Minor updates

app.py

@@ -1,44 +1,18 @@
 import pathlib
 import platform
 if platform.system()=='Linux': pathlib.WindowsPath=pathlib.PosixPath
+
 import dill
 import gradio as gr
 
 with open('model_dill.pkl','rb') as f: learn=dill.load(f)
 
-# Try to find classes in different places
-print("=== SEARCHING FOR CLASSES ===")
-try:
-    labels=learn.dls.train.dataset.items
-    print(f"Found in train.dataset.items: {labels}")
-except Exception as e:
-    print(f"train.dataset.items failed: {e}")
-    try:
-        labels=learn.dls.dataset.items
-        print(f"Found in dataset.items: {labels}")
-    except Exception as e:
-        print(f"dataset.items failed: {e}")
-        # Try prediction to see what class names it returns
-        from fastai.vision.all import PILImage
-        try:
-            # Create dummy image to test prediction
-            dummy_img=PILImage.create('cat.jpg')  # if file exists
-            pred,pred_idx,probs=learn.predict(dummy_img)
-            print(f"Prediction result - pred: {pred}, type: {type(pred)}")
-            print(f"pred_idx: {pred_idx}, probs: {probs}")
-            # Extract class name from prediction
-            labels=[str(pred),'Other']  # temporary
-        except Exception as e2:
-            print(f"Test prediction failed: {e2}")
-            labels=['Dog','Cat']
-            
-print(f"Final labels: {labels}")
+labels=['Dog','Cat']
 
 def predict(img):
     from fastai.vision.all import PILImage
     img=PILImage.create(img)
     pred,pred_idx,probs=learn.predict(img)
-    print(f"Prediction: {pred}, Probabilities: {probs}")
     return {labels[i]:float(probs[i]) for i in range(len(labels))}
 
 gr.Interface(