working demo

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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640m.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:04fe3d77980780c1f8297dc6d7f942fd5b3abe6942a188f742a85241e4f634eb
+size 103538690

Dockerfile

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+FROM python:3.8-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    libglib2.0-0 \
+    libsm6 \
+    libxext6 \
+    libxrender-dev \
+    libgl1-mesa-glx \
+    wget \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860", "--workers", "4"]

README.md

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+---
+title: NudeNet
+emoji: 👀
+colorFrom: red
+colorTo: indigo
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+****

app.py

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+import os
+import base64
+import tempfile
+from io import BytesIO
+from PIL import Image
+from fastapi import FastAPI, File, UploadFile, HTTPException, Query
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel
+from nudenet import NudeDetector
+import cv2
+from moviepy.editor import VideoFileClip
+from typing import Optional
+
+app = FastAPI(
+    title="Nudenet API",
+    description="API for detecting nudity in images and videos using Nudenet",
+    version="1.0.0",
+    docs_url="/",
+    redoc_url="/redoc"
+)
+
+# Initialize NudeDetector with both models
+detector_320n = NudeDetector()
+detector_640m = NudeDetector(model_path="640m.onnx", inference_resolution=640)
+
+class Base64Image(BaseModel):
+    image: str
+
+@app.post("/detect", summary="Detect nudity in an image")
+async def detect_nudity(image: UploadFile = File(...), use_640m: bool = Query(False, description="Use the 640m model instead of the default 320n model")):
+    if not image.content_type.startswith('image/'):
+        raise HTTPException(status_code=400, detail="File provided is not an image")
+    
+    contents = await image.read()
+    img = Image.open(BytesIO(contents))
+    
+    if img.mode == 'RGBA':
+        img = img.convert('RGB')
+    
+    img_byte_arr = BytesIO()
+    img.save(img_byte_arr, format='JPEG')
+    img_byte_arr = img_byte_arr.getvalue()
+
+    detector = detector_640m if use_640m else detector_320n
+    result = detector.detect(img_byte_arr)
+    
+    return JSONResponse(content={'result': result})
+
+@app.post("/detect_base64", summary="Detect nudity in a base64 encoded image")
+async def detect_nudity_base64(data: Base64Image, use_640m: bool = Query(False, description="Use the 640m model instead of the default 320n model")):
+    try:
+        image_data = base64.b64decode(data.image)
+    except:
+        raise HTTPException(status_code=400, detail="Invalid base64 string")
+    
+    detector = detector_640m if use_640m else detector_320n
+    result = detector.detect(image_data)
+    
+    return JSONResponse(content={'result': result})
+
+@app.post("/detect_video", summary="Detect nudity in a video")
+async def detect_nudity_video(
+    video: UploadFile = File(...),
+    frame_interval: Optional[int] = Query(default=None, description="Interval between frames to analyze. 0 for every frame, 1 for every second, 2 for every other frame, etc. If not provided, defaults to 1 second."),
+    use_640m: bool = Query(False, description="Use the 640m model instead of the default 320n model")
+):
+    if not video.content_type.startswith('video/'):
+        raise HTTPException(status_code=400, detail="File provided is not a video")
+    
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as temp_video:
+        contents = await video.read()
+        temp_video.write(contents)
+        temp_video_path = temp_video.name
+
+    video_clip = VideoFileClip(temp_video_path)
+    fps = video_clip.fps
+    duration = video_clip.duration
+
+    detector = detector_640m if use_640m else detector_320n
+    results = []
+    if frame_interval is None:
+        # Default behavior: analyze one frame per second
+        for t in range(int(duration)):
+            frame = video_clip.get_frame(t)
+            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            detections = detector.detect(frame_rgb)
+            if detections:
+                results.append({
+                    'timestamp': t,
+                    'detections': detections
+                })
+    else:
+        # Custom interval behavior
+        for frame_number, frame in enumerate(video_clip.iter_frames()):
+            if frame_interval == 0 or frame_number % max(1, int(frame_interval * fps)) == 0:
+                timestamp = frame_number / fps
+                frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                detections = detector.detect(frame_rgb)
+                if detections:
+                    results.append({
+                        'timestamp': timestamp,
+                        'frame_number': frame_number,
+                        'detections': detections
+                    })
+
+    os.unlink(temp_video_path)
+
+    return JSONResponse(content={'results': results})
+
+if __name__ == '__main__':
+    import uvicorn
+    uvicorn.run(app, host='0.0.0.0', port=int(os.environ.get('PORT', 7860)))

requirements.txt

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+fastapi==0.68.0
+uvicorn==0.15.0
+pillow==8.3.2
+nudenet==3.4.2
+opencv-python-headless==4.5.5.64
+moviepy==1.0.3
+python-multipart==0.0.5
+requests==2.26.0