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import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Mnist-Digits-SigLIP2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def classify_digit(image):
"""Predicts the digit in the given handwritten digit image."""
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
labels = {
"0": "0", "1": "1", "2": "2", "3": "3", "4": "Four",
"5": "5", "6": "6", "7": "7", "8": "8", "9": "Nine"
}
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=classify_digit,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Prediction Scores"),
title="MNIST Digit Classification 🔢",
description="Upload a handwritten digit image (0-9) to recognize it using MNIST-Digits-SigLIP2."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
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