Delete app.py.txt

app.py.txt

@@ -1,117 +0,0 @@
-import gradio as gr
-import cv2
-import multiprocessing
-import os
-import requests
-from refacer import Refacer
-
-# 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"
-
-# Function to download the model
-def download_model():
-    if not os.path.exists(model_path):
-        print("Downloading inswapper_128.onnx...")
-        response = requests.get(model_url)
-        if response.status_code == 200:
-            with open(model_path, 'wb') as f:
-                f.write(response.content)
-            print("Model downloaded successfully!")
-        else:
-            raise Exception(f"Failed to download the model. Status code: {response.status_code}")
-    else:
-        print("Model already exists.")
-
-# Download the model when the script runs
-download_model()
-
-# Initialize Refacer class (force CPU mode)
-refacer = Refacer(force_cpu=True)
-
-# Dummy function to simulate frame-level processing
-def process_frame(frame, origin_face, destination_face, threshold):
-    # Simulate face swapping or any processing needed
-    result_frame = refacer.reface(frame, [{
-        'origin': origin_face,
-        'destination': destination_face,
-        'threshold': threshold
-    }])
-    return result_frame
-
-# Function to process the video in parallel using multiprocessing
-def process_video(video_path, origins, destinations, thresholds, max_processes=2):
-    cap = cv2.VideoCapture(video_path)
-    frames = []
-
-    # Read all frames from the video
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-        frames.append(frame)
-
-    cap.release()
-
-    # Parallel processing of frames with limited processes (for CPU optimization)
-    with multiprocessing.Pool(processes=max_processes) as pool:
-        processed_frames = pool.starmap(process_frame, [
-            (frame, origins[min(i, len(origins) - 1)], destinations[min(i, len(destinations) - 1)], thresholds[min(i, len(thresholds) - 1)])
-            for i, frame in enumerate(frames)
-        ])
-
-    # Saving the processed frames back into a video
-    output_video_path = "processed_video.mp4"
-    fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # Compression using mp4 codec
-    out = cv2.VideoWriter(output_video_path, fourcc, 30.0, (640, 360))  # Reduce resolution to speed up processing
-
-    for frame in processed_frames:
-        out.write(frame)
-
-    out.release()
-    return output_video_path
-
-# Gradio Interface function
-def run(video_path, *vars):
-    # Split the inputs into origins, destinations, and thresholds based on num_faces
-    num_faces = 5  # You can adjust this based on your UI
-    origins = vars[:num_faces]
-    destinations = vars[num_faces:2*num_faces]
-    thresholds = vars[2*num_faces:]
-
-    # Ensure there are no index errors by limiting the number of inputs
-    if len(origins) != num_faces or len(destinations) != num_faces or len(thresholds) != num_faces:
-        return "Please provide input for all faces."
-
-    refaced_video_path = process_video(video_path, origins, destinations, thresholds)
-    print(f"Refaced video can be found at {refaced_video_path}")
-
-    return refaced_video_path
-
-# Prepare Gradio components
-origin = []
-destination = []
-thresholds = []
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        gr.Markdown("# Refacer")
-    with gr.Row():
-        video_input = gr.Video(label="Original video", format="mp4")
-        video_output = gr.Video(label="Refaced video", interactive=False, format="mp4")
-
-    for i in range(5):  # Set max faces to 5
-        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")
-
-    button.click(fn=run, inputs=[video_input] + origin + destination + thresholds, outputs=[video_output])
-
-# Launch the Gradio app
-demo.launch(show_error=True, server_name="0.0.0.0", server_port=7860)