Update README.md and app.py for enhanced UI experience

README.md

@@ -1,8 +1,8 @@
 ---
 title: MetaCLIP
-emoji: 🐠
-colorFrom: blue
-colorTo: purple
+emoji: 👁️
+colorFrom: red
+colorTo: orange
 sdk: gradio
 sdk_version: 3.50.2
 app_file: app.py

app.py

@@ -5,6 +5,16 @@ import numpy as np
 from transformers import CLIPProcessor, CLIPModel
 
 IMAGENET_CLASSES_FILE = "imagenet-classes.txt"
+EXAMPLES = ["dog.jpeg", "car.png"]
+
+MARKDOWN = """
+# Zero-Shot Image Classification with MetaCLIP
+
+This is the demo for a zero-shot image classification model based on 
+[MetaCLIP](https://github.com/facebookresearch/MetaCLIP), described in the paper 
+[Demystifying CLIP Data](https://arxiv.org/abs/2309.16671) that formalizes CLIP data 
+curation as a simple algorithm.
+"""
 
 
 def load_text_lines(file_path: str) -> List[str]:
@@ -31,12 +41,21 @@ def classify_image(input_image) -> str:
 
 
 with gr.Blocks() as demo:
-    gr.Markdown("""# Zero-Shot Image Classification with MetaCLIP""")
+    gr.Markdown(MARKDOWN)
     with gr.Row():
         image = gr.Image(image_mode='RGB', type='pil')
-        output_text = gr.Textbox()
-        submit_button = gr.Button("Submit")
+        output_text = gr.Textbox(label="Output")
+    submit_button = gr.Button("Submit")
 
     submit_button.click(classify_image, inputs=[image], outputs=output_text)
 
+    gr.Examples(
+        examples=EXAMPLES,
+        fn=classify_image,
+        inputs=[image],
+        outputs=[output_text],
+        cache_examples=True,
+        run_on_click=True
+    )
+
 demo.queue(max_size=64).launch(debug=False)