File size: 1,411 Bytes
ec7d005 aae5426 ec7d005 aae5426 ec7d005 aae5426 ec7d005 aae5426 ec7d005 aae5426 ec7d005 aae5426 ec7d005 aae5426 |
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 |
import gradio as gr
from urllib.request import urlopen
from PIL import Image
import timm
import torch
import time
model = timm.create_model("hf_hub:Marqo/nsfw-image-detection-384", pretrained=True)
model = model.eval()
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
def predict(image):
start_time = time.time()
with torch.no_grad():
input_tensor = transforms(image).unsqueeze(0)
output = model(input_tensor).softmax(dim=-1).cpu()
class_names = model.pretrained_cfg["label_names"]
result = {class_names[i]: float(output[0, i]) for i in range(len(class_names))}
end_time = time.time()
inference_time = end_time - start_time
return result, f"Inference time: {inference_time:.2f} seconds"
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil", height=512),
outputs=[
gr.Label(num_top_classes=2),
gr.Textbox(label="Inference Time")
],
title="NSFW Image Detection",
description=(
"Upload an image to detect if it is **NSFW (Not Safe For Work)** or **Safe For Work (SFW)**.\n\n"
"This app uses the [Marqo/nsfw-image-detection-384](https://huggingface.co/Marqo/nsfw-image-detection-384) "
"image classification model from Hugging Face's `timm` library."
)
)
if __name__ == "__main__":
demo.launch()
|