Update app.py

app.py

@@ -14,13 +14,12 @@ model_1 = model_1.to(device)
 clf_1 = pipeline(model=model_1, task="image-classification", image_processor=image_processor_1, device=device)
 
 # Load the second model
-hfUser = "Heem2"
-modelName = "AI-vs-Real-Image-Detection"
-clf_2 = pipeline("image-classification", model=f"{hfUser}/{modelName}")
+model_2_path = "Heem2/AI-vs-Real-Image-Detection"
+clf_2 = pipeline("image-classification", model=model_2_path)
 
 # Define class names for both models
 class_names_1 = ['artificial', 'real']
-class_names_2 = ['artificial', 'real']  # Adjust if the second model has different classes
+class_names_2 = ['AI Image', 'Real Image']  # Adjust if the second model has different classes
 
 def predict_image(img, confidence_threshold):
     # Ensure the image is a PIL Image