from fastapi import FastAPI
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import os
import logging

# 初始化日志
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("CodeSecurityAPI")

# 强制设置缓存路径
os.environ["HF_HOME"] = "/app/.cache/huggingface"

app = FastAPI()

# === 新增根路径响应 ===
@app.get("/")
async def read_root():
    return {
        "message": "欢迎使用代码安全检测API",
        "endpoints": {
            "detect": "POST /detect",
            "health": "GET /health"
        }
    }

# === 加载模型（必须放在FastAPI实例之后） ===
try:
    logger.info("Loading model...")
    model = AutoModelForSequenceClassification.from_pretrained(
        "mrm8488/codebert-base-finetuned-detect-insecure-code",
        cache_dir=os.getenv("HF_HOME")
    )
    tokenizer = AutoTokenizer.from_pretrained(
        "mrm8488/codebert-base-finetuned-detect-insecure-code",
        cache_dir=os.getenv("HF_HOME")
    )
    logger.info("Model loaded successfully")
except Exception as e:
    logger.error(f"Model load failed: {str(e)}")
    raise RuntimeError("模型加载失败，请检查网络连接或模型路径")

@app.post("/detect")
async def detect(code: str):
    try:
        code = code[:2000]
        inputs = tokenizer(code, return_tensors="pt", truncation=True, max_length=512)
        with torch.no_grad():
            outputs = model(**inputs)
        label_id = outputs.logits.argmax().item()
        return {
            "label": model.config.id2label[label_id],
            "score": outputs.logits.softmax(dim=-1)[0][label_id].item()
        }
    except Exception as e:
        return {"error": str(e)}

@app.get("/health")
async def health():
    return {"status": "ok"}