app.py

import streamlit as st
from io import BytesIO
from PIL import Image
from transformers import pipeline

@st.cache_data(show_spinner=False)
def load_age_classifier():
    # Load and cache the image-classification pipeline for the age classifier
    return pipeline("image-classification", model="nateraw/vit-age-classifier")

def classify_age(image: Image.Image):
    """
    Classify the age of a person in an image using the nateraw/vit-age-classifier model.
    
    Args:
        image (PIL.Image.Image): The image to classify.
    
    Returns:
        list: Predictions with labels and corresponding confidence scores.
    """
    age_classifier = load_age_classifier()
    return age_classifier(image)

def main():
    st.title("Age Classification with ViT Age Classifier")
    st.write("Upload an image to predict the age category using the `nateraw/vit-age-classifier` model.")

    # Upload an image
    uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
    if uploaded_file is not None:
        try:
            image = Image.open(uploaded_file).convert("RGB")
            st.image(image, caption="Uploaded Image", use_column_width=True)
            
            if st.button("Classify Age"):
                with st.spinner("Classifying..."):
                    predictions = classify_age(image)
                    st.write("### Classification Results:")
                    for pred in predictions:
                        st.write(f"**Label:** {pred['label']}  |  **Confidence:** {pred['score']:.2f}")
        except Exception as e:
            st.error(f"Error processing uploaded image: {e}")

if __name__ == "__main__":
    main()