Update app.py

app.py

@@ -18,21 +18,6 @@ COLORS = [
     [0.301, 0.745, 0.933]
 ]
 
-title = 'Object Detection App with DETR and YOLOS'
-
-description = """
-Links to HuggingFace Models:
-
-- [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50)  
-- [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101)  
-- [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small)
-
-"""
-
-models = ["facebook/detr-resnet-50","facebook/detr-resnet-101",'hustvl/yolos-small']
-
-options = gr.Dropdown(choices=models,label='Select Object Detection Model',show_label=True)
-
 def make_prediction(img, feature_extractor, model):
     inputs = feature_extractor(img, return_tensors="pt")
     outputs = model(**inputs)
@@ -95,13 +80,47 @@ def detect_objects(model_name,url,image_upload,threshold):
 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],
 ['facebook/detr-resnet-50',,'IMG_5205.JPG',0.7]] 
 
-gr.Interface(
-    fn = detect_objects,
-    inputs = [options,
-             gr.Textbox(lines=1,label='Enter valid image URL here..'),
-             gr.Image(type='pil'),
-             gr.Slider(minimum=0.2,maximum=1,value=0.7,label='Prediction Threshold')],
-    outputs=gr.Image(shape=(450,450)),
-    title = title,
-    description=description,
-    examples=examples).launch(enable_queue=True)
+title = 'Object Detection App with DETR and YOLOS'
+
+description = """
+Links to HuggingFace Models:
+
+- [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50)  
+- [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101)  
+- [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small)
+
+"""
+
+models = ["facebook/detr-resnet-50","facebook/detr-resnet-101",'hustvl/yolos-small']
+
+options = gr.Dropdown(choices=models,label='Select Object Detection Model',show_label=True)
+
+app = gr.blocks()
+
+with app:
+    gr.Markdown(title)
+    gr.Markdown(description)
+    options
+    slider_input = gr.Slider(minimum=0.2,maximum=1,value=0.7,label='Prediction Threshold')
+    
+    with gr.Tabs():
+        with gr.Tabitem('Image URL'):
+            with gr.Row():
+                url_input = gr.Textbox(lines=1,label='Enter valid image URL here..')
+                img_output_from_url = gr.Image(shape=(450,450))
+            
+            url_but = gr.Button('Detect')
+     
+        with gr.Tabitem('Image Upload):
+            with gr.Row():
+                img_input = gr.Image(type='pil')
+                img_output_from_upload= gr.Image(shape=(450,450))
+                
+            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],outputsimg_output_from_upload,queue=True)
+    
+    
+app.launch(enable_queue=True)