        return "⚠ 指定時間幅にデータなし", None, None, None, None
 
     proc_thresholds = thresholds_df[thresholds_df["ProcessNo_ProcessName"] == process_name]
     if proc_thresholds.empty:
-        return f"⚠ プロセス {process_name} の閾値が見つかりません", None, None
+        return f"⚠ プロセス {process_name} の閾値が見つかりません", None, None, None, None
 
+    # 全結果リスト
     all_results = []
     for _, row in df_window.iterrows():
         for _, thr in proc_thresholds.iterrows():
@@ -60,42 +63,72 @@ def diagnose_app(csv_file, excel_file, process_name, datetime_str, window_minute
                 "ColumnID": thr["ColumnID"],
                 "ItemName": thr["ItemName"],
                 "値": value,
+                "LL": thr.get("LL"),
+                "L": thr.get("L"),
+                "H": thr.get("H"),
+                "HH": thr.get("HH"),
                 "判定": status,
                 "重要項目": bool(thr.get("Important", False)),
-                "時刻": row["timestamp"]
+                "時刻": str(row["timestamp"])
             })
 
-    total = len(all_results)
-    if total == 0:
-        return f"⚠ プロセス {process_name} の診断結果なし", None, None
+    if not all_results:
+        return f"⚠ 診断結果なし", None, None, None, None
 
+    # --- 全項目集計 ---
+    total = len(all_results)
     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)
-
-    result_df = pd.DataFrame({
-        "状態": status_counts.index,
-        "件数": status_counts.values,
-        "割合(%)": status_ratio.values
-    })
-
+    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 ---
+    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)
+        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 ---
+    result_per_item = []
+    for item in {r["ItemName"] for r in important_results}:
+        item_results = [r for r in important_results if r["ItemName"] == item]
+        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)
+        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_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"
-        + " / ".join([f"{s}:{r:.1f}%" for s, r in status_ratio.items()])
+        + "[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"
+        )
     )
 
     result_json = json.dumps(all_results, ensure_ascii=False, indent=2, default=str)
 
-    return summary, result_df, result_json
+    return summary, result_df_all, result_df_imp, result_df_imp_items, result_json
 
 # --- Gradio UI ---
 with gr.Blocks() as demo:
     gr.Markdown("## 閾値診断アプリ")
 
     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="閾値テーブルをアップロード", type="filepath")
+        csv_input = gr.File(label="CSVファイル", type="filepath")
+        excel_input = gr.File(label="閾値テーブル", type="filepath")
 
     process_name = gr.Textbox(label="プロセス名", placeholder="例: E018-A012_除害RO")
     datetime_str = gr.Textbox(label="基準日時 (例: 2025/8/1 0:05)")
@@ -104,13 +137,15 @@ with gr.Blocks() as demo:
     run_btn = gr.Button("診断実行")
 
     summary_out = gr.Textbox(label="サマリー")
-    table_out = gr.Dataframe(label="診断結果（集計表）")
+    table_all_out = gr.Dataframe(label="全項目の状態集計結果")
+    table_imp_out = gr.Dataframe(label="重要項目全体の状態集計結果")
+    table_imp_items_out = gr.Dataframe(label="重要項目ごとの状態集計結果")
     json_out = gr.JSON(label="JSON出力")
 
     run_btn.click(
         diagnose_app,
         inputs=[csv_input, excel_input, process_name, datetime_str, window_minutes],
-        outputs=[summary_out, table_out, json_out]
+        outputs=[summary_out, table_all_out, table_imp_out, table_imp_items_out, json_out]
     )
 
 if __name__ == "__main__":