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| """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"]} | |
| 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 | |
| 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() | |