ui/ui_app.py

# ui/ui_app.py
from __future__ import annotations
import json
from typing import Dict, Any, List, Tuple

import gradio as gr
import plotly.graph_objects as go
import pandas as pd

from core.extract import parse_pdf
from core.market_infer import infer_market_metrics
from core.external_scoring import (
    get_external_template_df,
    fill_missing_with_external,
    merge_market_into_external_df,
    score_external_from_df,
)
from core.ai_judgement import suggest_external_with_llm, ai_evaluate

# ---------- 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 = [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)
    return fig

def _diff_bar(ext: Dict[str, float], ai: Dict[str, float]) -> go.Figure:
    ks = sorted(set(ext.keys()) | set(ai.keys()))
    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))
    return fig

def _fmt(x):
    try:
        f = float(x)
        if abs(f) >= 1e8: return f"{f/1e8:.2f}億"
        if abs(f) >= 1e6: return f"{f/1e6:.2f}百万円"
        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 "—"

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 = "—"
    try:
        ta = float(ta); te = float(te)
        er = f"{(te/ta*100):.1f}%" if ta>0 else "—"
    except Exception:
        pass
    period = ""
    if meta and isinstance(meta.get("period"), dict):
        period = f"{meta['period'].get('start_date','')} ~ {meta['period'].get('end_date','')}"
    unit = (meta.get("unit") or "円").replace("JPY","円")
    return f"""
<div style="display:grid;grid-template-columns:repeat(4,1fr);gap:12px;">
  <div style="background:#F8FAFF;border:1px solid #E5E7EB;border-radius:12px;padding:12px;">
    <div style="font-size:12px;color:#6B7280;">企業名</div>
    <div style="font-size:18px;font-weight:700;">{company or fin.get('company',{}).get('name','—')}</div>
    <div style="font-size:12px;color:#6B7280;margin-top:4px;">期間: {period or '—'} / 単位: {unit}</div>
  </div>
  <div style="background:#F8FAFF;border:1px solid #E5E7EB;border-radius:12px;padding:12px;">
    <div style="font-size:12px;color:#6B7280;">売上高</div>
    <div style="font-size:18px;font-weight:700;">{_fmt(is_.get('sales'))}</div>
  </div>
  <div style="background:#F8FAFF;border:1px solid #E5E7EB;border-radius:12px;padding:12px;">
    <div style="font-size:12px;color:#6B7280;">営業利益</div>
    <div style="font-size:18px;font-weight:700;">{_fmt(is_.get('operating_income'))}</div>
  </div>
  <div style="background:#F8FAFF;border:1px solid #E5E7EB;border-radius:12px;padding:12px;">
    <div style="font-size:12px;color:#6B7280;">自己資本比率</div>
    <div style="font-size:18px;font-weight:700;">{er}</div>
  </div>
  <div style="background:#F0FDF4;border:1px solid #DCFCE7;border-radius:12px;padding:12px;">
    <div style="font-size:12px;color:#047857;">外部評価（定量）</div>
    <div style="font-size:22px;font-weight:800;color:#065F46;">{ext_total:.1f}</div>
  </div>
  <div style="background:#EFF6FF;border:1px solid #DBEAFE;border-radius:12px;padding:12px;">
    <div style="font-size:12px;color:#1D4ED8;">AI評点</div>
    <div style="font-size:22px;font-weight:800;color:#1E40AF;">{ai_total:.1f}</div>
  </div>
</div>
"""

# ---------- market df helpers ----------
def _market_df_from_dict(d: Dict[str, Any]) -> pd.DataFrame:
    rows = []
    order = [
        "市場の年成長率(%)","市場成熟度(0-1)","競争強度(0-10)","参入障壁(0-10)","価格決定力(0-10)",
        "サイクル感応度(0-10)","規制リスク(0-10)","技術破壊リスク(0-10)","TAM_億円","SAM_億円","SOM_億円"
    ]
    for k in order:
        rows.append([k, d.get(k,"")])
    return pd.DataFrame(rows, columns=["指標","値"])

def _dict_from_market_df(df: pd.DataFrame) -> Dict[str, Any]:
    out = {}
    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]):
    """
    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):
    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):
    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:
        gr.Markdown("## 🧮 企業スコアリング：PDF抽出 × 市場推定（LLM）× 外部定量 × AI評点")

        with gr.Row():
            with gr.Column(scale=1):
                company = gr.Textbox(label="企業名（任意）")
                use_vision = gr.Checkbox(value=True, label="OpenAI VisionでPDF表を補完")
                files = gr.File(label="決算書PDF（複数可）", file_count="multiple", type="filepath")
                run_btn = gr.Button("📄 PDFを解析", variant="primary")
            with gr.Column(scale=2):
                cards = gr.HTML(label="サマリー")

        with gr.Tab("入力/市場推定"):
            with gr.Row():
                industry = gr.Textbox(label="事業領域（業界・カテゴリ）", placeholder="例）ヘルスケアIT / 産業ロボット 等")
                products = gr.Textbox(label="主力商品（1行1件）", lines=4, placeholder="製品A\n製品B\n…")
            with gr.Row():
                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"):
            df_fin  = gr.Dataframe(label="抽出テーブル（編集可）", interactive=True, wrap=True)
            ext_df  = gr.Dataframe(label="外部入力（編集可）", interactive=True, wrap=True)

        with gr.Tab("スコア"):
            with gr.Row():
                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）")
            diff_plot = gr.Plot(label="差分（棒）")
            rescore_btn = gr.Button("🔁 すべて再計算", variant="secondary")

        with gr.Tab("詳細"):
            fin_json = gr.Code(label="抽出JSON", language="json")
            ext_json = gr.Code(label="外部評価JSON", language="json")
            ai_json  = gr.Code(label="AI評点JSON", language="json")
            debug    = gr.Textbox(label="ログ", lines=8)

        # state
        fin_state = gr.State("")

        # wire
        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],
        ).then(lambda x: x, inputs=[fin_json], outputs=[fin_state])

        infer_btn.click(
            on_market_infer,
            inputs=[industry, products, country, horizon, ext_df, fin_state],
            outputs=[market_df, ext_df, ext_total, ai_total, ext_plot, ai_plot, diff_plot, ext_json, ai_json, debug],
        )

        rescore_btn.click(
            on_rescore_all,
            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