Update app.py

app.py

@@ -139,9 +139,9 @@ model_name = 'resnet100.onnx'
 # Load ONNX model
 model = get_model(ctx , model_name)
 
-def inference(img1,img2):
+def inference(first,second):
   # Load first image
-  img1 = cv2.imread(img1)
+  img1 = cv2.imread(first)
   
   # Preprocess first image
   pre1 = get_input(detector,img1)
@@ -150,7 +150,7 @@ def inference(img1,img2):
   out1 = get_feature(model,pre1)
   
   # Load second image
-  img2 = cv2.imread('player2.jpg')
+  img2 = cv2.imread(second)
   
   # Preprocess second image
   pre2 = get_input(detector,img2)
@@ -165,4 +165,6 @@ def inference(img1,img2):
   # Print predictions
   return 'Distance = %f' %(dist),'Similarity = %f' %(sim)
   
-gr.Interface(inference,[gr.inputs.Image(type="filepath"),gr.inputs.Image(type="filepath")],["text","text"]).launch()
+title="ArcFace"
+description="ArcFace is a CNN based model for face recognition which learns discriminative features of faces and produces embeddings for input face images. To enhance the discriminative power of softmax loss, a novel supervisor signal called additive angular margin (ArcFace) is used here as an additive term in the softmax loss. ArcFace can use a variety of CNN networks as its backend, each having different accuracy and performance."
+gr.Interface(inference,[gr.inputs.Image(type="filepath"),gr.inputs.Image(type="filepath")],["text","text"],title=title,description=description).launch(enable_queue=True)