Update app.py

app.py

@@ -1,13 +1,31 @@
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
 import os
+import logging
+from pathlib import Path
+
+# === 初始化日志 ===
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# === 检查缓存目录权限 ===
+def check_permissions():
+    cache_path = Path(os.getenv("HF_HOME", ""))
+    try:
+        cache_path.mkdir(parents=True, exist_ok=True)
+        test_file = cache_path / "permission_test.txt"
+        test_file.write_text("test")
+        test_file.unlink()
+        logger.info(f"✅ 缓存目录权限正常: {cache_path}")
+    except Exception as e:
+        logger.error(f"❌ 缓存目录权限异常: {str(e)}")
+        raise RuntimeError(f"Directory permission error: {str(e)}")
+
+check_permissions()
 
 # === FastAPI 配置 ===
 app = FastAPI()
-
-# 解决 CSP 限制的关键配置
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -16,21 +34,22 @@ app.add_middleware(
 )
 
 # === 模型加载 ===
-os.environ["HF_HOME"] = "/app/.cache/huggingface"
-model = AutoModelForSequenceClassification.from_pretrained("mrm8488/codebert-base-finetuned-detect-insecure-code")
-tokenizer = AutoTokenizer.from_pretrained("mrm8488/codebert-base-finetuned-detect-insecure-code")
+try:
+    logger.info("🔄 加载模型中...")
+    model = AutoModelForSequenceClassification.from_pretrained("mrm8488/codebert-base-finetuned-detect-insecure-code")
+    tokenizer = AutoTokenizer.from_pretrained("mrm8488/codebert-base-finetuned-detect-insecure-code")
+    logger.info("✅ 模型加载成功")
+except Exception as e:
+    logger.error(f"❌ 模型加载失败: {str(e)}")
+    raise
 
-# === HTTP API 接口 ===
+# === API 接口 ===
 @app.post("/detect")
 async def detect(code: str):
-    try:
-        inputs = tokenizer(code[:2000], return_tensors="pt", truncation=True, max_length=512)
-        with torch.no_grad():
-            outputs = model(**inputs)
-        label_id = outputs.logits.argmax().item()
-        return {
-            "label": int(label_id),  # 严格返回 0/1
-            "score": outputs.logits.softmax(dim=-1)[0][label_id].item()
-        }
-    except Exception as e:
-        return {"error": str(e)}
+    inputs = tokenizer(code[:2000], return_tensors="pt", truncation=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    return {
+        "label": int(outputs.logits.argmax()),
+        "score": outputs.logits.softmax(dim=-1).max().item()
+    }