Upload app.py with huggingface_hub

app.py

@@ -0,0 +1,77 @@
+"""
+재난문자 분류 API — Hugging Face Spaces 배포용
+
+HUB_MODEL_ID를 push_to_hub.py 실행 후 업로드한 모델 ID로 변경하세요.
+"""
+
+import re
+import torch
+import torch.nn.functional as F
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# ── 수정 필요 ──────────────────────────────────
+HUB_MODEL_ID = "nhs0327/koelectra-disaster-v3"
+# ──────────────────────────────────────────────
+
+app = FastAPI(title="재난문자 분류 API")
+
+MAX_LENGTH  = 96
+LABEL_NAMES = ['긴급', '주의', '일반']
+UNCERTAIN_THRESH = {'긴급': 0.60, '주의': 0.70, '일반': 0.70}
+
+_ORG_PATTERN  = re.compile(r'\[[^\]]{1,20}\]')
+_CERT_EMERG   = [
+    '즉시 대피', '대피명령', '대피 명령', '긴급대피', '긴급 대피', '신속히 대피',
+    '지진 발생', '쓰나미', '민방공 경보', '민방공경보', '테러 발생',
+]
+_CERT_CAUTION = [
+    '호우경보', '호우주의보', '태풍경보', '태풍주의보',
+    '한파경보', '한파주의보', '폭염경보', '폭염주의보',
+    '대설경보', '대설주의보', '강풍경보', '강풍주의보',
+    '풍랑경보', '풍랑주의보',
+]
+_CERT_GENERAL = ['찾습니다', '실종된']
+
+device    = torch.device("cpu")
+tokenizer = AutoTokenizer.from_pretrained(HUB_MODEL_ID)
+model     = AutoModelForSequenceClassification.from_pretrained(HUB_MODEL_ID)
+model.eval()
+
+
+class ClassifyRequest(BaseModel):
+    message: str
+
+
+@app.post("/classify")
+async def classify(req: ClassifyRequest):
+    text = _ORG_PATTERN.sub('[기관]', req.message)
+
+    if any(kw in text for kw in _CERT_EMERG):
+        return {"label": "긴급", "confidence": 1.0, "stage": "rule", "uncertain": False}
+    if any(kw in text for kw in _CERT_CAUTION):
+        return {"label": "주의", "confidence": 1.0, "stage": "rule", "uncertain": False}
+    if any(kw in text for kw in _CERT_GENERAL) and 'cm' in text:
+        return {"label": "일반", "confidence": 1.0, "stage": "rule", "uncertain": False}
+
+    inputs = tokenizer(text, truncation=True, padding='max_length',
+                       max_length=MAX_LENGTH, return_tensors='pt')
+    with torch.no_grad():
+        probs = F.softmax(model(**inputs).logits, dim=-1)[0]
+    pred_idx   = probs.argmax().item()
+    label      = LABEL_NAMES[pred_idx]
+    confidence = probs[pred_idx].item()
+
+    return {
+        "label":      label,
+        "confidence": round(confidence, 4),
+        "stage":      "model",
+        "uncertain":  confidence < UNCERTAIN_THRESH[label],
+        "probs":      {LABEL_NAMES[i]: round(probs[i].item(), 4) for i in range(3)},
+    }
+
+
+@app.get("/health")
+async def health():
+    return {"status": "ok"}