Refactor diagnosis process to improve file handling and error reporting. Enhanced data aggregation for important items and added detailed JSON output for item-wise analysis. Updated function parameters for better clarity.

app.py

@@ -78,66 +78,125 @@ def judge_status(value, ll, l, h, hh):
     else:
         return "OK"
 
-def diagnose_process_range(process_name, datetime_str, window_minutes):
-    global df, thresholds_df
-    if df is None or thresholds_df is None:
-        return None, None, None, "⚠ ファイル未読み込み", None
+def diagnose_process_range(csv_file, excel_file, process_name, datetime_str, window_minutes):
+    try:
+        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")
+
+        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")
+    except Exception as e:
+        return None, None, None, f"❌ 入力ファイルの読み込みに失敗しました: {e}", None
+
     try:
         target_time = pd.to_datetime(datetime_str)
     except Exception:
-        return None, None, None, "⚠ 日時が不正です", None
+        return None, None, None, f"⚠ 入力した日時 {datetime_str} が無効です。", None
 
     start_time = target_time - pd.Timedelta(minutes=window_minutes)
-    df_window = df[(df["timestamp"] >= start_time) & (df["timestamp"] <= target_time)]
+    end_time = target_time
+    df_window = df[(df["timestamp"] >= start_time) & (df["timestamp"] <= end_time)]
     if df_window.empty:
-        return None, None, None, "⚠ 指定範囲にデータなし", None
+        return None, None, None, "⚠ 指定した時間幅にデータが見つかりません。", None
 
     proc_thresholds = thresholds_df[thresholds_df["ProcessNo_ProcessName"] == process_name]
     if proc_thresholds.empty:
-        return None, None, None, f"⚠ {process_name} の閾値なし", None
+        return None, None, None, f"⚠ プロセス {process_name} の閾値が設定されていません。", None
 
     all_results = []
     for _, row in df_window.iterrows():
         for _, thr in proc_thresholds.iterrows():
-            col_tuple = f"{thr['ColumnID']}_{thr['ItemName']}_{thr['ProcessNo_ProcessName']}"
+            col_tuple = (thr["ColumnID"], thr["ItemName"], thr["ProcessNo_ProcessName"])
             if col_tuple not in df.columns:
                 continue
             value = row[col_tuple]
             status = judge_status(value, thr.get("LL"), thr.get("L"), thr.get("H"), thr.get("HH"))
             all_results.append({
+                "ColumnID": thr["ColumnID"],
                 "ItemName": thr["ItemName"],
                 "判定": status,
                 "重要項目": bool(thr.get("Important", False)),
                 "時刻": str(row["timestamp"])
             })
 
+    # --- 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)
+        ["LOW-LOW", "LOW", "OK", "HIGH", "HIGH-HIGH"], fill_value=0
+    )
     status_ratio = (status_counts / total * 100).round(1)
+    result_df_all = 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})
-
+    # --- 重要項目全体集計 ---
     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)
+            ["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})
+        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(%)"])
         status_ratio_imp = pd.Series(dtype=float)
 
-    result_json = json.dumps({
+    # --- 重要項目ごと集計 ---
+    result_per_item = []
+    for item in set([r["ItemName"] for r in important_results]):  # setで重複除去
+        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)
+        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": 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"
+        )
+    )
+
+    # --- JSON ---
+    json_data = {
         "集計結果": {
-            "全項目割合": {k: convert_value(v) for k, v in status_ratio.to_dict().items()},
-            "重要項目全体割合": {k: convert_value(v) for k, v in status_ratio_imp.to_dict().items()} 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": [
+                {k: v for k, v in row.items()} for _, row in result_df_imp_items.iterrows()
+            ]
         }
-    }, ensure_ascii=False, indent=2)
+    }
+    result_json = json.dumps(json_data, ensure_ascii=False, indent=2)
 
-    summary = f"✅ {process_name} の診断完了（{start_time} ～ {target_time}）"
-    return result_df_all, result_df_imp, None, summary, result_json
+    return result_df_all, result_df_imp, result_df_imp_items, summary, result_json
 
 # --- Tab2: 傾向検出 ---
 def detect_trends_with_forecast(process_name, datetime_str, window_minutes, forecast_minutes):