Update app.py

app.py

@@ -5,12 +5,21 @@ from fastapi.responses import JSONResponse, HTMLResponse
 from transformers import AutoImageProcessor, AutoModelForImageClassification
 from PIL import Image
 
-# Set Hugging Face cache to avoid permission issues
+# ✅ Force cache to /tmp/hf_cache before anything else
+os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf_cache"
 os.environ["HF_HOME"] = "/tmp/hf_cache"
+os.environ["HF_DATASETS_CACHE"] = "/tmp/hf_cache"
+
+# Create cache directory if missing
+os.makedirs("/tmp/hf_cache", exist_ok=True)
 
 # Load processor + model
-processor = AutoImageProcessor.from_pretrained("prithivMLmods/Realistic-Gender-Classification")
-model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Realistic-Gender-Classification")
+processor = AutoImageProcessor.from_pretrained(
+    "prithivMLmods/Realistic-Gender-Classification", cache_dir="/tmp/hf_cache"
+)
+model = AutoModelForImageClassification.from_pretrained(
+    "prithivMLmods/Realistic-Gender-Classification", cache_dir="/tmp/hf_cache"
+)
 
 # Create FastAPI app
 app = FastAPI()
@@ -43,10 +52,10 @@ async def predict(file: UploadFile = File(...)):
             outputs = model(**inputs)
             probs = torch.nn.functional.softmax(outputs.logits, dim=-1)[0].cpu().numpy()
 
-        # Get labels (ensure consistent order)
+        # Get labels
         labels = list(model.config.id2label.values())
 
-        # Fix keys: return "male" and "female" only
+        # Clean result for FlutterFlow
         result = {
             "female": float(probs[labels.index("female portrait")]),
             "male": float(probs[labels.index("male portrait")])