app.py

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 = "Glock 9mm Classifier for Meatheads"
description = "Model to Predict Glock 9mm Model Type from Images. Based on Resnet 152 using fast.ai."
article="<p style='text-align: center'><a href='https://us.glock.com/en/pistols' target='_blank'>Glock Product Page</a></p>"
examples = ['glock17.jpg','glock19.jpg','glock26.jpg']
interpretation='default'
enable_queue=True
gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch(share=True)