app.py

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()