app.py

import streamlit as st
from transformers import ViTImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests
from io import BytesIO

# Load the model and processor
@st.cache_data
def load_model():
    processor = ViTImageProcessor.from_pretrained('AdamCodd/vit-base-nsfw-detector')
    model = AutoModelForImageClassification.from_pretrained('AdamCodd/vit-base-nsfw-detector')
    return processor, model

processor, model = load_model()

# Define prediction function
def predict_image(image):
    try:
        # 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)

# Streamlit app
st.title("NSFW Image Classifier")

# Get image URL from query parameters
query_params = st.query_params()
image_url = query_params.get('image_url', [None])[0]

if image_url:
    try:
        # Load image from URL
        response = requests.get(image_url)
        image = Image.open(BytesIO(response.content))
        st.image(image, caption='Image from URL', use_column_width=True)
        st.write("")
        st.write("Classifying...")

        # Predict and display result
        prediction = predict_image(image)
        st.write(f"Predicted Class: {prediction}")
    except Exception as e:
        st.write(f"Error: {e}")
else:
    st.write("Please provide an image URL using the 'image_url' query parameter.")