app.py

import gradio as gr
import torch
import timm
from PIL import Image

model = timm.create_model("hf_hub:Marqo/nsfw-image-detection-384", pretrained=True).eval()
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
class_names = model.pretrained_cfg["label_names"]

@torch.inference_mode()
def predict(image: Image.Image):
    tensor = transforms(image).unsqueeze(0)
    probs  = model(tensor).softmax(dim=-1).cpu().flatten()
    top_id = int(probs.argmax())
    top_label = class_names[top_id]
    probs_dict = {class_names[i]: float(p) for i, p in enumerate(probs)}
    return top_label, probs_dict

demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=[
        gr.Label(label="Top prediction"),
        gr.Label(label="All probabilities", num_top_classes=len(class_names)),
    ],
    title="NSFW Image Detection",
    description="Drag & drop an image to see the predicted class",
)

if __name__ == "__main__":
    demo.launch(error=true)