Update app.py

app.py

@@ -68,12 +68,36 @@ def detect_objects(model_name,url_input,image_input,threshold):
     
     #Make prediction
     processed_outputs = make_prediction(image, feature_extractor, model)
+    print(processed_outputs)
     
     #Visualize prediction
     viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label)
     
     return viz_img   
+
+def detect_objects2(model_name,url_input,image_input,threshold):
+    
+    #Extract model and feature extractor
+    feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
+    
+    
         
+    model = DetrForObjectDetection.from_pretrained(model_name)
+        
+
+    
+
+    image = image_input
+    
+    #Make prediction
+    processed_outputs = make_prediction(image, feature_extractor, model)
+    print(processed_outputs)
+    
+    #Visualize prediction
+    viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label)
+    
+    return processed_outputs 
+    
 def set_example_image(example: list) -> dict:
     return gr.Image.update(value=example[0])
 
@@ -105,6 +129,8 @@ demo = gr.Blocks(css=css)
 with demo:
     gr.Markdown(title)
     gr.Markdown(description)
+    gr.Markdown(detect_objects2)
+    
     
     options = gr.Dropdown(choices=models,label='Select Object Detection Model',show_label=True)
     slider_input = gr.Slider(minimum=0.1,maximum=1,value=0.7,label='Prediction Threshold')
@@ -121,6 +147,13 @@ with demo:
                 example_images = gr.Dataset(components=[img_input], samples=[[path.as_posix()] for path in sorted(pathlib.Path('images').rglob('*.jpg'))])
                 
             img_but = gr.Button('Detect')
+
+        with gr.Blocks() as demo:
+            name = gr.Textbox(label="Name")
+            output = gr.Textbox(label="Results")
+            greet_btn = gr.Button("Results")
+            greet_btn.click(fn=detect_objects2, inputs=[options,img_input,img_input,slider_input], outputs=output, queue=True)
+