Upload app.py

app.py

@@ -107,7 +107,7 @@ def predict(image, model_id, threshold):
 
 
 with gr.Blocks() as demo:
-    gr.Markdown("""# <p align='center'><img width='500px' src='https://user-images.githubusercontent.com/34196005/215836968-fb54e066-a524-4caf-b469-92bbaa96f921.gif' /></p>
+    gr.Markdown("""# <p align='center'><a href="https://github.com/keremberke/awesome-yolov8-models" target='_blank'><img width='500px' src='https://user-images.githubusercontent.com/34196005/215836968-fb54e066-a524-4caf-b469-92bbaa96f921.gif' /></a></p>
     <p style='text-align: center'>
         <br> <a href='https://yolov8.xyz' target='_blank'>project website</a> | <a href='https://github.com/keremberke/awesome-yolov8-models' target='_blank'>project github</a> 
     </p>
@@ -128,20 +128,22 @@ with gr.Blocks() as demo:
         with gr.Row():
             half_ind = int(len(det_examples) / 2)
             with gr.Column():
-                detect_examples = gr.Examples(
-                        det_examples[:half_ind],
+                gr.Examples(
+                        det_examples[half_ind:],
                         inputs=[detect_input, detect_model_id, detect_threshold],
                         outputs=detect_output,
                         fn=predict,
                         cache_examples=False,
+                        run_on_click=True,
                     )
             with gr.Column():
-                detect_examples = gr.Examples(
+                gr.Examples(
                         det_examples[:half_ind],
                         inputs=[detect_input, detect_model_id, detect_threshold],
                         outputs=detect_output,
                         fn=predict,
                         cache_examples=False,
+                        run_on_click=True,
                     )
     with gr.Tab("Segmentation"):
         with gr.Row():
@@ -153,22 +155,24 @@ with gr.Blocks() as demo:
             with gr.Column():
                 segment_output = gr.Image(label="Predictions:", interactive=False)
         with gr.Row():
-            half_ind = int(len(det_examples) / 2)
+            half_ind = int(len(seg_examples) / 2)
             with gr.Column():
-                segment_examples = gr.Examples(
-                        seg_examples[:half_ind],
+                gr.Examples(
+                        seg_examples[half_ind:],
                         inputs=[segment_input, segment_model_id, segment_threshold],
                         outputs=segment_output,
                         fn=predict,
                         cache_examples=False,
+                        run_on_click=True,
                     )
             with gr.Column():
-                segment_examples = gr.Examples(
+                gr.Examples(
                         seg_examples[:half_ind],
                         inputs=[segment_input, segment_model_id, segment_threshold],
                         outputs=segment_output,
                         fn=predict,
                         cache_examples=False,
+                        run_on_click=True,
                     )
     with gr.Tab("Classification"):
         with gr.Row():
@@ -182,32 +186,34 @@ with gr.Blocks() as demo:
                     label="Predictions:", show_label=True, num_top_classes=5
                 )
         with gr.Row():
-            half_ind = int(len(det_examples) / 2)
+            half_ind = int(len(cls_examples) / 2)
             with gr.Column():
-                classify_examples = gr.Examples(
+                gr.Examples(
                         cls_examples[half_ind:],
                         inputs=[classify_input, classify_model_id, classify_threshold],
                         outputs=classify_output,
                         fn=predict,
                         cache_examples=False,
+                        run_on_click=True,
                     )
             with gr.Column():
-                classify_examples = gr.Examples(
+                gr.Examples(
                         cls_examples[:half_ind],
                         inputs=[classify_input, classify_model_id, classify_threshold],
                         outputs=classify_output,
                         fn=predict,
                         cache_examples=False,
+                        run_on_click=True,
                     )
 
     detect_button.click(
-        predict, inputs=[detect_input, detect_model_id, detect_threshold], outputs=detect_output
+        predict, inputs=[detect_input, detect_model_id, detect_threshold], outputs=detect_output, api_name="detect"
     )
     segment_button.click(
-        predict, inputs=[segment_input, segment_model_id, segment_threshold], outputs=segment_output
+        predict, inputs=[segment_input, segment_model_id, segment_threshold], outputs=segment_output, api_name="segment"
     )
     classify_button.click(
-        predict, inputs=[classify_input, classify_model_id, classify_threshold], outputs=classify_output
+        predict, inputs=[classify_input, classify_model_id, classify_threshold], outputs=classify_output, api_name="classify"
     )
 
 demo.launch(server_port=8080)