Update ui/ui_app.py

ui/ui_app.py

@@ -1,309 +1,241 @@
 # ui/ui_app.py
 from __future__ import annotations
-import json, traceback, base64, math
-from typing import Any, Dict, List
+import os, io, json, base64, traceback
+from typing import Any, Dict, List, Tuple
+
 import gradio as gr
 import pandas as pd
 import plotly.graph_objects as go
 
-from core.pdf_io import pdf_to_images, pdf_to_text
-from core.extract import extract_financials
-from core.scoring import score_company
-from core.external_score import (
-    get_external_template_df, fill_missing_with_external, score_external_from_df, apply_llm_signals_to_df
-)
-from core.units import detect_unit, unit_factor, scale_financials_yen
-from core.openai_client import VISION_MODEL, TEXT_MODEL, get_client
-from core.llm_quant import extract_market_product_signals
-
-# ===== helpers =====
-def fin_to_df(fin: Dict[str, Any]) -> pd.DataFrame:
-    rows = []
-    def add(cat, d):
-        for k, v in (d or {}).items():
-            rows.append({"category": cat, "item": k, "value": v})
-    add("balance_sheet", fin.get("balance_sheet"))
-    add("income_statement", fin.get("income_statement"))
-    add("cash_flows", fin.get("cash_flows"))
-    return pd.DataFrame(rows, columns=["category", "item", "value"])
-
-def df_to_fin(df: pd.DataFrame) -> Dict[str, Any]:
-    out = {"balance_sheet": {}, "income_statement": {}, "cash_flows": {}}
-    for _, r in df.iterrows():
-        cat, item, val = str(r["category"]), str(r["item"]), r["value"]
-        try:
-            parsed = None if val in (None, "", "null") else float(val)
-        except Exception:
-            parsed = None
-        if cat in out:
-            out[cat][item] = parsed
-    return out
-
-def _fmt_yen(n: float) -> str:
-    if n is None: return "—"
-    try:
-        n = float(n)
-    except Exception:
-        return "—"
-    # 視認性のため 兆/億/万円 に自動スケール
-    absn = abs(n)
-    if absn >= 1e12:
-        return f"{n/1e12:.2f} 兆円"
-    if absn >= 1e8:
-        return f"{n/1e8:.2f} 億円"
-    if absn >= 1e4:
-        return f"{n/1e4:.1f} 万円"
-    return f"{int(n):,} 円"
+# 既存の抽出/スコア関数を利用
+from core.extract import parse_pdf  # あなたの既存実装をインポート（PDF→fin(dict), df）
+from core.scoring import score_company           # 社内ルールのスコア(dict)
+from core.external_scoring import score_external # 外部評価（定量化）(dict)
+from core.ai_judgement import make_ai_memo       # AI所見（中立）
 
-def _fmt_pct(r: float) -> str:
-    if r is None: return "—"
+# ============ 小さなUIヘルパ ============
+def _safe_json_loads(s: str|Dict[str,Any]|None) -> Dict[str,Any]:
+    if s is None: return {}
+    if isinstance(s, dict): return s
     try:
-        return f"{r*100:.2f}%"
+        return json.loads(s)
     except Exception:
-        return "—"
-
-def radar(score: Dict[str, Any]) -> go.Figure:
-    labels = [d["metric"] for d in score["details"]]
-    values = [d["score"] for d in score["details"]]
-    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['total_score']}（グレード: {score['grade']}）"
-    )
-    return fig
-
-def health() -> str:
-    msgs = []
-    try:
-        _ = get_client()
-        msgs.append("✅ OpenAI: OK")
-    except Exception as e:
-        msgs.append(f"❌ OpenAI: {e}")
-    msgs.append(f"ℹ️ Vision={VISION_MODEL} / Text={TEXT_MODEL}")
-    return "<br>".join(msgs)
-
-def kpi_cards_html(fin: Dict[str, Any], score: Dict[str, Any]) -> str:
-    bs = fin.get("balance_sheet", {}) or {}
-    pl = fin.get("income_statement", {}) or {}
-    assets = bs.get("total_assets")
-    equity = bs.get("total_equity")
-    curA = bs.get("current_assets"); curL = bs.get("current_liabilities")
-    sales = pl.get("sales"); op = pl.get("operating_income"); net = pl.get("net_income")
-
-    equity_ratio = (equity or 0) / (assets or 1) if assets else None
-    current_ratio = (curA or 0) / (curL or 1) if curL else None
-    opm = (op or 0) / (sales or 1) if sales else None
-    npm = (net or 0) / (sales or 1) if sales else None
-    roa = (net or 0) / (assets or 1) if assets else None
-
-    def badge(val, kind="pct"):
-        if val is None: return '<span class="px-2 py-1 rounded bg-gray-200 text-gray-700">—</span>'
-        v = val*100 if kind=="pct" else val
-        col = "#22c55e" if v >= (20 if kind=="pct" else 0) else "#f59e0b" if v >= (10 if kind=="pct" else 0) else "#ef4444"
-        return f'<span class="px-2 py-1 rounded" style="background:{col}20;color:{col}">{v:.2f}{"%" if kind=="pct" else ""}</span>'
-
-    html = f"""
-<div style="display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:12px">
-  <div class="card" style="padding:12px;border:1px solid #eee;border-radius:12px">
-    <div>総資産</div><div style="font-size:1.2rem;font-weight:700">{_fmt_yen(assets)}</div>
-  </div>
-  <div class="card" style="padding:12px;border:1px solid #eee;border-radius:12px">
-    <div>売上高</div><div style="font-size:1.2rem;font-weight:700">{_fmt_yen(sales)}</div>
-  </div>
-  <div class="card" style="padding:12px;border:1px solid #eee;border-radius:12px">
-    <div>営業利益</div><div style="font-size:1.2rem;font-weight:700">{_fmt_yen(op)}</div>
-  </div>
-  <div class="card" style="padding:12px;border:1px solid #eee;border-radius:12px">
-    <div>自己資本比率</div><div style="font-size:1.1rem;font-weight:700">{_fmt_pct(equity_ratio)} {badge(equity_ratio)}</div>
-  </div>
-  <div class="card" style="padding:12px;border:1px solid #eee;border-radius:12px">
-    <div>流動比率</div><div style="font-size:1.1rem;font-weight:700">{_fmt_pct(current_ratio)} {badge(current_ratio)}</div>
-  </div>
-  <div class="card" style="padding:12px;border:1px solid #eee;border-radius:12px">
-    <div>ROA</div><div style="font-size:1.1rem;font-weight:700">{_fmt_pct(roa)} {badge(roa)}</div>
-  </div>
-</div>
-<div style="margin-top:8px;color:#64748b">社内スコア: <b>{score['total_score']}</b> （グレード {score['grade']}）</div>
-"""
+        return {}
+
+def _kpi_cards_html(score: Dict[str,Any], ext: Dict[str,Any]) -> str:
+    total = score.get("total_score", None)
+    grade = score.get("grade", "-")
+    ext_total = ext.get("external_total", None)
+
+    gap = None
+    if isinstance(total, (int, float)) and isinstance(ext_total, (int, float)):
+        gap = round(total - ext_total, 1)
+
+    def kpi(label: str, value: str, sub: str = "") -> str:
+        return f"""
+        <div class="kpi">
+          <div class="kpi-label">{label}</div>
+          <div class="kpi-value">{value}</div>
+          <div class="kpi-sub">{sub}</div>
+        </div>"""
+
+    html = """
+    <style>
+      .kpi-wrap {display:grid;grid-template-columns:repeat(4,minmax(180px,1fr));gap:12px;margin-top:4px;}
+      .kpi{background:#fff;border:1px solid #e9eef5;border-radius:14px;padding:14px;box-shadow:0 2px 6px rgba(0,0,0,0.04);}
+      .kpi-label{color:#64748b;font-size:12px;margin-bottom:6px;}
+      .kpi-value{font-size:26px;font-weight:700;}
+      .kpi-sub{color:#94a3b8;font-size:11px;margin-top:2px;}
+      @media (max-width: 860px){.kpi-wrap{grid-template-columns:repeat(2,1fr);} }
+    </style>
+    <div class="kpi-wrap">
+    """
+
+    html += kpi("総合スコア（社内）", f"{total if total is not None else '—'}", f"グレード: {grade}")
+    html += kpi("外部評価（定量）", f"{ext_total if ext_total is not None else '—'}", "100点満点")
+    html += kpi("ギャップ（社内−外部）", f"{gap if gap is not None else '—'}", "＋は社内＞外部")
+    # 代表的な指標（上位1・下位1）をサマリに
+    det = score.get("details", []) or []
+    if det:
+        best = max(det, key=lambda x: x.get("score", 0))
+        worst = min(det, key=lambda x: x.get("score", 0))
+        html += kpi("強み指標", f"{best.get('metric','—')}", f"スコア {round(best.get('score',0),1)}")
+        # 4つ目で既に埋まっているので、下位はサブテキストで表示
+        html = html.replace("</div></div>", f"<div class='kpi-sub'>弱み指標: {worst.get('metric','—')} / {round(worst.get('score',0),1)}</div></div>", 1)
+    else:
+        html += kpi("指標ハイライト", "—", "データ不足")
+
+    html += "</div>"
     return html
 
-# ===== main actions =====
-def run_analyze(company: str, use_vision: bool, unit_sel: str, use_pdf_for_ext: bool, aux_text: str, files: list[str]):
-    if not files:
-        raise gr.Error("PDF をアップロードしてください。")
-
-    # 1) 単位の自動推定
-    first_text = pdf_to_text(files[0], pages=2)
-    detected = detect_unit(first_text) or "円"
-    unit_label = unit_sel if unit_sel != "自動推定" else detected
-    factor = unit_factor(unit_label)
-
-    # 2) 抽出（Vision→失敗時Text）
-    try:
-        imgs: List[bytes] = []
-        for p in files:
-            imgs += pdf_to_images(p, dpi=200, max_pages=6)
-        fin_raw = extract_financials(imgs if use_vision else None, None, company or "")
-    except Exception:
-        text_blob = ""
-        for p in files:
-            text_blob += pdf_to_text(p) + "\n\n"
-        fin_raw = extract_financials(None, text_blob, company or "")
-
-    # 3) 円換算
-    fin_yen = scale_financials_yen(fin_raw, factor=factor)
-
-    # 4) 社内スコア
-    df = fin_to_df(fin_yen)
-    score = score_company(fin_yen)
-    fig = radar(score)
-    kpi_html = kpi_cards_html(fin_yen, score)
-
-    # 5) 外部評価に使う LLM 定量シグナル（市場/製品）
-    signals = None
-    try:
-        ext_text = ""
-        if use_pdf_for_ext:
-            # 追加でテキストを多めに取得（セクション説明文など）
-            for p in files:
-                ext_text += pdf_to_text(p, pages=8) + "\n\n"
-        ext_text = (aux_text or "") + "\n\n" + ext_text
-        if len(ext_text.strip()) > 0:
-            signals = extract_market_product_signals(ext_text[:18000], company_hint=company or "")
-    except Exception:
-        signals = None
-
-    # 6) 外部評価テンプレを生成 → LLMで可能な項目を自動補完
-    df_ext = get_external_template_df()
-    df_ext = fill_missing_with_external(df_ext)
-    if signals:
-        df_ext = apply_llm_signals_to_df(df_ext, signals)
-
-    # 7) AI所見（中立・根拠明示）
-    try:
-        client = get_client()
-        prompt = f"""あなたは独立の財務アナリストです。主観や推測を避け、事実と“比率”を根拠に簡潔な箇条書きを作成。
-- 断定・煽り禁止、将来予測禁止
-- 各行の末尾に根拠(指標名=値)を括弧で併記
-- 外部評価(市場/製品)は参考情報として最後に別見出しで一言添える
-
-[財務データ（円換算）]
-{json.dumps(fin_yen, ensure_ascii=False)}
-
-[社内スコア（財務比率基準）]
-{json.dumps(score, ensure_ascii=False)}
-
-[外部シグナル（市場/製品；参考）]
-{json.dumps(signals or {}, ensure_ascii=False)}
-"""
-        resp = client.chat.completions.create(
-            model=TEXT_MODEL,
-            messages=[{"role":"system","content":"中立・簡潔・根拠明示。"},
-                      {"role":"user","content": prompt}],
-            temperature=0.2,
-        )
-        insight = resp.choices[0].message.content
-    except Exception as e:
-        insight = f"AI所見の生成に失敗: {e}"
-
-    unit_info = f"PDF表記の単位: <b>{detected}</b> / 適用単位: <b>{unit_label}</b>（円換算係数={factor:g}）"
-    signals_md = "—"
-    if signals:
-        ev = signals.get("market", {}).get("evidence") or []
-        cagr = signals.get("market", {}).get("cagr_pct")
-        pc = signals.get("products", {}).get("count")
-        pg = signals.get("products", {}).get("growing_count")
-        signals_md = f"- 市場CAGR: {cagr if cagr is not None else '—'}%\n- 主力商品数: {pc or '—'} / 成長中: {pg or '—'}\n- エビデンス: {(' / '.join(ev[:2])) if ev else '—'}"
-
-    return (
-        unit_info,                 # HTML
-        kpi_html,                  # HTML
-        json.dumps(fin_raw, ensure_ascii=False, indent=2),   # 折りたたみ表示用
-        json.dumps(fin_yen, ensure_ascii=False, indent=2),   # 折りたたみ表示用
-        df,                        # 編集表（円換算）
-        json.dumps(score, ensure_ascii=False, indent=2),
-        fig,
-        insight,
-        df_ext,                    # 外部テンプレ（LLM補完済み）
-        signals_md                 # LLM抽出の要点
-    )
-
-def run_recalc(df: pd.DataFrame):
-    try:
-        fin = df_to_fin(df)           # ここは既に円換算された表が編集される想定
-        score = score_company(fin)
-        fig = radar(score)
-        return (json.dumps(score, ensure_ascii=False, indent=2),
-                fig,
-                json.dumps(fin, ensure_ascii=False, indent=2))
-    except Exception as e:
-        tb = traceback.format_exc(limit=6)
-        raise gr.Error(f"再計算に失敗しました: {e}\n\n<pre style='white-space:pre-wrap'>{tb}</pre>")
+def _score_bar(score: Dict[str,Any]) -> go.Figure:
+    det = score.get("details", []) or []
+    labels = [d.get("metric","") for d in det]
+    vals   = [float(d.get("score",0)) for d in det]
+    # ソート（高→低）
+    pairs = sorted(zip(vals, labels), reverse=True)
+    vals, labels = zip(*pairs) if pairs else ([],[])
+    fig = go.Figure(go.Bar(x=list(vals), y=list(labels), orientation="h", text=[f"{v:.1f}" for v in vals], textposition="auto"))
+    fig.update_layout(height=460, margin=dict(l=10,r=10,t=30,b=10), title="指標別スコア（社内ルール）", xaxis=dict(range=[0,100]))
+    return fig
 
-def calc_external(df_ext: pd.DataFrame):
-    res = score_external_from_df(df_ext)
-    return json.dumps(res, ensure_ascii=False, indent=2)
+def _external_bar(ext: Dict[str,Any]) -> go.Figure:
+    items = ext.get("items", []) or []
+    labels = [f"{i.get('category','')}: {i.get('name','')}" for i in items]
+    vals   = [float(i.get("score",0)) for i in items]
+    pairs  = sorted(zip(vals, labels))
+    vals, labels = zip(*pairs) if pairs else ([],[])
+    fig = go.Figure(go.Bar(x=list(vals), y=list(labels), orientation="h", text=[f"{v:.1f}" for v in vals], textposition="auto"))
+    fig.update_layout(height=520, margin=dict(l=10,r=10,t=30,b=10), title="外部評価（定量）項目スコア", xaxis=dict(range=[0,100]))
+    return fig
 
-# ===== UI =====
+def _strengths_md(score: Dict[str,Any]) -> str:
+    det = score.get("details", []) or []
+    if not det: return "強み・弱みの抽出に必要な明細が見つかりませんでした。"
+    det_sorted = sorted(det, key=lambda x: x.get("score",0), reverse=True)
+    top3 = det_sorted[:3]
+    bottom3 = det_sorted[-3:][::-1]
+    def fmt(items):
+        return "\n".join([f"- **{i.get('metric','')}**：{i.get('score',0):.1f} 点（値: {i.get('value','—')}{i.get('unit','')}）" for i in items])
+    return f"### 強み（上位3）\n{fmt(top3)}\n\n### 改善余地（下位3）\n{fmt(bottom3)}"
+
+# ============ アプリ本体 ============
 def build_ui() -> gr.Blocks:
-    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 × OpenAI Vision）")
+    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 × 定量評価 × AI所見）")
+
         with gr.Row():
             with gr.Column(scale=1):
                 company = gr.Textbox(label="企業名（任意）", placeholder="例：株式会社OO")
-                use_vision = gr.Checkbox(value=True, label="OpenAIでPDFをAI解析（Vision）")
-                unit_sel = gr.Dropdown(choices=["自動推定","円","千円","百万円","千万円","億円"],
-                                       value="自動推定", label="金額単位（PDF記載）")
-                use_pdf_for_ext = gr.Checkbox(value=True, label="PDFから市場/製品情報も抽出（外部評価用）")
-                aux_text = gr.Textbox(label="補助テキスト・Web抜粋（任意）", lines=4,
-                                      placeholder="業界説明/IRの抜粋などを貼ると、市場CAGRや主力商品数を自動推定します。")
-                files = gr.File(label="決算書PDF（複数可）", file_count="multiple", type="filepath")
+                files   = gr.File(label="決算書PDF（複数可）", file_count="multiple", type="filepath")
+                force_ocr = gr.Checkbox(False, label="OCRを強制（スキャンPDF向け / やや遅い）")
                 run_btn = gr.Button("📄 解析して反映", variant="primary")
-                recalc_btn = gr.Button("🔁 表の値で再計算")
-                health_btn = gr.Button("🩺 環境チェック")
-                health_out = gr.HTML()
-
+                recalc_btn = gr.Button("🔁 この表の値で再計算")
             with gr.Column(scale=1):
-                unit_html = gr.HTML(label="単位情報")
-                kpi_html = gr.HTML(label="主要KPI")
+                kpi_html = gr.HTML(label="ハイライトKPI")
 
         with gr.Tabs():
             with gr.Tab("概要"):
-                chart = gr.Plot(label="スコアレーダー（社内ルール）")
+                strengths_md = gr.Markdown()
+
             with gr.Tab("抽出結果（編集可）"):
-                with gr.Accordion("抽出JSON（換算前/円換算）", open=False):
-                    fin_json_raw = gr.Code(label="抽出JSON（換算前）", language="json", interactive=False)
-                    fin_json_yen = gr.Code(label="抽出JSON（円換算）", language="json")
-                df_out = gr.Dataframe(headers=["category", "item", "value"], interactive=True, wrap=True)
+                df_out = gr.Dataframe(headers=["category","item","value"], interactive=True, wrap=True)
+
             with gr.Tab("スコア（社内ルール）"):
-                score_json = gr.Code(label="スコア（JSON）", language="json")
+                score_bar = gr.Plot()
+                radar_plot = gr.Plot()
+                with gr.Accordion("🔎 原データ（JSON）", open=False):
+                    score_json_view = gr.JSON(value={})
+
             with gr.Tab("AI所見（中立）"):
-                insight_md = gr.Markdown()
+                ai_md = gr.Markdown()
+
             with gr.Tab("外部評価（定量化）"):
-                signals_md = gr.Markdown(label="抽出シグナル（市場/製品）")
-                df_ext = gr.Dataframe(headers=["カテゴリー","入力項目","値"], interactive=True, wrap=True)
-                calc_t = gr.Button("🧮 外部スコア計算")
-                ext_json = gr.Code(label="外部評価（JSON）", language="json")
+                ext_bar = gr.Plot()
+                with gr.Accordion("🔎 原データ（JSON）", open=False):
+                    ext_json_view = gr.JSON(value={})
+
+        # ---------- ハンドラ ----------
+        def _run(files: List[str], company: str, force_ocr: bool):
+            """
+            1) PDF→抽出 2) 社内スコア 3) 外部評価（定量） 4) AI所見 5) 可視化
+            """
+            if not files:
+                raise gr.Error("PDF をアップロードしてください。")
+
+            # --- 1) 抽出 ---
+            fin_dict, fin_df = parse_pdf(files, company=company or "", force_ocr=force_ocr)
+
+            # --- 2) 社内スコア ---
+            score = score_company(fin_dict)
+
+            # --- 3) 外部評価（定量） ---
+            ext = score_external(company=company or "", fin=fin_dict, df=fin_df)
+
+            # --- 4) AI所見（中立・簡潔） ---
+            ai = make_ai_memo(company=company or "", fin=fin_dict, score=score, ext=ext)
+
+            # --- 5) 可視化 ---
+            kpi = _kpi_cards_html(score, ext)
+            sbar = _score_bar(score)
+            ebar = _external_bar(ext)
+            strengths = _strengths_md(score)
+
+            return (
+                kpi,
+                strengths,
+                fin_df,
+                sbar,
+                # 既存のレーダー（社内ルール可視化）。score側で作っているなら差し替えてOK
+                go.Figure(
+                    data=[go.Scatterpolar(
+                        r=[d["score"] for d in score.get("details", [])] + ([score.get("details", [])[0]["score"]] if score.get("details") else []),
+                        theta=[d["metric"] for d in score.get("details", [])] + ([score.get("details", [])[0]["metric"]] if score.get("details") else []),
+                        fill="toself"
+                    )],
+                    layout=go.Layout(
+                        title="スコア・レーダー（社内）",
+                        polar=dict(radialaxis=dict(visible=True, range=[0,100])),
+                        showlegend=False, height=380, margin=dict(l=20,r=20,t=30,b=20)
+                    )
+                ),
+                score,  # gr.JSON 用
+                ai,
+                ebar,
+                ext   # gr.JSON 用
+            )
+
+        def _recalc(df: pd.DataFrame, last_company: str):
+            """
+            テーブル編集後の再計算。抽出は行わず、DF→fin辞書へ戻して再スコア。
+            """
+            # df -> fin 辞書（あなたの既存関数に合わせて調整）
+            fin = {"balance_sheet":{}, "income_statement":{}, "cash_flows":{}}
+            for _, r in df.iterrows():
+                cat = str(r.get("category",""))
+                item= str(r.get("item",""))
+                val = r.get("value", None)
+                try:
+                    parsed = None if val in (None,"","null") else float(val)
+                except Exception:
+                    parsed = None
+                if cat in fin: fin[cat][item] = parsed
+
+            score = score_company(fin)
+            ext = score_external(company=last_company or "", fin=fin, df=df)
+            kpi = _kpi_cards_html(score, ext)
+            sbar = _score_bar(score)
+            ebar = _external_bar(ext)
+            strengths = _strengths_md(score)
+            return (
+                kpi, strengths, sbar,
+                go.Figure(
+                    data=[go.Scatterpolar(
+                        r=[d["score"] for d in score.get("details", [])] + ([score.get("details", [])[0]["score"]] if score.get("details") else []),
+                        theta=[d["metric"] for d in score.get("details", [])] + ([score.get("details", [])[0]["metric"]] if score.get("details") else []),
+                        fill="toself"
+                    )],
+                    layout=go.Layout(
+                        title="スコア・レーダー（社内）",
+                        polar=dict(radialaxis=dict(visible=True, range=[0,100])),
+                        showlegend=False, height=380, margin=dict(l=20,r=20,t=30,b=20)
+                    )
+                ),
+                score, ebar, ext
+            )
 
         run_btn.click(
-            run_analyze,
-            inputs=[company, use_vision, unit_sel, use_pdf_for_ext, aux_text, files],
-            outputs=[unit_html, kpi_html, fin_json_raw, fin_json_yen, df_out, score_json, chart, insight_md, df_ext, signals_md],
+            _run,
+            inputs=[files, company, force_ocr],
+            outputs=[kpi_html, strengths_md, df_out, score_bar, radar_plot, score_json_view, ai_md, ext_bar, ext_json_view],
             concurrency_limit=1
         )
 
         recalc_btn.click(
-            run_recalc, inputs=[df_out], outputs=[score_json, chart, fin_json_yen],
+            _recalc,
+            inputs=[df_out, company],
+            outputs=[kpi_html, strengths_md, score_bar, radar_plot, score_json_view, ext_bar, ext_json_view],
             concurrency_limit=1
         )
 
-        calc_t.click(calc_external, inputs=[df_ext], outputs=[ext_json], concurrency_limit=1)
-
-        health_btn.click(health, outputs=health_out, concurrency_limit=1)
-
     return demo