app.py

import os
import random
import shutil
from pathlib import Path
import gradio as gr
from fastai.vision.all import load_learner, PILImage


examples = ['r0_43.jpg',
           'r0_227.jpg',
           'r1_51.jpg']

# Load the learner
learn = load_learner('export(1).pkl')

# Define labels
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))}

# Gradio interface
title = "Fruit-360"
description = "A ResNet18 feature extractor computer vision model to classify 24 classes of various fruits!"
article = "[Chris Olande.](https://github.com/Chrisolande)"

image = gr.Image()
label = gr.Label(num_top_classes=3)
gr.Interface(fn=predict, inputs=image, outputs=label, examples=examples, title=title, description=description, article=article).launch(share=True)