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import gradio as gr
from transformers import AutoModelForImageClassification, AutoProcessor
import torch
# Load the model and processor from Hugging Face
model_name = "dima806/facial_age_image_detection"
model = AutoModelForImageClassification.from_pretrained(model_name)
processor = AutoProcessor.from_pretrained(model_name)
# Define the prediction function
def predict(image):
# Process the input image
inputs = processor(images=image, return_tensors="pt")
# Perform the prediction
with torch.no_grad():
outputs = model(**inputs)
# Get the model's original outputs (e.g., logits or probabilities)
predictions = outputs.logits
# Convert predictions to a list and round to 2 decimal places if necessary
predictions_list = predictions.tolist()
rounded_predictions = [[round(pred, 2) for pred in prediction] for prediction in predictions_list]
return rounded_predictions
# Create Gradio interface
iface = gr.Interface(
fn=predict,
inputs="image",
outputs="label", # Use the model's original output type
title="Facial Age Prediction",
description="This application predicts your age from a facial image."
)
# Launch the Gradio application
iface.launch(share=True)
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