Uploaded app.py

app.py

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+import os
+import gradio as gr
+import torch
+from transformers import pipeline
+import cv2
+
+# Load Hugging Face face swap model using the DeepFaceLab model by senhan007
+face_swap_model = pipeline("image-to-image", model="senhan007/DeepFaceLab")
+
+def swap_faces(image, video):
+    image_path = "uploaded_image.jpg"
+    video_path = "uploaded_video.mp4"
+    output_path = "swapped_video.mp4"
+
+    # Save uploaded files
+    image.save(image_path)
+    video.save(video_path)
+
+    # Open the video file
+    video_cap = cv2.VideoCapture(video_path)
+    frame_rate = video_cap.get(cv2.CAP_PROP_FPS)
+    width = int(video_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(video_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    output_video = cv2.VideoWriter(output_path, fourcc, frame_rate, (width, height))
+
+    while True:
+        ret, frame = video_cap.read()
+        if not ret:
+            break
+
+        # Apply the face swap (replace with model logic)
+        # For now, this is just a placeholder; you'll need to integrate the model inference here.
+        swapped_frame = frame
+
+        output_video.write(swapped_frame)
+
+    video_cap.release()
+    output_video.release()
+
+    return output_path
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=swap_faces,
+    inputs=[
+        gr.Image(type="pil", label="Upload Reference Image"),
+        gr.Video(type="file", label="Upload Video"),
+    ],
+    outputs=gr.Video(label="Face Swapped Video"),
+    title="Video Face Swap",
+    description="Upload a reference image and a video to swap faces."
+)
+
+iface.launch()