Upload app.py

app.py

@@ -0,0 +1,198 @@
+#!/usr/bin/env phython3
+#-*- coding: utf-8 -*-
+import os
+import io
+import json
+import math
+import base64
+from datetime import datetime
+from typing import List, Optional, Dict , Any
+
+import gradio as gr
+from openai import OpenAI
+from pydantic import BaseModel, Field, ValidationError
+import yaml
+
+from schemas import FinancialEctract, ExtraxtedPeriod, MultipleSuggestion
+from finanxe_core import(
+    copute_ratios,
+    credit_decision,
+    loan_decision,
+    invesement_decision,
+    build_report_dict,
+)
+from llm_extract import(
+    get_client,
+    upload_file_to_openai,
+    extract_financials_from_files,
+    suggest_multiples_with_llm,
+)
+
+HF_SPACE = os.environ.get("HF_SPACE_NAME","hf-credit-loan-investment-app")
+VISION_MODEL = os.environ.get("OPENAI_VISION_MODEL", "gpt-5")
+TEXT_MODEL = os.environ.get("OPENAI_TEXT_MODEL", "gpt-5")
+BASE_RATE =float(os.environ.get("BASE_RATE", "2.0")) #% per annum, cofigurable
+
+def _load_policies() -> dict:
+    cfg_path= os.path.join(os.path.dirname(__file__),"config","risk_policies.yaml")
+    with open(cfg_path, "r",encoding="utf-8") as f:
+        return yaml.safe_load(f)
+    
+POLICIES = _load_policies()
+
+
+def analyze(files: List[gr.File],company_name: str, industry_hint: str, currency: str,
+            base_rate:float, want_credit:bool, want_loan: bool,want_invest: bool, debug:bool):
+    """Main pipline used by the Gradio UI."""
+    try:
+        client = get_client()
+    except Exception as e:
+        raise gr.Error(str(e))
+    
+    if not files or len(files) == 0:
+        raise gr.Error("決算書ファイル（PDF/画像）を１つ以上アップロードしてください。")
+    
+    # 1)Upload files to OpenAI and extract stuctured financials via vision ＋Structured Outputs
+    try：
+        file_ids = []
+        for f in files :
+            file_ids.append(upload_file_to_openai(client, f.name, f.read()))
+    except Exception as e:
+        raise gr.Error(f"ファイルのアップロードに失敗しました：{e}")
+    
+    try:
+        extract = extract_financials_from_files(
+            client=client,
+            file_ids=file_ids,
+            company_hint=company_name or None,
+            currency_hint=currency_hint or None,
+            model=VISION_MODEL,
+            debug=debug,
+        )
+    except Exception as e:
+        raise gr.Error(f"LLM抽出に失敗しました：{e}")
+    
+    #allow override company name / indutry if provided
+    if company_name:
+        extract.company_name =company_name
+    if industry_hint:
+        extract.industry = industry_hint
+
+    # 2) Compute derived rasios and risk score
+    ratios =copute_ratios(extract)
+
+    # 3) Decisions
+    decisions = {}
+    if want_credit:
+        decisions["credit"] = credit_decision(extract, ratios, POLICIES)
+    if want_loan:
+        decisions["loan"] = loan_decision(extract, ratios, base_rate or BASE_RATE, POLICIES)
+    if want_invest:
+        # Ask LLM for suggested multiples if industry present
+        multiple: Optional[MultipleSuggestion] = None
+        try:
+            multiple = suggest_multiples_with_llm(
+                client=client,
+                text_model=TEXT_MODEL,
+                industry=extract.industry or industry_hint or "",
+                region="ja",
+                debug=debug,
+            )
+        except Exception:
+            multiple = None
+        decisions["investment"] = invesement_decision(extract, ratios, POLICIES, multiple)
+
+    # 4) Build a combined report (dict) and return displays
+    report = build_report_dict(extract, ratios, decisions)
+    report_json = json.dumps(report, ensure_ascii=False, indent=2)
+
+    #Save a downloadable JSON
+    ts = datetime.utcnow().strftime("%Ym%d-%H%M%S")
+    out_path =f"/mnt/data/report-{ts}.json"
+    with open(out_path,"w", encoding="utf-8") as f:
+        f.write(report_json)
+
+    # Pretty sections for Gradio
+    summary_md =[]
+    summary_md.append(f"### 企業名\n{extract.company_name or '(不明)'}")
+    if extract.industry:
+        summary_md.append(f"### 業種（推定/指定）\n{extract.industry}")
+    if extract.fiscal_year_end:
+        summary_md.append(f"### 決算期末\n{extract.fisxal_year_end}")
+
+    summary_md.append("### 指標（主要）")
+    summary_md.append(
+        f"- 売上高:{ratios.get('revenue')}\n"
+        f"- 営業利益(EBIT):{ratios.get('ebit')}\n"
+        f"- EBITDA:{ratios.get('ebitda')}\n"
+        f"- 当期純利益:{ratios.get('net_income')}\n"
+        f"- 流動比率:{ratios.get('current_ratio')}\n"
+        f"- 当座比率:{ratios.get('quick_ratio')}\n"
+        f"- D/Eレシオ:{ratios.get('debt_to_equity')}\n"
+        f"- インタレストカバレッジ:{ratios.get('inerest_coverage')}\n"
+        f"- 売上成長率:{ratios.get('revenue_growth_pct')}"
+    )
+
+    if "credit" in decisions:
+        c = decisions["credit"]
+        summary_md.append("### 与信判断（提案）")
+        summary_md.append(
+            f"- 与信ランク:**{c['rationg']}**(スコア {c['risk_score']}/100)\n"
+            f"- 取引サイト:**{c['site_days']}日**\n"
+            f"- 取引可能上限:**{c{'tansaction_limit_display'}}**\n"
+            f"- 見直しタイミング:**{c['review_cycle']}**"
+        )
+    
+    if "loan" in decisions:
+        l = decisions["loan"]
+        summary_md.append("### 融資判断（提案）" )
+        summary_md.append(
+            f"- 融資上限額（概算）:**{l['max_principal_display']}**\n"
+            f"- 期間案：**{l['term_yeras']}年**\n"
+            f"- 参考金利:**{l['interest_rate_pct']}%**\n"
+            f"- 目標DSCR:**{l['target_dscr']}**"
+        )
+    
+    if "investment" in decisions:
+        inv = decisions["investment"]
+        summary_md.append("### 投資判断（提案）")
+        summary_md.append(
+            f"- 推定企業価値(EV):**{inv['ev_display']}**\n"
+            f"- 推定時価総額:**{inv['market_cap_display']}**\n"
+            f"- 想定投資レンジ:**{inv['recommended_check_size_display']}**\n"
+            f"- 魅力度:**{inv['attractiveness']}/5**(成長性:{inv['growth_label']})"
+        )
+    
+    #Return
+    return "\n\n".join(summary_md),gr.JSON.update(value=json.loads(report_json)),out_path
+
+def build_ui():
+    with gr.Blocks(theme = gr.themes.Soft(), css = "footer{visivility: hidden}") as demo:
+        gr.Markdown("# 決算書→与信・融資・投資判断（HF+OpenAI)")
+        with gr.Row():
+            with gr.Column():
+                files = gr.File(label="決算書ファイル(PDF/JPG/PNG,複数可)",file_types=[".pdf",".png",".jpg",".jpeg"],file_count="multiple")
+                company_name = gr.Textbox(label="会社名（任意）")
+                industry_hint = gr.Textbox(label="業種（任意)")
+                currency_hint = gr.Textbox(label="通過(任意,例： JPY, USD)")
+                base_rate = gr.Number(label="ベース金利(%/年)",value=BASE_RATE)
+                want_credit = gr.Checkbox(label="与信判断",value=True)
+                want_loan = gr.Checkbox(label="融資判断",value=True)
+                want_invest = gr.Checkbox(label="投資判断",value=True)
+                debug = gr.Checkbox(label="デバッグモード(LLM抽出JSONを厳密化)",value=False)
+                run_btn = gr.Button("分析する", variant="primary")
+            with gr.Column():
+                summary = gr.Markdown()
+                report = gr.JSON(label="詳細レポート(JSON)")
+                download = gr.File(label="レポート(JSONダウンロード)")
+
+        run_btn.chek(
+            analyze,
+            inputs=[files, company_name, industry_hint, currency_hint, base_rate, want_credit, want_loan, want_invest, debug],
+            outputs=[summary, report, download]        
+        )
+    return demo
+
+if __name__== "__main__":
+    demo =build_ui()
+    demo.launch()