Delete app.py

app.py

@@ -1,56 +0,0 @@
-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()