Fix import-time crash: lazy safety classifier, no hard raise on missing API key

.gitignore

@@ -0,0 +1,2 @@
+__pycache__/
+*.pyc

app.py

@@ -16,19 +16,32 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
 # 环境变量加载
 load_dotenv()
 API_KEY = os.getenv("WAVESPEED_API_KEY")
-if not API_KEY:
-    raise ValueError("WAVESPEED_API_KEY 未在环境变量中设置")
+# Missing credentials are reported when a request is made, not at import.
+# Raising here took the whole Space down with nothing but a traceback.
 
 # 安全分类配置
 MODEL_URL = "TostAI/nsfw-text-detection-large"
 CLASS_NAMES = {0: "✅ SAFE", 1: "⚠️ QUESTIONABLE", 2: "🚫 UNSAFE"}
 
 # 加载安全模型
-try:
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_URL)
-    model = AutoModelForSequenceClassification.from_pretrained(MODEL_URL)
-except Exception as e:
-    raise RuntimeError(f"安全模型加载失败: {str(e)}")
+# The safety classifier is loaded on first use rather than at import. Loading
+# it at import meant a multi-hundred-MB download had to finish before the app
+# could bind its port, so the Space was killed as unhealthy before it ever
+# served a request. If the model cannot be loaded we raise, so a classifier
+# failure never silently leaves prompts unscreened.
+tokenizer = None
+model = None
+_classifier_lock = threading.Lock()
+
+
+def _ensure_classifier():
+    global tokenizer, model
+    if tokenizer is not None and model is not None:
+        return
+    with _classifier_lock:
+        if tokenizer is None or model is None:
+            tokenizer = AutoTokenizer.from_pretrained(MODEL_URL)
+            model = AutoModelForSequenceClassification.from_pretrained(MODEL_URL)
 
 
 # 会话管理
@@ -101,6 +114,7 @@ def create_error_image(message):
 
 @torch.no_grad()
 def classify_prompt(prompt):
+    _ensure_classifier()
     """安全分类"""
     inputs = tokenizer(prompt,
                        return_tensors="pt",

requirements.txt

@@ -1,7 +1,7 @@
 gradio
 aiohttp
-plotly
 python-dotenv
 pydantic==2.8.2
 torch
-transformers==4.37.2
+transformers==4.37.2
+requests