remove half implemented batch processing feature

ui/app.py

@@ -222,25 +222,6 @@ def create_interface():
                     outputs=flag_output
                 )
 
-
-            with gr.Tab("Batch Processing"):
-                gr.Markdown("### Upload multiple images for batch processing")
-
-                batch_input = gr.File(
-                    label="Upload Multiple Images",
-                    file_count="multiple",
-                    type="filepath"
-                )
-
-                batch_predict_btn = gr.Button("Predict All", variant="primary")
-
-                batch_output = gr.Markdown(label="Batch Results")
-
-                batch_predict_btn.click(
-                    # fn=app.predict_batch,
-                    inputs=[batch_input, model_selector, confidence_slider],
-                    outputs=batch_output
-                )
             with gr.Tab("About"):
                 gr.Markdown(
                     """

ui/utils.py

@@ -97,14 +97,6 @@ def get_disease_info(class_name):
     }
 
 
-def batch_preprocess_images(images):
-    """
-    Preprocess a list of images into a batch tensor
-    """
-    tensors = [preprocess_image(img) for img in images]
-    return torch.cat(tensors, dim=0)
-
-
 def create_confidence_label(predictions, top_k=5):
     """
     Render a formatted multiline prediction list
@@ -120,31 +112,4 @@ def create_confidence_label(predictions, top_k=5):
 
 def get_class_names():
     """Return the loaded class names from the txt file."""
-    return CLASS_NAMES
-
-if __name__ == "__main__":
-    print("Testing utility functions...")
-
-    test_names = [
-        "Tomato___Late_blight",
-        "Apple___healthy",
-        "Corn_(maize)___Common_rust_"
-    ]
-
-    print("\nClass name formatting:")
-    for name in test_names:
-        print(f"  {name} -> {format_class_name(name)}")
-
-    print("\nDisease info:")
-    for name in test_names:
-        info = get_disease_info(name)
-        print(f"  {name}:")
-        print(f"    Plant: {info['plant']}")
-        print(f"    Disease: {info['disease']}")
-        print(f"    Healthy: {info['is_healthy']}")
-
-    print("\nImage preprocessing:")
-    dummy_image = Image.new('RGB', (512, 512), color='red')
-    tensor = preprocess_image(dummy_image)
-    print(f"  Input size: {dummy_image.size}")
-    print(f"  Output tensor shape: {tensor.shape}")
+    return CLASS_NAMES