Update app.py

app.py

@@ -17,8 +17,17 @@ clf = pipeline(model=model, task="image-classification", image_processor=image_p
 class_names = ['artificial', 'real']
 
 def predict_image(img, confidence_threshold):
-    # Convert the image to a PIL Image and resize it
-    img_pil = Image.fromarray(img).convert('RGB')  # Convert NumPy array to PIL Image
+    print(f"Type of img: {type(img)}")  # Debugging statement
+    if not isinstance(img, Image.Image):
+        raise ValueError(f"Expected a PIL Image, but got {type(img)}")
+    
+    # Convert the image to RGB if not already
+    if img.mode != 'RGB':
+        img_pil = img.convert('RGB')
+    else:
+        img_pil = img
+    
+    # Resize the image
     img_pil = transforms.Resize((256, 256))(img_pil)
     
     # Get the prediction
@@ -39,7 +48,7 @@ def predict_image(img, confidence_threshold):
         return f"Label: real, Confidence: {result['real']:.4f}"
     else:
         return "Uncertain Classification"
-
+        
 # Define the Gradio interface
 image = gr.Image(label="Image to Analyze", sources=['upload'], type='pil')  # Ensure the image type is PIL
 confidence_slider = gr.Slider(0.0, 1.0, value=0.5, step=0.01, label="Confidence Threshold")