Update app.py

app.py

@@ -51,7 +51,7 @@ 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,image_upload,threshold):
+def detect_objects_from_url(model_name,url,threshold):
     
     #Extract model and feature extractor
     feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
@@ -68,8 +68,29 @@ def detect_objects(model_name,url,image_upload,threshold):
         
         if validators.url(url):
             image = Image.open(requests.get(url, stream=True).raw)
+    
+    #Make prediction
+    processed_outputs = make_prediction(image, feature_extractor, model)
+    
+    #Visualize prediction
+    viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label)
+    
+    return viz_img   
+    
+ def detect_objects_from_upload(model_name,image_upload,threshold):
+    
+    #Extract model and feature extractor
+    feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
+    
+    if 'detr' in model_name:
+        
+        model = DetrForObjectDetection.from_pretrained(model_name)
+        
+    elif 'yolos' in model_name:
+    
+        model = YolosForObjectDetection.from_pretrained(model_name)
             
-    elif image_upload:
+    if image_upload:
         image = image_upload
     
     #Make prediction
@@ -78,7 +99,7 @@ def detect_objects(model_name,url,image_upload,threshold):
     #Visualize prediction
     viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label)
     
-    return viz_img   
+    return viz_img
 
 #examples=[['facebook/detr-resnet-50','https://media-cldnry.s-nbcnews.com/image/upload/t_fit-1500w,f_auto,q_auto:best/newscms/2020_14/3290756/200331-wall-street-ew-#343p.jpg',,0.7]
 
@@ -123,8 +144,8 @@ with demo:
             img_but = gr.Button('Detect')
         
     
-    url_but.click(detect_objects,inputs=[options,url_input,None,slider_input],outputs=img_output_from_url,queue=True)
-    img_but.click(detect_objects,inputs=[options,None,img_input,slider_input],outputs=img_output_from_upload,queue=True)
+    url_but.click(detect_objects_from_url,inputs=[options,url_input,slider_input],outputs=img_output_from_url,queue=True)
+    img_but.click(detect_objects,inputs=[options,img_input,slider_input],outputs=img_output_from_upload,queue=True)
     
     
 demo.launch(enable_queue=True)