Update app.py

app.py

@@ -117,7 +117,7 @@ def export_json(results, model, img_size):
     ]
 
 
-def yolo_det(img, device, model_name, inference_size, conf, iou, label_opt, model_cls):
+def yolo_det(img, experiment_id, device, model_name, inference_size, conf, iou, label_opt, model_cls):
 
     global model, model_name_tmp, device_tmp
 
@@ -190,6 +190,7 @@ def main(args):
 
     # -------------------输入组件-------------------
     inputs_img = gr.inputs.Image(type="pil", label="Input Image")
+
     experiment_id = gr.inputs.Radio(
         choices=[
             "add [1,1,1,1,1] paths",
@@ -224,6 +225,7 @@ def main(args):
     # 输入参数
     inputs = [
         inputs_img,  # 输入图片
+        experiment_id, # path adding scheduler
         device,  # 设备
         inputs_model,  # 模型
         inputs_size,  # 推理尺寸
@@ -237,9 +239,9 @@ def main(args):
     outputs02 = gr.outputs.JSON(label="检测信息")
 
     # 标题
-    title = "基于Gradio的YOLOv5通用目标检测系统"
+    title = "LIVE: Towards Layer-wise Image Vectorization (CVPR 2022 Oral Presentation)"
     # 描述
-    description = "<div align='center'>可自定义目标检测模型、安装简单、使用方便</div>"
+    description = "<div align='center'>Layer-wise image vectorization</div>"
 
     # 示例图片
     examples = [
@@ -290,7 +292,7 @@ def main(args):
     ).launch(
         inbrowser=True,  # 自动打开默认浏览器
         show_tips=True,  # 自动显示gradio最新功能
-        favicon_path="./icon/logo.ico",
+        # favicon_path="./icon/logo.ico",
     )