Update app.py

app.py

@@ -40,20 +40,30 @@ processor = None
 model = None
 
 
-def load_model() -> None:
-    global processor, model
-    if model is not None and processor is not None:
+processor = None
+model = None
+
+def load_processor() -> None:
+    global processor
+    if processor is not None:
         return
 
-    print("Loading processor...", processor)
+    print("Loading processor...")
     processor = AutoProcessor.from_pretrained(
         MODEL_ID,
         token=HF_TOKEN if HF_TOKEN else None,
         min_pixels=MIN_PIXELS,
         max_pixels=MAX_PIXELS,
     )
+    print("Processor ready")
+
 
-    print("Loading model...", model)
+def load_model() -> None:
+    global model
+    if model is not None:
+        return
+
+    print("Loading model...")
     model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
         MODEL_ID,
         token=HF_TOKEN if HF_TOKEN else None,
@@ -61,12 +71,10 @@ def load_model() -> None:
         torch_dtype="auto",
         low_cpu_mem_usage=True,
     )
-
-    print("model .eval()...")
     model.eval()
     print("Model ready")
 
-load_model();
+load_processor()
 
 def prepare_image(image: Image.Image) -> Image.Image:
     """Upscale and sharpen to help with tiny text on pantry labels."""
@@ -114,7 +122,7 @@ def extract_json(text: str) -> Dict[str, Any]:
 PROMPT = """Analyze this pantry image in detail, list every ingredient"""
 
 
-@spaces.GPU(size="large", duration=240)
+@spaces.GPU(size="large", duration=120)
 def analyze_pantry(image: Image.Image):
     if image is None:
         return {"error": "Upload an image first."}