Initial implementation of the project structure and core functionality.

app.py

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+# 予兆解析アプリ Gradio + MCP対応版
+
+import gradio as gr
+import pandas as pd
+import numpy as np
+from sklearn.linear_model import LinearRegression
+import json
+import os
+
+# --- ユーティリティ ---
+def normalize(s):
+    return str(s).replace("\u3000", " ").replace("\n", "").replace("\r", "").strip()
+
+def find_matching_column(df, col_id, item_name, process_name):
+    norm_item = normalize(item_name)
+    candidates = [
+        c for c in df.columns
+        if isinstance(c, str)
+        and col_id in c
+        and process_name in c
+        and norm_item in normalize(c)
+    ]
+    return candidates[0] if candidates else None
+
+# --- 予兆解析関数 ---
+def forecast_process_with_lag(csv_file, excel_file, lag_file, process_name, datetime_str, forecast_minutes):
+    try:
+        # CSV 読み込み（3行ヘッダー）
+        df = pd.read_csv(csv_file.name, header=[0, 1, 2])
+        timestamp_col = pd.to_datetime(df.iloc[:, 0], errors="coerce")
+        df = df.drop(df.columns[0], axis=1)
+        df.insert(0, "timestamp", timestamp_col)
+
+        # MultiIndex → 文字列化
+        def col_to_str(col):
+            return "_".join([str(c) for c in col if c]) if isinstance(col, tuple) else str(col)
+        df.columns = [
+            "timestamp" if (isinstance(c, str) and c == "timestamp") else col_to_str(c)
+            for c in df.columns
+        ]
+
+        # 閾値テーブル
+        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")
+
+        # ラグテーブル
+        lag_matrix = pd.read_excel(lag_file.name, index_col=0)
+
+    except Exception as e:
+        return 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)
+        forecast_time = target_time + pd.Timedelta(minutes=forecast_minutes)
+    except Exception:
+        return None, f"⚠ 入力した日時 {datetime_str} が無効です。", None
+
+    proc_thresholds = thresholds_df[(thresholds_df["ProcessNo_ProcessName"] == process_name) & (thresholds_df["Important"] == True)]
+    if proc_thresholds.empty:
+        return None, f"⚠ プロセス {process_name} に重要項目なし", None
+
+    if process_name not in lag_matrix.index:
+        return None, f"⚠ タイムラグ表に {process_name} の行がありません", None
+
+    lag_row = lag_matrix.loc[process_name].dropna()
+    lag_row = lag_row[lag_row > 0]  # 正のラグのみ
+    if lag_row.empty:
+        return None, f"⚠ プロセス {process_name} に正のラグを持つ上流工程がありません", None
+
+    results = []
+    for _, thr in proc_thresholds.iterrows():
+        y_col = find_matching_column(df, thr["ColumnID"], thr["ItemName"], thr["ProcessNo_ProcessName"])
+        if y_col is None:
+            continue
+
+        # 学習データ（直近24時間）
+        df_window = df[df["timestamp"] <= target_time].copy()
+        df_window = df_window[df_window["timestamp"] >= target_time - pd.Timedelta(hours=24)]
+        if df_window.empty:
+            continue
+
+        try:
+            base_df = df_window[["timestamp", y_col]].rename(columns={y_col: "y"})
+        except KeyError:
+            continue
+
+        merged_df = base_df.copy()
+        for up_proc, lag_min in lag_row.items():
+            try:
+                up_cols = [c for c in df.columns if isinstance(c, str) and up_proc in c]
+                for x_col in up_cols:
+                    shifted = df_window.loc[:, ["timestamp", x_col]].copy()
+                    shifted["timestamp"] = shifted["timestamp"] + pd.Timedelta(minutes=lag_min)
+                    shifted = shifted.rename(columns={x_col: f"{x_col}_lag{lag_min}"})
+                    merged_df = pd.merge_asof(
+                        merged_df.sort_values("timestamp"),
+                        shifted.sort_values("timestamp"),
+                        on="timestamp",
+                        direction="nearest"
+                    )
+            except Exception:
+                continue
+
+        X_all = merged_df.drop(columns=["timestamp", "y"], errors="ignore").values
+        Y_all = merged_df["y"].values
+        if X_all.shape[1] == 0 or len(Y_all) < 5:
+            continue
+
+        # モデル学習
+        model = LinearRegression().fit(X_all, Y_all)
+
+        # 未来予測
+        X_pred = []
+        for up_proc, lag_min in lag_row.items():
+            up_cols = [c for c in df.columns if isinstance(c, str) and up_proc in c]
+            for x_col in up_cols:
+                try:
+                    ref_time = forecast_time - pd.Timedelta(minutes=lag_min)
+                    idx = (df["timestamp"] - ref_time).abs().idxmin()
+                    X_pred.append(df.loc[idx, x_col])
+                except Exception:
+                    continue
+        if not X_pred:
+            continue
+
+        pred_val = model.predict([X_pred])[0]
+
+        # 閾値リスク判定
+        ll, l, h, hh = thr.get("LL"), thr.get("L"), thr.get("H"), thr.get("HH")
+        risk = "OK"
+        if pd.notna(ll) and pred_val <= ll:
+            risk = "LOW-LOW"
+        elif pd.notna(l) and pred_val <= l:
+            risk = "LOW"
+        elif pd.notna(hh) and pred_val >= hh:
+            risk = "HIGH-HIGH"
+        elif pd.notna(h) and pred_val >= h:
+            risk = "HIGH"
+
+        results.append({
+            "ItemName": thr["ItemName"],
+            "予測値": round(float(pred_val), 3),
+            "予測時刻": str(forecast_time),
+            "予測リスク": risk,
+            "使用上流工程数": len(lag_row)
+        })
+
+    result_df = pd.DataFrame(results)
+    result_json = json.dumps(results, ensure_ascii=False, indent=2)
+    summary = f"✅ {process_name} の予兆解析完了 ({target_time} → {forecast_time})"
+    return result_df, summary, result_json
+
+# --- Gradio UI ---
+with gr.Blocks(css="body {overflow-y: scroll;}") as demo:
+    gr.Markdown("## 予兆解析アプリ (MCP対応)")
+
+    with gr.Row():
+        csv_input = gr.File(label="CSVファイルをアップロード", file_types=[".csv"], type="filepath")
+        excel_input = gr.File(label="Excel閾値ファイルをアップロード", file_types=[".xlsx"], type="filepath")
+        lag_input = gr.File(label="タイムラグファイルをアップロード", file_types=[".xlsx"], type="filepath")
+
+    process_name = gr.Textbox(label="プロセス名", value="E018-A012_除害RO")
+    datetime_str = gr.Textbox(label="基準日時", value="2025/8/2 0:05")
+    forecast_minutes = gr.Number(label="予測時間幅（分）", value=60)
+
+    run_btn = gr.Button("予兆解析を実行")
+
+    result_df = gr.Dataframe(label="予兆解析結果", wrap=True, interactive=False)
+    summary_output = gr.Textbox(label="サマリー")
+    json_output = gr.Json(label="JSON結果")
+
+    run_btn.click(
+        forecast_process_with_lag,
+        inputs=[csv_input, excel_input, lag_input, process_name, datetime_str, forecast_minutes],
+        outputs=[result_df, summary_output, json_output]
+    )
+
+if __name__ == "__main__":
+    use_mcp = os.getenv("USE_MCP", "0") == "1"
+    if use_mcp:
+        demo.launch(mcp_server=True)
+    else:
+        demo.launch(server_name="0.0.0.0", share=False)