Update app.py

app.py

@@ -1,121 +1,17 @@
-from __future__ import annotations
-import os, io, base64, json, tempfile
-from typing import List, Dict, Any
-from fastapi import FastAPI, UploadFile, File, Form
-from fastapi.middleware.cors import CORSMiddleware
-from pdf2image import convert_from_path
-import pdfplumber
-from openai import OpenAI
-from scorer import score_company
+from fastapi import FastAPI
+import gradio as gr
 
-OPENAI_MODEL_VISION = os.getenv("OPENAI_VISION_MODEL", "gpt-4o-mini")
-OPENAI_MODEL_TEXT   = os.getenv("OPENAI_TEXT_MODEL",   "gpt-4o-mini")
+# API(FastAPI) 本体
+from api.app import api  # /api/app.py 内で FastAPI() を作成
 
-def _b64(b: bytes) -> str: return base64.b64encode(b).decode("utf-8")
+# UI(Gradio Blocks)
+from ui.ui_app import build_ui  # /ui/ui_app.py が Blocks を返す
 
-def _client() -> OpenAI:
-    key = os.getenv("OPENAI_API_KEY")
-    if not key: raise RuntimeError("OPENAI_API_KEY not set")
-    return OpenAI(api_key=key, timeout=60)
+demo = build_ui()
 
-SYSTEM_JSON = """あなたは有能な財務アナリストです。
-与えられた決算書（画像またはテキスト）から、次の厳密な JSON 構造のみを日本語の単位なし・半角数値で返してください。分からない項目は null。
-{
-  "company": {"name": null},
-  "period": {"start_date": null, "end_date": null},
-  "balance_sheet": {
-    "total_assets": null, "total_liabilities": null, "total_equity": null,
-    "current_assets": null, "fixed_assets": null,
-    "current_liabilities": null, "long_term_liabilities": null
-  },
-  "income_statement": {
-    "sales": null, "cost_of_sales": null, "gross_profit": null,
-    "operating_expenses": null, "operating_income": null,
-    "ordinary_income": null, "net_income": null
-  },
-  "cash_flows": {
-    "operating_cash_flow": null, "investing_cash_flow": null, "financing_cash_flow": null
-  }
-}
-"""
+# ルート("/")にGradio、APIは /api/ 以下に残す
+app = gr.mount_gradio_app(api, demo, path="/")
 
-def pdf_to_images(path: str, dpi=220, max_pages=6) -> List[bytes]:
-    pages = convert_from_path(path, dpi=dpi, fmt="png")
-    out = []
-    for i, p in enumerate(pages):
-        if i >= max_pages: break
-        buf = io.BytesIO(); p.save(buf, format="PNG"); out.append(buf.getvalue())
-    return out
-
-def pdf_to_text(path: str, max_chars=15000) -> str:
-    chunks = []
-    with pdfplumber.open(path) as pdf:
-        for i, page in enumerate(pdf.pages):
-            t = (page.extract_text() or "").strip()
-            if t: chunks.append(f"[page {i+1}]\n{t}")
-            if sum(len(c) for c in chunks) > max_chars: break
-    return "\n\n".join(chunks)[:max_chars]
-
-def extract(images: List[bytes] | None, text_blob: str | None, company_hint: str) -> Dict[str, Any]:
-    client = _client()
-    if images:
-        content = [{"type":"text","text":SYSTEM_JSON}]
-        if company_hint: content.append({"type":"text","text":f"会社名の候補: {company_hint}"})
-        for im in images:
-            content.append({"type":"input_image","image_url":f"data:image/png;base64,{_b64(im)}"})
-        res = client.chat.completions.create(
-            model=OPENAI_MODEL_VISION,
-            messages=[{"role":"system","content":"返答は有効なJSONのみ"},
-                      {"role":"user","content":content}],
-            response_format={"type":"json_object"}, temperature=0.1,
-        )
-        return json.loads(res.choices[0].message.content)
-    else:
-        prompt = f"{SYSTEM_JSON}\n\n{text_blob or ''}"
-        res = client.chat.completions.create(
-            model=OPENAI_MODEL_TEXT,
-            messages=[{"role":"system","content":"返答は有効なJSONのみ"},
-                      {"role":"user","content":prompt}],
-            response_format={"type":"json_object"}, temperature=0.1,
-        )
-        return json.loads(res.choices[0].message.content)
-
-app = FastAPI()
-app.add_middleware(
-    CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"]
-)
-
-@app.get("/health")
-def health():
-    return {"ok": True, "vision_model": OPENAI_MODEL_VISION, "text_model": OPENAI_MODEL_TEXT}
-
-@app.post("/analyze")
-async def analyze(
-    company: str = Form(""),
-    use_vision: bool = Form(True),
-    files: List[UploadFile] = File(...),
-):
-    # 受け取った PDF を一時保存
-    tmps = []
-    for f in files:
-        suf = ".pdf" if not f.filename.lower().endswith(".pdf") else ""
-        fd, path = tempfile.mkstemp(suffix=suf); os.close(fd)
-        with open(path, "wb") as w: w.write(await f.read())
-        tmps.append(path)
-
-    try:
-        if use_vision:
-            imgs = []
-            for p in tmps: imgs += pdf_to_images(p)
-            fin = extract(imgs, None, company)
-        else:
-            text = ""
-            for p in tmps: text += pdf_to_text(p) + "\n\n"
-            fin = extract(None, text, company)
-
-        score = score_company(fin)
-        return {"financials": fin, "score": score}
-    finally:
-        for p in tmps:
-            try: os.remove(p)
-            except: pass
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)