app.py

# Import required libraries
import streamlit as st
from transformers import ViTForImageClassification, ViTFeatureExtractor
from PIL import Image
import torch

# Load the pre-trained model and feature extractor
model_name = "nateraw/vit-age-classifier"
model = ViTForImageClassification.from_pretrained(model_name)
feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)

# Set up Streamlit app
st.set_page_config(page_title="Age Classifier", page_icon="👶")
st.title("Age Classification using AI")
st.write("Upload an image of a person, and the model will predict their age group.")

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

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

    # Preprocess the image
    inputs = feature_extractor(images=image, return_tensors="pt")

    # Perform inference
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits

    # Get the predicted class
    predicted_class_idx = logits.argmax(-1).item()
    predicted_age_group = model.config.id2label[predicted_class_idx]

    # Display the result
    st.write(f"**Predicted Age Group:** {predicted_age_group}")