Update app.py

app.py

@@ -1,27 +1,22 @@
 import gradio as gr
 from inference import load_model, run_inference
 
-# Load the model once when the app starts
 net_feat, net_cls = load_model()
 
-# Define the Gradio function
+
 def classify_image(image):
     return run_inference(image, net_feat, net_cls)
 
-# Example image for demo
-example_images = ["example.jpeg", "example2.webp","image1.jpeg"] 
+example_images = ["example.jpeg", "example2.webp","image2.jpeg"] 
 
-# Create the Gradio Interface
 interface = gr.Interface(
     fn=classify_image, 
-    inputs=gr.Image(type="filepath"),  # Pass image file paths
-    outputs=gr.Textbox(label="Predicted clothing type:"),  # Returns the predicted class name
+    inputs=gr.Image(type="filepath"),  
+    outputs=gr.Textbox(label="Predicted clothing type:"),  
     title="Clothing1M Classifier",
     description="Upload an image of clothing image.",
-    examples=example_images  # Provides a sample of images for testing
+    examples=example_images 
 )
 
-
-# Launch the Gradio app
 if __name__ == "__main__":
     interface.launch()