Refactor file handling in diagnosis function and update Gradio UI for file input types

app.py

@@ -2,7 +2,6 @@ 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"
@@ -15,20 +14,19 @@ def judge_status(value, ll, l, h, hh):
     else:
         return "OK"
 
-# --- 診断処理 ---
 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
 
-    # CSV読み込み（3行ヘッダー維持）
-    df = pd.read_csv(csv_file.name, header=[0, 1, 2])
+    # CSV読み込み（3行ヘッダー）
+    df = pd.read_csv(csv_file, 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")
 
-    # 閾値テーブル読み込み
-    thresholds_df = pd.read_excel(excel_file.name)
+    # 閾値テーブル
+    thresholds_df = pd.read_excel(excel_file)
     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:
@@ -40,20 +38,16 @@ def diagnose_app(csv_file, excel_file, process_name, datetime_str, window_minute
     except Exception:
         return f"⚠ 入力した日時 {datetime_str} が無効です", None, None
 
-    # 時間幅のデータ抽出
     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
 
-    # プロセスの閾値行を抽出
     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
 
-    # 判定実行
     all_results = []
     for _, row in df_window.iterrows():
         for _, thr in proc_thresholds.iterrows():
@@ -71,7 +65,6 @@ def diagnose_app(csv_file, excel_file, process_name, datetime_str, window_minute
                 "時刻": row["timestamp"]
             })
 
-    # 集計（件数と割合）
     total = len(all_results)
     if total == 0:
         return f"⚠ プロセス {process_name} の診断結果なし", None, None
@@ -96,14 +89,13 @@ def diagnose_app(csv_file, excel_file, process_name, datetime_str, window_minute
 
     return summary, result_df, 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="file")
-        excel_input = gr.File(label="閾値テーブルをアップロード", type="file")
+        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")
 
     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)")
@@ -123,8 +115,7 @@ with gr.Blocks() as demo:
 
 if __name__ == "__main__":
     import os
-    # Hugging Face / ローカル両対応
-    if os.getenv("SPACE_ID"):
+    if os.getenv("SPACE_ID"):  # Hugging Face環境
         demo.launch(server_name="0.0.0.0")
-    else:
+    else:  # ローカル
         demo.launch()