backend/app/hf_utils.py

import httpx
from .config import settings

HF_API_URL = "https://api-inference.huggingface.co/models"

async def hf_text_classification(model: str, text: str):
    headers = {"Authorization": f"Bearer {settings.hf_api_key}"}
    async with httpx.AsyncClient(timeout=60) as client:
        r = await client.post(f"{HF_API_URL}/{model}", headers=headers, json={"inputs": text})
        r.raise_for_status()
        return r.json()

async def toxicity_score(text: str) -> float:
    try:
        results = await hf_text_classification(settings.hf_toxic_model, text)
        # モデルによりスキーマ差異あり。unitary/toxic-bertは label "toxic" のscore
        if isinstance(results, list) and results and isinstance(results[0], list):
            # pipeline="text-classification" (top_k>1) 形式
            labels = {d["label"].lower(): d["score"] for d in results[0]}
            return float(labels.get("toxic", 0.0))
        elif isinstance(results, list) and results and "label" in results[0]:
            return float(results[0]["score"])
    except Exception:
        pass
    return 0.0

async def sentiment_polarity(text: str) -> float:
    try:
        results = await hf_text_classification(settings.hf_sentiment_model, text)
        # ラベル: NEGATIVE/NEUTRAL/POSITIVE を [+1,0,+1] に近似
        if isinstance(results, list) and results and isinstance(results[0], list):
            labels = {d["label"].upper(): d["score"] for d in results[0]}
            return float(labels.get("POSITIVE", 0.0) - labels.get("NEGATIVE", 0.0))
        elif isinstance(results, list) and results and "label" in results[0]:
            label = results[0]["label"].upper()
            score = results[0]["score"]
            return score if label == "POSITIVE" else (-score if label == "NEGATIVE" else 0.0)
    except Exception:
        pass
    return 0.0