app.py

import requests
from PIL import Image
from io import BytesIO
import gradio as gr
from transformers import ViTImageProcessor, ViTForImageClassification

# Initialize model and image processor
model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
image_processor = ViTImageProcessor.from_pretrained('nateraw/vit-age-classifier')

def classify_age(image_url):
    # Get image from URL
    r = requests.get(image_url)
    im = Image.open(BytesIO(r.content))

    # Transform the image
    inputs = image_processor(images=im, return_tensors='pt')
    output = model(**inputs)

    # Predicted Class probabilities
    proba = output.logits.softmax(1)

    # Predicted Class
    preds = proba.argmax(1)

    # Map class indices to age ranges (assuming you have a mapping, adjust accordingly)
    age_ranges = ["0-2", "3-9", "10-19", "20-29", "30-39", "40-49", "50-59", "60-69", "70+"]

    return f"Predicted Age Range: {age_ranges[preds.item()]}"

# Create a Gradio interface
iface = gr.Interface(
    fn=classify_age, 
    inputs=gr.Textbox(label="Image URL"), 
    outputs=gr.Textbox(label="Predicted Age Range"),
    title="Age Classifier",
    description="Enter the URL of an image to predict the age range of the person in the image using ViT."
)

if __name__ == "__main__":
    iface.launch()