Update app.py

app.py

@@ -51,20 +51,15 @@ def visualize_prediction(pil_img, output_dict, threshold=0.7, id2label=None):
     plt.axis("off")
     return fig2img(plt.gcf())
 
-def detect_objects(model_name,url_input,image_input,threshold):
+def detect_objects(model_name,image_input,threshold):
     
     #Extract model and feature extractor
     feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
     
-    if 'detr' in model_name:
+    if 'detr' in model_name:    
+        model = DetrForObjectDetection.from_pretrained(model_name)     
         
-        model = DetrForObjectDetection.from_pretrained(model_name)
-       
-    
-    if validators.url(url_input):
-        image = Image.open(requests.get(url_input, stream=True).raw)
-        
-    elif image_input:
+    if image_input:
         image = image_input
     
     #Make prediction
@@ -89,8 +84,6 @@ Links to HuggingFace Models:
 """
 
 models = ["facebook/detr-resnet-50","facebook/detr-resnet-101"]
-urls = ["https://c8.alamy.com/comp/J2AB4K/the-new-york-stock-exchange-on-the-wall-street-in-new-york-J2AB4K.jpg"]
-
 
 css = '''
 h1#title {
@@ -120,8 +113,7 @@ with demo:
             img_but = gr.Button('Detect')
         
     
-    url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_url,queue=True)
-    img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_upload,queue=True)
+    img_but.click(detect_objects,inputs=[options,img_input,slider_input],outputs=img_output_from_upload,queue=True)
     example_images.click(fn=set_example_image,inputs=[example_images],outputs=[img_input])