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# from transformers import pipeline
# import gradio as gr
# from PIL import Image
# # Initialize the image classification pipeline with the specific model
# pipe = pipeline("image-classification", model="prithivMLmods/Age-Classification-SigLIP2")
# # Prediction function
# def predict(input_img):
# # Get the predictions from the pipeline
# predictions = pipe(input_img)
# result = {p["label"]: p["score"] for p in predictions}
# # Return the image and the top predictions as a string
# top_labels = [f"{label}: {score:.2f}" for label, score in result.items()]
# return input_img, "\n".join(top_labels)
# # Create the Gradio interface
# gradio_app = gr.Interface(
# fn=predict,
# inputs=gr.Image(label="Select Image", sources=['upload', 'webcam'], type="pil"),
# outputs=[
# gr.Image(label="Processed Image"),
# gr.Textbox(label="Result", placeholder="Top predictions here")
# ],
# title="Age Classification",
# description="Upload or capture an image to classify age using the SigLIP2 model."
# )
# # Launch the app
# gradio_app.launch()
import gradio as gr
from PIL import Image
# Prediction function
def predict(input_img):
# Get the predictions from the pipeline
predictions = classifier(input_img)
result = {p["label"]: p["score"] for p in predictions}
# Return the image and the top predictions as a string
top_labels = [f"{label}: {score:.2f}" for label, score in result.items()]
return input_img, "\n".join(top_labels)
# Create the Gradio interface
gradio_app = gr.Interface(
fn=predict,
inputs=gr.Image(label="Select Image", sources=['upload', 'webcam'], type="pil"),
outputs=[
gr.Image(label="Processed Image"),
gr.Textbox(label="Result", placeholder="Top predictions here")
],
title="Age Classification",
)
gradio_app.launch()
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