Update trend detection application to support MCP, enhance forecasting comments, and improve JSON output handling. Adjust Gradio UI for simultaneous web and MCP server launch.

app.py

@@ -1,4 +1,4 @@
-# 傾向検出アプリ（Gradio版・重要項目ごとの詳細＋JSONダウンロード）修正版
+# 傾向検出アプリ（Gradio版・MCP対応）
 
 import gradio as gr
 import pandas as pd
@@ -8,10 +8,9 @@ import json
 import tempfile
 import os
 
-# --- 状態判定＆未来予測関数 ---
+# --- 傾向検出＋未来予測関数 ---
 def detect_trends_with_forecast(process_name, datetime_str, window_minutes, forecast_minutes, csv_file, excel_file):
     try:
-        # ★ 修正ポイント: gr.File(type="filepath") は文字列パスが来る
         csv_path = csv_file
         excel_path = excel_file
 
@@ -22,7 +21,7 @@ def detect_trends_with_forecast(process_name, datetime_str, window_minutes, fore
         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
+        # 閾値テーブル
         thresholds_df = pd.read_excel(excel_path)
         thresholds_df["Important"] = thresholds_df["Important"].astype(str).str.upper().map({"TRUE": True, "FALSE": False})
         for col in ["LL", "L", "H", "HH"]:
@@ -69,7 +68,7 @@ def detect_trends_with_forecast(process_name, datetime_str, window_minutes, fore
             last_val = series.iloc[-1]
             n = len(series)
 
-            # 未来予測（最小1ステップ）
+            # 未来予測
             forecast_steps = max(1, int(round(forecast_minutes / interval_minutes)))
             forecast_index = n + forecast_steps
             forecast_val = model.predict([[forecast_index]])[0][0]
@@ -114,18 +113,14 @@ def detect_trends_with_forecast(process_name, datetime_str, window_minutes, fore
 
         result_df = pd.DataFrame(results)
 
-        # JSONテキスト
+        # JSON保存（ファイルパスを返す）
         result_json = json.dumps(results, ensure_ascii=False, indent=2)
-
-        # ★ 修正ポイント: BytesIO ではなく、一時ファイルに保存して「パス」を返す
         fd, tmp_path = tempfile.mkstemp(suffix=".json", prefix="trend_result_")
         os.close(fd)
         with open(tmp_path, "w", encoding="utf-8") as f:
             f.write(result_json)
 
         summary = f"✅ {process_name} の傾向検出＋未来予測完了（{start_time}～{end_time}, 予測+{forecast_minutes}分）"
-
-        # gr.File 出力には「ファイルパス（文字列）」を返す
         return result_df, summary, tmp_path
 
     except Exception as e:
@@ -152,7 +147,6 @@ with gr.Blocks() as demo:
     with gr.Row():
         result_table = gr.Dataframe(label="傾向＋未来予測結果", wrap=True)
     summary_output = gr.Textbox(label="サマリー")
-    # ★ 修正ポイント: gr.File の出力にはパスを渡す。type指定は不要（出力）
     json_download = gr.File(label="JSON結果ダウンロード")
 
     run_btn.click(
@@ -162,8 +156,10 @@ with gr.Blocks() as demo:
     )
 
 if __name__ == "__main__":
-    use_mcp = os.getenv("USE_MCP", "0") == "1"
-    if use_mcp:
-        demo.launch(server_name="0.0.0.0", mcp_server=True)
-    else:
-        demo.launch(server_name="0.0.0.0")
+    # Web UI と MCP を同時に有効化
+    demo.launch(
+        server_name="0.0.0.0",
+        mcp_server=True,   # MCP サーバー起動
+        share=False,       # Hugging Face なら不要
+        ssr_mode=False     # SSR は無効に
+    )