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b4c0acb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | """Hugging Face Space (Gradio + ZeroGPU). For local use, the Streamlit app is deepfake_detect/app.py."""
import gradio as gr
import pandas as pd
import spaces
from deepfake_detect import Detector
from deepfake_detect.faces import draw_faces
# ZeroGPU: load once onto "cuda" at startup; a real GPU is attached only while a @spaces.GPU function runs
det = Detector(device="cuda")
ABOUT = """# deepfake-detect
Upload an image or a video. Every face is scored by a CLIP ViT-L/14 with tuned LayerNorms
([model](https://huggingface.co/CodeRafay/deepfake-detect): 0.914 AUC on unseen Celeb-DF v2).
**A score is evidence, not proof.** Compression, lighting and new kinds of fakes all shift it.
Don't make decisions about people based on this alone. [Code and write-up](https://github.com/CodeRafay/deepfake-detect)"""
def verdict(r):
return {"fake": r["score"], "real": 1 - r["score"]}
@spaces.GPU(duration=60)
def scan_image(rgb):
if rgb is None:
return None, None, None
r = det.predict_image(rgb)
return verdict(r), draw_faces(rgb, r), r
@spaces.GPU(duration=120)
def scan_video(path):
if path is None:
return None, None, None
r = det.predict_video(path)
return verdict(r), pd.DataFrame(r["frames"]), r
with gr.Blocks(title="deepfake-detect") as demo:
gr.Markdown(ABOUT)
with gr.Tab("Image"):
with gr.Row():
img = gr.Image(type="numpy", label="Image")
with gr.Column():
img_label = gr.Label(label="Verdict")
img_out = gr.Image(label="Faces (red = above threshold)")
img_json = gr.JSON(label="Raw result")
img.change(scan_image, img, [img_label, img_out, img_json])
with gr.Tab("Video"):
with gr.Row():
vid = gr.Video(label="Video")
with gr.Column():
vid_label = gr.Label(label="Verdict")
vid_plot = gr.LinePlot(x="time", y="score", y_lim=[0, 1], label="Fake score per sampled frame")
vid_json = gr.JSON(label="Raw result")
vid.change(scan_video, vid, [vid_label, vid_plot, vid_json])
demo.launch()
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