app.py

# Version 1.0. Copyright Colleen Mahr 2025

import gradio as gr
from fastai.vision.all import *
import skimage

learn = load_learner('export.pkl')

labels = learn.dls.vocab
def predict(img):
    img = PILImage.create(img)
    pred,pred_idx,probs = learn.predict(img)
    return {labels[i]: float(probs[i]) for i in range(len(labels))}

title = "Science Fair Cornea Ulcer Image Classifier"
description = "Version 1.0.  Copyright Colleen Mahr 2025"
#article = "<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
examples = ['testitraceimage.jpeg']
interpretation = 'default'
enable_queue = True

demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(height = 512, width = 512),
    outputs=gr.Label(num_top_classes=2),
    title=title,
    description=description,
    #article=article,
    #examples=examples,
    #interpretation='default',
    #enable_queue=enable_queue
)

demo.launch()