Update app.py

app.py

@@ -4,7 +4,6 @@ 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")
@@ -51,36 +50,26 @@ 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
+# Define Gradio interface with local image examples
 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://www.quickanddirtytips.com/wp-content/uploads/2022/05/ezgif.com-gif-maker-3.jpg",
-        "https://img.freepik.com/free-photo/people-posing-together-registration-day_23-2149096794.jpg",
-        "https://www.shutterstock.com/shutterstock/photos/2077329079/display_1500/stock-photo-pushkar-india-nov-people-walking-in-a-busy-road-in-the-street-market-in-holy-city-2077329079.jpg",
-        "https://static.autox.com/uploads/2023/01/mahindra-xuv400.jpg",
-        "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQrmOeQJN9IzbR8ofJDpxcIHDSOSXg_5hzgfA&s"
-    ]
 
+    # Use local image files instead of URLs
     examples = gr.Examples(
-        examples=example_urls,
+        examples=[
+            ["image1.jpg"],
+            ["image2.jpg"]
+        ],
         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)