Update app.py

app.py

@@ -4,6 +4,7 @@ from PIL import Image
 import gradio as gr
 from transformers import pipeline
 import matplotlib.pyplot as plt
+import requests
 
 # Initialize the models
 detector50 = pipeline(model="facebook/detr-resnet-50")
@@ -50,13 +51,37 @@ def infer(model, in_pil_img):
     results = detector101(in_pil_img) if model == "detr-resnet-101" else detector50(in_pil_img)
     return get_figure(in_pil_img, results)
 
+def url_to_image(url):
+    response = requests.get(url)
+    img = Image.open(io.BytesIO(response.content))
+    return img
+
 # Define Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("## DETR Object Detection")
     model = gr.Radio(["detr-resnet-50", "detr-resnet-101"], value="detr-resnet-50", label="Model name")
+    
+    # Example images from the internet
+    example_urls = [
+        "https://upload.wikimedia.org/wikipedia/commons/4/47/PNG_transparency_demonstration_1.png",
+        "https://upload.wikimedia.org/wikipedia/commons/thumb/6/6e/Golde33443.jpg/800px-Golde33443.jpg",
+        "https://upload.wikimedia.org/wikipedia/commons/e/e3/Brazilian_Jaguar.jpg",
+        "https://upload.wikimedia.org/wikipedia/commons/f/f9/Big_Sur%2C_California.jpg",
+        "https://upload.wikimedia.org/wikipedia/commons/2/2c/Rotating_earth_%28large%29.gif"
+    ]
+
+    examples = gr.Examples(
+        examples=example_urls,
+        inputs=[gr.Image(type="pil")],
+        fn=lambda x: url_to_image(x),
+        label="Try these example images"
+    )
+
     input_image = gr.Image(label="Input image", type="pil")
     output_image = gr.Image(label="Output image")
     send_btn = gr.Button("Infer")
+    
+    # Trigger inference on button click
     send_btn.click(fn=infer, inputs=[model, input_image], outputs=output_image)
 
 demo.launch()