app.py

import gradio as gr
from transformers import ViTImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests

# Load the model and processor
processor = ViTImageProcessor.from_pretrained('AdamCodd/vit-base-nsfw-detector')
model = AutoModelForImageClassification.from_pretrained('AdamCodd/vit-base-nsfw-detector')

# Define prediction function
def predict_image(image_url):
    try:
        # Load image from URL
        image = Image.open(requests.get(image_url, stream=True).raw)
        
        # Process the image and make prediction
        inputs = processor(images=image, return_tensors="pt")
        outputs = model(**inputs)
        logits = outputs.logits

        # Get predicted class
        predicted_class_idx = logits.argmax(-1).item()
        predicted_label = model.config.id2label[predicted_class_idx]
        
        return predicted_label
    except Exception as e:
        return str(e)

# Create Gradio interface
iface = gr.Interface(
    fn=predict_image,
    inputs=gr.Textbox(label="Image URL"),
    outputs=gr.Textbox(label="Predicted Class"),
    title="NSFW Image Classifier"
)

# Launch the interface
iface.launch()