deploy space gradio

README.md

@@ -1,15 +1,18 @@
 ---
-title: Deepfake Detect
-emoji: 👁
-colorFrom: blue
-colorTo: gray
+title: deepfake-detect
+emoji: 🔍
+colorFrom: gray
+colorTo: red
 sdk: gradio
-sdk_version: 6.30.0
-python_version: '3.12'
+sdk_version: 5.49.1
 app_file: app.py
-pinned: false
 license: mit
-short_description: Open-source face deepfake detector for images and videos.
+models:
+  - CodeRafay/deepfake-detect
+short_description: Detect face deepfakes in images and videos
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Upload an image or video to get a fake score per face, with a per-frame chart for videos.
+It uses the latest model from [CodeRafay/deepfake-detect](https://huggingface.co/CodeRafay/deepfake-detect): unseen-dataset (Celeb-DF v2) AUC 0.914.
+
+**A score is evidence, not proof.** The model is for research use only. Code and full write-up: https://github.com/CodeRafay/deepfake-detect

app.py

@@ -0,0 +1,58 @@
+"""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()

requirements.txt

@@ -0,0 +1 @@
+deepfake-detect @ git+https://github.com/CodeRafay/deepfake-detect