Update app.py

app.py

@@ -335,11 +335,11 @@ def main(image_bgr, backbone_choice, box_model = torchvision.models.detection.fa
     box_model.to(CTX)
     box_model.eval()
     
-    if backbone_choice == "HRNet":
-      backbone_choice = "tph_a4_256x192"
-    else:
-      backbone_choice == "ResNet"
+    if backbone_choice == "ResNet":
       backbone_choice = "tpr_a4_256x192"
+    else:
+      backbone_choice == "HRNet"
+      backbone_choice = "tph_a4_256x192"
     model = torch.hub.load('yangsenius/TransPose:main', backbone_choice , pretrained=True)
 
     img_dimensions = (256, 192)
@@ -364,10 +364,10 @@ def main(image_bgr, backbone_choice, box_model = torchvision.models.detection.fa
     return image_bgr
 
 title = "TransPose"
-description = "Gradio demo for TransPose: Keypoint localization via Transformer. Dataset: COCO train2017 & COCO val2017."
+description = "Gradio demo for TransPose: Keypoint localization via Transformer. Dataset: COCO train2017 & COCO val2017. Default backbone selection = HRNet"
 article = "<div style='text-align: center;'><a href='https://github.com/yangsenius/TransPose' target='_blank'>Full credits: github.com/yangsenius/TransPose</a></div>"
 
-examples = [["./examples/one.jpg"], ["./examples/two.jpg"]]
+examples = [["./examples/one.jpg", "HRNet"], ["./examples/two.jpg", "HRNet"]]
 
 iface = gr.Interface(main, inputs=[gr.inputs.Image(), gr.inputs.Radio(["HRNet", "ResNet"])], outputs="image", description=description, article=article, title=title, examples=examples)
 iface.launch(enable_queue=True, debug='True')