Update ui/ui_app.py

ui/ui_app.py

@@ -1,7 +1,7 @@
 # ui/ui_app.py
 from __future__ import annotations
 import json
-from typing import Dict, Any, List
+from typing import Dict, Any, List, Tuple
 
 import gradio as gr
 import plotly.graph_objects as go
@@ -17,23 +17,24 @@ from core.external_scoring import (
 )
 from core.ai_judgement import suggest_external_with_llm, ai_evaluate
 
-# ---------- chart helpers ----------
+# ---------- charts ----------
 def _radar(title: str, cat_scores: Dict[str, float]) -> go.Figure:
     if not cat_scores:
         cat_scores = {"N/A": 0.0}
     labels = list(cat_scores.keys())
-    vals = [cat_scores[k] for k in labels]
+    vals = [float(cat_scores[k] or 0.0) for k in labels]
     fig = go.Figure()
-    fig.add_trace(go.Scatterpolar(r=vals+[vals[0]], theta=labels+[labels[0]], fill="toself", name=title))
-    fig.update_layout(polar=dict(radialaxis=dict(visible=True, range=[0,100])),
-                      showlegend=False, height=360, margin=dict(l=30,r=30,t=40,b=30), title=title)
+    fig.add_trace(go.Scatterpolar(r=vals + [vals[0]], theta=labels + [labels[0]], fill="toself", name=title))
+    fig.update_layout(polar=dict(radialaxis=dict(visible=True, range=[0, 100])),
+                      showlegend=False, height=360, margin=dict(l=30, r=30, t=40, b=30), title=title)
     return fig
 
-def _diff_bar(ext: Dict[str,float], ai: Dict[str,float]) -> go.Figure:
+def _diff_bar(ext: Dict[str, float], ai: Dict[str, float]) -> go.Figure:
     ks = sorted(set(ext.keys()) | set(ai.keys()))
-    diffs = [(ai.get(k,0)-ext.get(k,0)) for k in ks]
+    diffs = [(float(ai.get(k, 0.0)) - float(ext.get(k, 0.0))) for k in ks]
     fig = go.Figure(data=[go.Bar(x=ks, y=diffs)])
-    fig.update_layout(title="AI評点 - 外部評価（カテゴリ差分）", height=320, margin=dict(l=30,r=30,t=40,b=30))
+    fig.update_layout(title="AI評点 - 外部評価（カテゴリ差分）",
+                      height=320, margin=dict(l=30, r=30, t=40, b=30))
     return fig
 
 def _fmt(x):
@@ -44,16 +45,17 @@ def _fmt(x):
         if abs(f) >= 1e3: return f"{f/1e3:.1f}千"
         return f"{f:.0f}"
     except Exception:
-        return str(x) if x not in (None,"") else "—"
+        return str(x) if x not in (None, "") else "—"
 
 def _cards(company, meta, fin, ext_total, ai_total) -> str:
     bs = fin.get("balance_sheet", {}) or {}; is_ = fin.get("income_statement", {}) or {}
     ta = bs.get("total_assets") or 0; te = bs.get("total_equity") or 0
-    er = ""
+    er = "—"
     try:
-        ta = float(ta); te = float(te); er = f"{(te/ta*100):.1f}%" if ta>0 else "—"
+        ta = float(ta); te = float(te)
+        er = f"{(te/ta*100):.1f}%" if ta>0 else "—"
     except Exception:
-        er = "—"
+        pass
     period = ""
     if meta and isinstance(meta.get("period"), dict):
         period = f"{meta['period'].get('start_date','')} ~ {meta['period'].get('end_date','')}"
@@ -88,7 +90,7 @@ def _cards(company, meta, fin, ext_total, ai_total) -> str:
 </div>
 """
 
-# ---------- core flows ----------
+# ---------- market df helpers ----------
 def _market_df_from_dict(d: Dict[str, Any]) -> pd.DataFrame:
     rows = []
     order = [
@@ -101,94 +103,136 @@ def _market_df_from_dict(d: Dict[str, Any]) -> pd.DataFrame:
 
 def _dict_from_market_df(df: pd.DataFrame) -> Dict[str, Any]:
     out = {}
-    for _, r in df.iterrows():
-        k = str(r["指標"]); v = r["値"]
-        try:
-            out[k] = float(v)
-        except Exception:
-            out[k] = None
+    try:
+        for _, r in df.iterrows():
+            k = str(r["指標"]); v = r["値"]
+            try:
+                out[k] = float(v)
+            except Exception:
+                out[k] = None
+    except Exception:
+        # 列崩れ対策：空dictで返す
+        return {}
     return out
 
+# ---------- flows ----------
 def on_analyze(company: str, use_vision: bool, files: List[str]):
-    if not files:
-        raise gr.Error("PDF をアップロードしてください。")
-    fin, df_fin, meta, log = parse_pdf(files, company, use_vision)
-
-    # 外部入力テンプレ＋不足項目のLLMサジェスト（従来どおり）
-    ext_df = get_external_template_df()
-    ext_df = fill_missing_with_external(ext_df, suggest_external_with_llm(fin, company))
-
-    # 初期（空の市場DF）
-    market_df = _market_df_from_dict({})
-    # 外部/AIは市場未反映のまま仮計算（UI起動のため）
-    ext_res = score_external_from_df(ext_df)
-    ai_res  = ai_evaluate(fin, {})
-
-    cards = _cards(company, meta, fin, ext_res["external_total"], ai_res["ai_total"])
-    ext_fig = _radar("外部評価（カテゴリ）", ext_res.get("category_scores", {}))
-    ai_fig  = _radar("AI評点（カテゴリ）", ai_res.get("category_scores", {}))
-    diff    = _diff_bar(ext_res.get("category_scores", {}), ai_res.get("category_scores", {}))
-
-    return (cards, df_fin, ext_df, market_df,
-            ext_res["external_total"], ai_res["ai_total"],
-            ext_fig, ai_fig, diff,
-            json.dumps(fin, ensure_ascii=False, indent=2),
-            json.dumps(ext_res, ensure_ascii=False, indent=2),
-            json.dumps(ai_res, ensure_ascii=False, indent=2),
-            "\n".join([str(x) for x in (log if isinstance(log,list) else [log])]))
+    """
+    例外はcatchしてUIに出す。常に所定の型で返す。
+    """
+    try:
+        if not files:
+            raise RuntimeError("PDF をアップロードしてください。")
+        fin, df_fin, meta, log = parse_pdf(files, company, use_vision)
+
+        # 外部テンプレ & 埋め候補
+        ext_df = get_external_template_df()
+        ext_df = fill_missing_with_external(ext_df, suggest_external_with_llm(fin, company))
+
+        # 市場（空の表）/ 仮スコア
+        market_df = _market_df_from_dict({})
+        ext_res = score_external_from_df(ext_df)
+        ai_res  = ai_evaluate(fin, {})
+
+        cards = _cards(company, meta, fin, ext_res["external_total"], ai_res["ai_total"])
+        ext_fig = _radar("外部評価（カテゴリ）", ext_res.get("category_scores", {}))
+        ai_fig  = _radar("AI評点（カテゴリ）", ai_res.get("category_scores", {}))
+        diff    = _diff_bar(ext_res.get("category_scores", {}), ai_res.get("category_scores", {}))
+
+        return (cards, df_fin, ext_df, market_df,
+                float(ext_res["external_total"]), float(ai_res["ai_total"]),
+                ext_fig, ai_fig, diff,
+                json.dumps(fin, ensure_ascii=False, indent=2),
+                json.dumps(ext_res, ensure_ascii=False, indent=2),
+                json.dumps(ai_res, ensure_ascii=False, indent=2),
+                "\n".join([str(x) for x in (log if isinstance(log,list) else [log])]))
+    except Exception as e:
+        # 失敗してもUIが壊れないように空プレースホルダで返す
+        empty_df = pd.DataFrame(columns=["カテゴリー","入力項目","値"])
+        return (
+            f"<div style='color:#b91c1c'>解析に失敗: {e}</div>",
+            pd.DataFrame(columns=["category","item","value"]),
+            empty_df,
+            _market_df_from_dict({}),
+            0.0, 0.0,
+            _radar("外部評価（カテゴリ）", {}),
+            _radar("AI評点（カテゴリ）", {}),
+            _diff_bar({}, {}),
+            "{}", "{}","{}",
+            f"TRACE: {type(e).__name__}: {e}"
+        )
 
 def on_market_infer(industry: str, products_text: str, country: str, horizon: int,
                     ext_df: pd.DataFrame, fin_json: str):
-    prods = [p.strip() for p in (products_text or "").splitlines() if p.strip()]
-    market = infer_market_metrics(industry, prods, country, horizon)
-    market_df = _market_df_from_dict(market)
-
-    # ext_dfに市場推定を統合
-    ext_df2 = merge_market_into_external_df(ext_df, market, prods)
-
-    # スコア更新
-    fin = json.loads(fin_json)
-    ext_res = score_external_from_df(ext_df2)
-    # AIは市場の“別軸”影響を軽めに（成長余地構成で反映）
-    ext_like = {"市場の年成長率(%)": market.get("市場の年成長率(%)"),
-                "主力商品数": len(prods),
-                "成長中主力商品数": sum(1 for p in prods if (market.get("製品別年成長率(%)",{}).get(p,0) or 0)>10)}
-    ai_res = ai_evaluate(fin, ext_like)
-
-    ext_fig = _radar("外部評価（カテゴリ）", ext_res.get("category_scores", {}))
-    ai_fig  = _radar("AI評点（カテゴリ）", ai_res.get("category_scores", {}))
-    diff    = _diff_bar(ext_res.get("category_scores", {}), ai_res.get("category_scores", {}))
-
-    return (market_df, ext_df2,
-            ext_res["external_total"], ai_res["ai_total"],
-            ext_fig, ai_fig, diff,
-            json.dumps(ext_res, ensure_ascii=False, indent=2),
-            json.dumps(ai_res, ensure_ascii=False, indent=2),
-            "市場推定OK: " + "; ".join(market.get("注記", [])[:3]))
+    try:
+        prods = [p.strip() for p in (products_text or "").splitlines() if p.strip()]
+        market = infer_market_metrics(industry or "", prods, country or "JP", int(horizon or 3))
+        market_df = _market_df_from_dict(market)
+
+        # ext_dfに市場推定を統合
+        ext_df2 = merge_market_into_external_df(ext_df, market, prods)
+
+        # スコア更新
+        fin = json.loads(fin_json or "{}")
+        ext_res = score_external_from_df(ext_df2)
+
+        ext_like = {
+            "市場の年成長率(%)": market.get("市場の年成長率(%)"),
+            "主力商品数": len(prods),
+            "成長中主力商品数": sum(1 for p in prods if (market.get("製品別年成長率(%)",{}).get(p,0) or 0)>10)
+        }
+        ai_res = ai_evaluate(fin, ext_like)
+
+        ext_fig = _radar("外部評価（カテゴリ）", ext_res.get("category_scores", {}))
+        ai_fig  = _radar("AI評点（カテゴリ）", ai_res.get("category_scores", {}))
+        diff    = _diff_bar(ext_res.get("category_scores", {}), ai_res.get("category_scores", {}))
+
+        return (market_df, ext_df2,
+                float(ext_res["external_total"]), float(ai_res["ai_total"]),
+                ext_fig, ai_fig, diff,
+                json.dumps(ext_res, ensure_ascii=False, indent=2),
+                json.dumps(ai_res, ensure_ascii=False, indent=2),
+                "市場推定OK: " + "; ".join(market.get("注記", [])[:3]))
+    except Exception as e:
+        return (_market_df_from_dict({}),
+                ext_df,
+                0.0, 0.0,
+                _radar("外部評価（カテゴリ）", {}),
+                _radar("AI評点（カテゴリ）", {}),
+                _diff_bar({}, {}),
+                "{}", "{}", f"市場推定に失敗: {e}")
 
 def on_rescore_all(ext_df: pd.DataFrame, market_df: pd.DataFrame, fin_json: str, products_text: str):
-    fin = json.loads(fin_json)
-    prods = [p.strip() for p in (products_text or "").splitlines() if p.strip()]
-    market = _dict_from_market_df(market_df)
-    # ext_dfへ反映（手で編集されたmarket_dfも取り込む）
-    ext_df2 = merge_market_into_external_df(ext_df, market, prods)
-
-    ext_res = score_external_from_df(ext_df2)
-    ext_like = {"市場の年成長率(%)": market.get("市場の年成長率(%)"),
-                "主力商品数": len(prods),
-                "成長中主力商品数": sum(1 for p in prods if (market.get("製品別年成長率(%)",{}).get(p,0) or 0)>10)}
-    ai_res = ai_evaluate(fin, ext_like)
-
-    ext_fig = _radar("外部評価（カテゴリ）", ext_res.get("category_scores", {}))
-    ai_fig  = _radar("AI評点（カテゴリ）", ai_res.get("category_scores", {}))
-    diff    = _diff_bar(ext_res.get("category_scores", {}), ai_res.get("category_scores", {}))
-
-    return (ext_df2,
-            ext_res["external_total"], ai_res["ai_total"],
-            ext_fig, ai_fig, diff,
-            json.dumps(ext_res, ensure_ascii=False, indent=2),
-            json.dumps(ai_res, ensure_ascii=False, indent=2),
-            "再計算完了")
+    try:
+        fin = json.loads(fin_json or "{}")
+        prods = [p.strip() for p in (products_text or "").splitlines() if p.strip()]
+        market = _dict_from_market_df(market_df)
+        ext_df2 = merge_market_into_external_df(ext_df, market, prods)
+
+        ext_res = score_external_from_df(ext_df2)
+        ext_like = {
+            "市場の年成長率(%)": market.get("市場の年成長率(%)"),
+            "主力商品数": len(prods),
+            "成長中主力商品数": sum(1 for p in prods if (market.get("製品別年成長率(%)",{}).get(p,0) or 0)>10)
+        }
+        ai_res = ai_evaluate(fin, ext_like)
+
+        ext_fig = _radar("外部評価（カテゴリ）", ext_res.get("category_scores", {}))
+        ai_fig  = _radar("AI評点（カテゴリ）", ai_res.get("category_scores", {}))
+        diff    = _diff_bar(ext_res.get("category_scores", {}), ai_res.get("category_scores", {}))
+
+        return (ext_df2,
+                float(ext_res["external_total"]), float(ai_res["ai_total"]),
+                ext_fig, ai_fig, diff,
+                json.dumps(ext_res, ensure_ascii=False, indent=2),
+                json.dumps(ai_res, ensure_ascii=False, indent=2),
+                "再計算完了")
+    except Exception as e:
+        return (ext_df, 0.0, 0.0,
+                _radar("外部評価（カテゴリ）", {}),
+                _radar("AI評点（カテゴリ）", {}),
+                _diff_bar({}, {}),
+                "{}", "{}", f"再計算に失敗: {e}")
 
 def build_ui():
     with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo"), analytics_enabled=False) as demo:
@@ -211,7 +255,6 @@ def build_ui():
                 country = gr.Dropdown(choices=["JP","US","EU","APAC","GLOBAL"], value="JP", label="対象地域")
                 horizon = gr.Slider(1, 7, value=3, step=1, label="予測年数")
                 infer_btn = gr.Button("🔎 市場を推定（LLM）", variant="secondary")
-
             market_df = gr.Dataframe(label="市場メトリクス（編集可）", interactive=True, wrap=True)
 
         with gr.Tab("外部入力/財務"):
@@ -220,8 +263,8 @@ def build_ui():
 
         with gr.Tab("スコア"):
             with gr.Row():
-                ext_total = gr.Number(label="外部評価 合計(0-100)", value=0, precision=1, interactive=False)
-                ai_total  = gr.Number(label="AI評点 合計(0-100)", value=0, precision=1, interactive=False)
+                ext_total = gr.Number(label="外部評価 合計(0-100)", value=0.0, precision=1, interactive=False)
+                ai_total  = gr.Number(label="AI評点 合計(0-100)", value=0.0, precision=1, interactive=False)
             with gr.Row():
                 ext_plot = gr.Plot(label="外部評価（レーダー）")
                 ai_plot  = gr.Plot(label="AI評点（レーダー）")
@@ -241,8 +284,8 @@ def build_ui():
         run_btn.click(
             on_analyze,
             inputs=[company, use_vision, files],
-            outputs=[cards, df_fin, ext_df, market_df, ext_total, ai_total, ext_plot, ai_plot, diff_plot,
-                     fin_json, ext_json, ai_json, debug],
+            outputs=[cards, df_fin, ext_df, market_df, ext_total, ai_total,
+                     ext_plot, ai_plot, diff_plot, fin_json, ext_json, ai_json, debug],
         ).then(lambda x: x, inputs=[fin_json], outputs=[fin_state])
 
         infer_btn.click(
@@ -256,5 +299,4 @@ def build_ui():
             inputs=[ext_df, market_df, fin_state, products],
             outputs=[ext_df, ext_total, ai_total, ext_plot, ai_plot, diff_plot, ext_json, ai_json, debug],
         )
-
     return demo