replace app.py

app.py

@@ -6,29 +6,31 @@ import torch
 
 def yolov7_inference(
     image: gr.inputs.Image = None,
-
+    conf_threshold: gr.inputs.Slider = 0.50,
 ):
 
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     path = 'y7-prdef.pt'
     model = torch.hub.load("WongKinYiu/yolov7","custom",f"{path}")
-
+    model.conf = conf_threshold
     results = model([image], size=640)
     return results.render()[0]
         
 
 inputs = [
     gr.inputs.Image(type="pil", label="Input Image"),
+    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.50, step=0.05, label="Confidence Threshold"),
 ]
 
-
 demo_app = gr.Interface(
     fn=yolov7_inference,
     inputs=inputs,
     outputs=gr.outputs.Image(type="filepath", label="Output Image"),
-    title="Yolov7 | Jar lid product defects",
-    examples=['t1.JPG'],
+    title="Detection of jar lid defects (Yolov7)",
+    description = "App detecting jar lids that are damaged (deformation, hole, scratch) versus intact. | Ruthger Righart ",
+    article = "<p style='text-align: center'><a href='https://www.rrighart.com' target='_blank'>Webpage</a></p> <p style='text-align: center'><a href='https://www.kaggle.com/code/rrighart/detection-of-product-defects-using-yolov7' target='_blank'>Kaggle</a></p>",
+    examples=[['t1.JPG', 0.50]],
     cache_examples=True,
 )
-demo_app.launch(debug=True, enable_queue=True)
+demo_app.launch(debug=False, enable_queue=True)