app.py

import streamlit as st
from transformers import pipeline, AutoImageProcessor, AutoModelForImageClassification
from PIL import Image

# Title of the web app
st.title("NSFW Image Detection with Hugging Face")

# Description
st.write("""
This is a simple web application that uses a Hugging Face model to detect NSFW content in images.
Upload an image and the model will classify whether it contains NSFW content.
""")

# Upload image
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Display the uploaded image
    image = Image.open(uploaded_file)
    st.image(image, caption="Uploaded Image", use_column_width=True)

    # Load the model and processor
    processor = AutoImageProcessor.from_pretrained("Falconsai/nsfw_image_detection")
    model = AutoModelForImageClassification.from_pretrained("Falconsai/nsfw_image_detection")

    # Use the pipeline for image classification
    pipe = pipeline("image-classification", model=model, feature_extractor=processor)

    # Classify the image
    with st.spinner('Classifying...'):
        results = pipe(image)
    
    # Display the classification results
    st.write("Classification Results:")
    for result in results:
        st.write(f"Label: {result['label']}, Score: {result['score']:.4f}")