Update app.py

app.py

@@ -7,7 +7,6 @@ import tempfile
 import time
 import gradio as gr
 
-
 class Predictor:
     def __init__(self):
         self.setup()
@@ -25,7 +24,6 @@ class Predictor:
             )
         os.chdir('..')
 
-        """Load the model into memory to make running multiple predictions efficient"""
         self.face_swapper = insightface.model_zoo.get_model('models/inswapper_128.onnx',
                                                             providers=onnxruntime.get_available_providers())
         self.face_enhancer = gfpgan.GFPGANer(model_path='models/GFPGANv1.4.pth', upscale=1)
@@ -42,15 +40,10 @@ class Predictor:
             return None
 
     def predict(self, input_image, swap_image):
-        """Run a single prediction on the model"""
         try:
             frame = cv2.imread(input_image.name)
             face = self.get_face(frame)
             source_face = self.get_face(cv2.imread(swap_image.name))
-            try:
-                print(frame.shape, face.shape, source_face.shape)
-            except:
-                print("printing shapes failed.")
             result = self.face_swapper.get(frame, face, source_face, paste_back=True)
 
             _, _, result = self.face_enhancer.enhance(
@@ -64,7 +57,6 @@ class Predictor:
             print(f"{e}")
             return None
 
-
 # Instantiate the Predictor class
 predictor = Predictor()
 
@@ -75,15 +67,16 @@ footer {
 """
 
 # Create Gradio Interface
-iface = gr.Interface(css=css)
+iface = gr.Interface(
     fn=predictor.predict,
     inputs=[
         gr.inputs.Image(type="file", label="Target Image"),
         gr.inputs.Image(type="file", label="Swap Image")
     ],
     outputs=gr.outputs.Image(type="file", label="Result"),
-    examples=[["input.jpg", "swap img.jpg"]])
-
+    examples=[["input.jpg", "swap img.jpg"]],
+    css=css
+)
 
 # Launch the Gradio Interface
 iface.launch()