Update app.py

app.py

@@ -1,9 +1,10 @@
 import gradio as gr
 from refacer import Refacer
+import argparse
 import os
 import requests
 
-# Model download URL and path
+# Hugging Face URL to download the model
 model_url = "https://huggingface.co/ofter/4x-UltraSharp/resolve/main/inswapper_128.onnx"
 model_path = "./inswapper_128.onnx"
 
@@ -21,72 +22,70 @@ def download_model():
     else:
         print("Model already exists.")
 
-# Download the model
+# Download the model when the script runs
 download_model()
 
-# Initialize the Refacer class
-refacer = Refacer(force_cpu=True)  # Use CPU for simplicity
+# Argument parser
+parser = argparse.ArgumentParser(description='Refacer')
+parser.add_argument("--max_num_faces", type=int, help="Max number of faces on UI", default=5)
+parser.add_argument("--force_cpu", help="Force CPU mode", default=False, action="store_true")
+parser.add_argument("--share_gradio", help="Share Gradio", default=False, action="store_true")
+parser.add_argument("--server_name", type=str, help="Server IP address", default="127.0.0.1")
+parser.add_argument("--server_port", type=int, help="Server port", default=7860)
+parser.add_argument("--colab_performance", help="Use in colab for better performance", default=False, action="store_true")
+args = parser.parse_args()
 
-# Function to process the video
-def reface_video(video_path, *faces):
-    try:
-        # Prepare face data
-        face_data = [
-            {
-                "origin": face["origin"],
-                "destination": face["destination"],
-                "threshold": face.get("threshold", 0.2),
-            }
-            for face in faces if face.get("origin") and face.get("destination")
-        ]
+# Initialize the Refacer class
+refacer = Refacer(force_cpu=args.force_cpu, colab_performance=args.colab_performance)
 
-        # Process the video
-        print("Processing video...")
-        refaced_video_path = refacer.reface(video_path, face_data)
+num_faces = args.max_num_faces
 
-        # Return only the refaced video
-        return refaced_video_path
-    except Exception as e:
-        return f"Error processing video: {e}"
+# Run function for refacing video
+def run(*vars):
+    video_path = vars[0]
+    origins = vars[1:(num_faces+1)]
+    destinations = vars[(num_faces+1):(num_faces*2)+1]
+    thresholds = vars[(num_faces*2)+1:]
 
-# Build Gradio UI
-def build_ui():
-    num_faces = 5  # Maximum number of faces
+    faces = []
+    for k in range(0, num_faces):
+        if origins[k] is not None and destinations[k] is not None:
+            faces.append({
+                'origin': origins[k],
+                'destination': destinations[k],
+                'threshold': thresholds[k]
+            })
 
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown("# Refacer: AI-Powered Video Face Replacement")
+    # Call refacer to process video and get file path
+    refaced_video_path = refacer.reface(video_path, faces)  # refaced video path
+    print(f"Refaced video can be found at {refaced_video_path}")
 
-        with gr.Row():
-            input_video = gr.Video(label="Upload a video", source="upload")  # Corrected the keyword to 'source'
-            output_video = gr.Video(label="Refaced video", interactive=False)
+    return refaced_video_path  # Return the file path to show in Gradio output
 
-        # Create inputs for multiple faces
-        faces = []
-        for i in range(num_faces):
-            with gr.Tab(f"Face #{i+1}"):
-                with gr.Row():
-                    origin = gr.Image(label="Origin Face", type="filepath")
-                    destination = gr.Image(label="Destination Face", type="filepath")
-                    threshold = gr.Slider(
-                        label="Threshold", minimum=0.0, maximum=1.0, value=0.2
-                    )
-                faces.append({"origin": origin, "destination": destination, "threshold": threshold})
+# Prepare Gradio components
+origin = []
+destination = []
+thresholds = []
 
-        with gr.Row():
-            submit_button = gr.Button("Reface Video")
+with gr.Blocks() as demo:
+    with gr.Row():
+        gr.Markdown("# Refacer")
+    with gr.Row():
+        video = gr.Video(label="Original video", format="mp4")
+        video2 = gr.Video(label="Refaced video", interactive=False, format="mp4")
 
-        # Connect inputs and outputs
-        inputs = [input_video] + [face["origin"] for face in faces] + [
-            face["destination"] for face in faces
-        ] + [face["threshold"] for face in faces]
-        submit_button.click(
-            fn=reface_video, inputs=inputs, outputs=[output_video]
-        )
+    for i in range(0, num_faces):
+        with gr.Tab(f"Face #{i+1}"):
+            with gr.Row():
+                origin.append(gr.Image(label="Face to replace"))
+                destination.append(gr.Image(label="Destination face"))
+            with gr.Row():
+                thresholds.append(gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.2))
+    
+    with gr.Row():
+        button = gr.Button("Reface", variant="primary")
 
-    return demo
+    button.click(fn=run, inputs=[video] + origin + destination + thresholds, outputs=[video2])
 
 # Launch the Gradio app
-if __name__ == "__main__":
-    ui = build_ui()
-    ui.queue().launch(server_name="0.0.0.0", server_port=7860, debug=True)
+demo.queue().launch(show_error=True, share=args.share_gradio, server_name="0.0.0.0", server_port=args.server_port)