Update ui/ui_app.py

ui/ui_app.py

@@ -1,251 +1,200 @@
 # ui/ui_app.py
 from __future__ import annotations
-import os, json, io
-from typing import Any, Dict, List, Tuple
-
+import json
+import shutil
 import gradio as gr
-import pandas as pd
-import plotly.graph_objects as go
-
-# 既存スコア計算（無ければ簡易版でフォールバック）
-try:
-    from core.scoring import score_company   # 既存の社内スコア（レーダー）
-except Exception:
-    def score_company(fin: Dict[str,Any]) -> Dict[str,Any]:
-        bs = fin.get("balance_sheet",{}) or {}
-        is_ = fin.get("income_statement",{}) or {}
-        safe = lambda x: float(x) if x not in (None,"") else 0.0
-        kpis = {
-            "総資産": safe(bs.get("total_assets")),
-            "売上高": safe(is_.get("sales")),
-            "営業利益": safe(is_.get("operating_income")),
-            "純利益": safe(is_.get("net_income")),
-        }
-        details = []
-        for k,v in kpis.items():
-            val = max(0.0, min(100.0, (v/(kpis["売上高"]+1e-9))*30 if k!="売上高" else 50))
-            details.append({"metric": k, "score": round(val,1)})
-        total = round(sum(d["score"] for d in details)/len(details),1) if details else 0.0
-        grade = "A" if total>=85 else "B" if total>=70 else "C" if total>=55 else "D"
-        return {"total_score": total, "grade": grade, "details": details}
-
-try:
-    from core.external_scoring import score_external  # あなたの外部評価（定量化）
-except Exception:
-    def score_external(df: pd.DataFrame) -> Dict[str,Any]:
-        return {"name":"外部評価（簡易）","external_total": 60.0, "items":[],"notes":"モジュール未検出のため簡易"}
-
-try:
-    from core.ai_judgement import make_ai_memo       # AI所見（中立）
-except Exception:
-    def make_ai_memo(fin: Dict[str,Any], score: Dict[str,Any], ext: Dict[str,Any]) -> str:
-        return "（AI所見モジュール未検出のため簡易）\n- 財務の整合性と収益性を総合的に確認してください。"
-
-from core.extract import parse_pdf  # PDF→(fin, df, meta, log)
-
-UNITS = [("自動",""), ("円","円"), ("千円","千円"), ("万円","万円"),
-         ("百万円","百万円"), ("千万円","千万円"), ("億円","億円")]
-
-def _fmt_yen(x: Any) -> str:
+
+from core.extract import parse_pdf  # (fin, df, meta, log) を返す
+
+APP_TITLE = "📊 企業スコアリング／PDF解析（Vision対応・単位自動推定）"
+
+
+# --------- ユーティリティ（UI用） ---------
+def _fmt_num(v):
+    if v is None or v == "":
+        return "—"
     try:
-        f = float(x)
-        sign = "-" if f < 0 else ""
-        f = abs(f)
-        return f"{sign}{f:,.0f} 円"
+        x = float(v)
     except Exception:
-        return "—"
+        return str(v)
+    # 大きな数は3桁区切り
+    return f"{x:,.0f}"
+
+def _get(dct, path, default=None):
+    cur = dct or {}
+    for k in path:
+        if not isinstance(cur, dict) or k not in cur:
+            return default
+        cur = cur[k]
+    return cur
+
+def render_summary(fin: dict, meta: dict) -> str:
+    """JSONをそのまま出さず、要点だけのカードをHTMLで描画"""
+    unit_label = meta.get("unit_label", "（不明）")
+    company = _get(fin, ["company", "name"], "—")
+    period = f"{_get(fin, ['period','start_date'],'—')} 〜 {_get(fin, ['period','end_date'],'—')}"
+
+    bs = fin.get("balance_sheet", {}) or {}
+    is_ = fin.get("income_statement", {}) or {}
+    cf = fin.get("cash_flows", {}) or {}
+
+    def row(label, key, src):
+        return f"""
+        <div class="row">
+          <div class="k">{label}</div>
+          <div class="v">{_fmt_num(src.get(key))}</div>
+        </div>"""
 
-def _radar(score: Dict[str, Any]) -> go.Figure:
-    labels = [d["metric"] for d in score.get("details",[])]
-    values = [d["score"] for d in score.get("details",[])]
-    if not labels:
-        labels, values = ["データ不足"], [0]
-    fig = go.Figure()
-    fig.add_trace(go.Scatterpolar(r=values + values[:1], theta=labels + labels[:1], fill="toself"))
-    fig.update_layout(polar=dict(radialaxis=dict(visible=True, range=[0, 100])),
-                      showlegend=False, margin=dict(l=20, r=20, t=30, b=20), height=380,
-                      title=f"総合スコア: {score.get('total_score',0)}（グレード: {score.get('grade','-')}）")
-    return fig
-
-def _summary_cards(fin: Dict[str,Any]) -> str:
-    bs = fin.get("balance_sheet",{}) or {}
-    is_ = fin.get("income_statement",{}) or {}
     html = f"""
     <style>
-    .cards {{display:grid;grid-template-columns:repeat(4,minmax(0,1fr));gap:12px}}
-    .card {{border:1px solid #eee;border-radius:12px;padding:12px;background:#fff;box-shadow:0 1px 2px rgba(0,0,0,.04)}}
-    .k {{font-size:.8rem;color:#666}}
-    .v {{font-size:1.1rem;font-weight:700;margin-top:6px}}
-    @media (max-width: 980px) {{ .cards {{grid-template-columns:repeat(2,minmax(0,1fr));}}}}
+    .cards {{ display:grid; grid-template-columns: repeat(3, minmax(0,1fr)); gap:12px; }}
+    @media (max-width: 1024px) {{ .cards {{ grid-template-columns: 1fr; }} }}
+    .card {{
+        background:#fff; border-radius:14px; box-shadow:0 4px 14px rgba(0,0,0,0.06);
+        padding:16px 16px 10px; border:1px solid #eee;
+    }}
+    .hd {{ font-weight:700; font-size:14px; margin-bottom:6px; color:#334; letter-spacing: .02em; }}
+    .sub {{ color:#667; font-size:12px; margin-bottom:10px; }}
+    .row {{ display:flex; justify-content:space-between; padding:6px 0; border-bottom:1px dashed #eef; }}
+    .row:last-child {{ border-bottom:none; }}
+    .k {{ color:#566; }}
+    .v {{ font-weight:600; color:#111; }}
+    .meta {{
+        margin: 8px 0 14px; color:#556; font-size:12px; display:flex; gap:18px; flex-wrap:wrap;
+    }}
+    .badge {{
+        background:#f6f7fb; border:1px solid #e7e9f2; padding:4px 8px; border-radius:999px; font-size:12px;
+    }}
     </style>
+
+    <div class="meta">
+      <span class="badge">会社名: {company}</span>
+      <span class="badge">期間: {period}</span>
+      <span class="badge">推定単位: {unit_label}</span>
+    </div>
+
     <div class="cards">
-      <div class="card"><div class="k">総資産</div><div class="v">{_fmt_yen(bs.get('total_assets'))}</div></div>
-      <div class="card"><div class="k">売上高</div><div class="v">{_fmt_yen(is_.get('sales'))}</div></div>
-      <div class="card"><div class="k">営業利益</div><div class="v">{_fmt_yen(is_.get('operating_income'))}</div></div>
-      <div class="card"><div class="k">当期純利益</div><div class="v">{_fmt_yen(is_.get('net_income'))}</div></div>
+      <div class="card">
+        <div class="hd">貸借対照表（主項目）</div>
+        <div class="sub">主要残高を表示しています</div>
+        {row("総資産", "total_assets", bs)}
+        {row("負債合計", "total_liabilities", bs)}
+        {row("純資産", "total_equity", bs)}
+        {row("流動資産", "current_assets", bs)}
+        {row("固定資産", "fixed_assets", bs)}
+        {row("流動負債", "current_liabilities", bs)}
+        {row("固定負債", "long_term_liabilities", bs)}
+      </div>
+
+      <div class="card">
+        <div class="hd">損益計算書（主項目）</div>
+        <div class="sub">売上と利益の概況</div>
+        {row("売上高", "sales", is_)}
+        {row("売上原価", "cost_of_sales", is_)}
+        {row("売上総利益", "gross_profit", is_)}
+        {row("販管費", "operating_expenses", is_)}
+        {row("営業利益", "operating_income", is_)}
+        {row("経常利益", "ordinary_income", is_)}
+        {row("当期純利益", "net_income", is_)}
+      </div>
+
+      <div class="card">
+        <div class="hd">キャッシュフロー（主項目）</div>
+        <div class="sub">各CFの方向性</div>
+        {row("営業CF", "operating_cash_flow", cf)}
+        {row("投資CF", "investing_cash_flow", cf)}
+        {row("財務CF", "financing_cash_flow", cf)}
+      </div>
     </div>
     """
     return html
 
-def build_ui():
-    with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo"), fill_height=True, analytics_enabled=False) as demo:
-        gr.Markdown("## 🧮 企業スコアリング（PDF解析 × 単位自動判定 × UI改善）")
 
-        state_fin = gr.State({})
-        state_df  = gr.State(pd.DataFrame(columns=["category","item","value"]))
-        state_unit_detected = gr.State({"label":"円","scale":1.0})
-        state_unit_current  = gr.State({"label":"円","scale":1.0})
+# --------- クリックハンドラ ---------
+def on_analyze(company: str, use_vision: bool, files):
+    try:
+        fin, df, meta, log = parse_pdf(files, company=company or "", use_vision=use_vision)
+
+        summary_html = render_summary(fin, meta)
+        fin_json = json.dumps(fin, ensure_ascii=False, indent=2)
+
+        unit_label = f"推定単位: {meta.get('unit_label','—')}（スケール: ×{int(meta.get('unit_scale',1)):,}）"
+        debug = f"[OK] 解析完了\n--- log ---\n{log}"
+
+        return summary_html, df, fin_json, unit_label, debug
+    except Exception as e:
+        msg = f"解析に失敗しました: {e}"
+        return (
+            f"<div style='color:#b11;font-weight:600'>{msg}</div>",
+            None,
+            "{}",
+            "推定単位: —",
+            msg,
+        )
+
+def on_health():
+    msgs = []
+    try:
+        import gradio
+        msgs.append(f"Gradio: {gradio.__version__}")
+    except Exception as e:
+        msgs.append(f"Gradio: 取得失敗 ({e})")
+
+    import os
+    from shutil import which
+    for b in ("pdftoppm", "pdftocairo"):
+        ok = bool(which(b))
+        msgs.append(("✅" if ok else "❌") + f" {b}: " + ("検出" if ok else "見つからず（packages.txt に poppler-utils が必要）"))
+    if os.environ.get("OPENAI_API_KEY"):
+        msgs.append("✅ OPENAI_API_KEY: 検出")
+    else:
+        msgs.append("❌ OPENAI_API_KEY: 未設定（Spaceの「Variables and secrets」に設定）")
+    return "\n".join(msgs)
+
+
+# --------- UI本体 ---------
+def build_ui() -> gr.Blocks:
+    with gr.Blocks(
+        title=APP_TITLE,
+        theme=gr.themes.Soft(primary_hue="indigo"),
+        fill_height=True,
+        analytics_enabled=False,
+    ) as demo:
+        gr.Markdown(f"## {APP_TITLE}\nPDFから主要財務項目を抽出し、**単位を自動推定**してスケーリングします。Vision失敗時はテキスト抽出にフォールバックします。")
 
         with gr.Row():
-            with gr.Column(scale=1, min_width=320):
-                company = gr.Textbox(label="企業名（任意）", placeholder="例：株式会社OO")
-                use_vision = gr.Checkbox(value=True, label="OpenAI Visionで解析")
-                files = gr.File(label="決算書PDF（複数可）", file_count="multiple", type="filepath")
-
-                with gr.Group():
-                    unit_manual = gr.Dropdown(choices=[u for u,_ in UNITS], value="自動", label="単位の上書き（任意）")
-                    apply_unit_btn = gr.Button("↻ 単位を反映（換算）")
+            with gr.Column(scale=1):
+                company = gr.Textbox(label="企業名（任意）", placeholder="例：株式会社〇〇")
+                use_vision = gr.Checkbox(value=True, label="OpenAI Visionで表を解析（推奨）")
+                files = gr.Files(
+                    label="決算書PDF（複数可）",
+                    type="filepath",
+                    file_count="multiple",
+                    file_types=[".pdf"],
+                )
+                run_btn = gr.Button("📄 解析する", variant="primary")
+                health_btn = gr.Button("🩺 環境チェック")
 
-                run_btn = gr.Button("📄 PDFを解析", variant="primary")
-                dl_csv = gr.File(label="ダウンロード（CSV）", interactive=False)
-                dl_json = gr.File(label="ダウンロード（JSON）", interactive=False)
+                unit_text = gr.Markdown("推定単位: —")
 
-                unit_info = gr.HTML()
-                debug_out = gr.Textbox(label="ログ", lines=10, interactive=False)
+                gr.Markdown("※ Vision / 画像化に失敗した場合はテキスト抽出へ自動切替します。Poppler（`poppler-utils`）必須。")
 
             with gr.Column(scale=2):
-                summary = gr.HTML()
+                summary = gr.HTML(label="サマリー（カード表示）")
                 with gr.Tabs():
-                    with gr.Tab("編集可能テーブル"):
-                        df_out = gr.Dataframe(headers=["category","item","value"], interactive=True, wrap=True)
-                        recalc_btn = gr.Button("🔁 表の値で再計算")
-                    with gr.Tab("スコア"):
-                        score_json = gr.JSON(label="スコア（内部JSON）", visible=False)
-                        chart = gr.Plot(label="スコアレーダー")
-                    with gr.Tab("外部評価"):
-                        ext_json = gr.JSON(label="外部評価（内部JSON）", visible=False)
-                        ext_md = gr.Markdown()
-                    with gr.Tab("AI所見"):
-                        insight_md = gr.Markdown()
-
-        # -------- ハンドラ --------
-        def on_analyze(company: str, use_vision: bool, files: List[str]):
-            try:
-                fin, df, meta, log = parse_pdf(files, company, use_vision)
-                # 状態に反映
-                state_fin_v = fin
-                state_df_v = df
-                det = {"label": meta["unit_label"], "scale": meta["unit_scale"]}
-                cur = det.copy()
-
-                # スコア
-                sc = score_company(fin)
-                fig = _radar(sc)
-
-                # 外部評価（数値中心）
-                ext = score_external(df)
-                ext_md_text = f"**外部評価合計:** {ext.get('external_total','—')}\n\n" \
-                              f"{ext.get('notes','')}"
-
-                # 所見
-                memo = make_ai_memo(fin, sc, ext)
-
-                # ダウンロード用ファイル作成
-                csv_path = "/tmp/result.csv"
-                json_path = "/tmp/result.json"
-                df.to_csv(csv_path, index=False)
-                with open(json_path, "w", encoding="utf-8") as f:
-                    json.dump(fin, f, ensure_ascii=False, indent=2)
-
-                unit_badge = f"<b>単位（自動）:</b> {det['label']}  ×{det['scale']:,.0f}"
-                if meta.get("warnings"):
-                    unit_badge += "<br>" + " / ".join(f"⚠️ {w}" for w in meta["warnings"])
-
-                return (
-                    fin,              # state_fin
-                    df,               # state_df
-                    det,              # detected
-                    cur,              # current
-                    _summary_cards(fin),
-                    df, fig, sc,
-                    ext, ext_md_text,
-                    memo,
-                    unit_badge,
-                    log,
-                    csv_path,
-                    json_path
-                )
-            except Exception as e:
-                import traceback
-                tb = traceback.format_exc(limit=4)
-                raise gr.Error(f"解析に失敗しました: {e}\n\n{tb}")
+                    with gr.Tab("表（編集可）"):
+                        df_out = gr.Dataframe(headers=["category", "item", "value"], interactive=True, wrap=True)
+                    with gr.Tab("抽出JSON"):
+                        fin_json = gr.Code(label="抽出JSON", language="json")
+
+                debug_out = gr.Textbox(label="ログ", lines=8, interactive=False, show_copy_button=True)
 
         run_btn.click(
             on_analyze,
             inputs=[company, use_vision, files],
-            outputs=[
-                state_fin, state_df, state_unit_detected, state_unit_current,
-                summary,
-                df_out, chart, score_json,
-                ext_json, ext_md,
-                insight_md,
-                unit_info,
-                debug_out,
-                dl_csv, dl_json
-            ],
-        )
-
-        def on_apply_unit(unit_label: str, df_cur: pd.DataFrame,
-                          det: Dict[str,Any], cur: Dict[str,Any], fin_cur: Dict[str,Any]):
-            from core.unit_utils import UNIT_SCALE, apply_unit_scale
-            # 新しい係数
-            if unit_label in ("", "自動"):
-                new = det
-            else:
-                new = {"label": unit_label, "scale": UNIT_SCALE.get(unit_label, 1.0)}
-            # 係数比で再換算（現在→新）
-            ratio = (new["scale"] / max(cur.get("scale",1.0), 1e-12))
-
-            def _scale_df(df: pd.DataFrame) -> pd.DataFrame:
-                df2 = df.copy()
-                for i in df2.index:
-                    try:
-                        v = df2.at[i,"value"]
-                        df2.at[i,"value"] = float(v) * ratio if v not in (None,"") else v
-                    except Exception:
-                        pass
-                return df2
-
-            df2 = _scale_df(df_cur)
-            fin2 = apply_unit_scale(fin_cur, ratio)  # 比率で再拡大/縮小
-
-            sc2 = score_company(fin2)
-            fig2 = _radar(sc2)
-            unit_badge = f"<b>単位（現在）:</b> {new['label']} ×{new['scale']:,.0f}"
-            return fin2, df2, new, _summary_cards(fin2), df2, fig2, sc2, unit_badge
-
-        apply_unit_btn.click(
-            on_apply_unit,
-            inputs=[unit_manual, df_out, state_unit_detected, state_unit_current, state_fin],
-            outputs=[state_fin, state_df, state_unit_current, summary, df_out, chart, score_json, unit_info]
-        )
-
-        def on_recalc(df_cur: pd.DataFrame, fin_cur: Dict[str,Any]):
-            # ユーザー編集を反映して再計算
-            fin2 = {"balance_sheet":{}, "income_statement":{}, "cash_flows":{}}
-            for _, r in df_cur.iterrows():
-                cat, item, val = str(r["category"]), str(r["item"]), r["value"]
-                try: v = None if val in (None,"","null") else float(val)
-                except Exception: v = None
-                if cat in fin2: fin2[cat][item] = v
-            sc2 = score_company(fin2)
-            fig2 = _radar(sc2)
-            return fin2, sc2, fig2, _summary_cards(fin2)
-
-        recalc_btn.click(
-            on_recalc,
-            inputs=[df_out, state_fin],
-            outputs=[state_fin, score_json, chart, summary]
+            outputs=[summary, df_out, fin_json, unit_text, debug_out],
+            concurrency_limit=1,
         )
+        health_btn.click(on_health, outputs=debug_out, concurrency_limit=1)
 
     return demo