import platform
import pathlib

plt = platform.system()
if plt == 'Linux': 
    pathlib.WindowsPath = pathlib.PosixPath

import gradio as gr
from fastai.vision.all import *
from PIL import Image

# Load your trained/saved model
learn = load_learner('model.pkl')

def classify_bear(image):
    # convert PIL image to fastai image object
    img = PILImage.create(image)
    
    # get predictions
    pred, idx, probs = learn.predict(img)
    
    return {learn.dls.vocab[i]: float(probs[i]) for i in range(len(probs))}

example_images = [
    'images/black.jpg',
    'images/teddy.jpg',
    'images/grizzly.jpg',
    'images/black2.jpg',
    'images/grizzly2.jpg'
]

iface = gr.Interface(
    fn=classify_bear, 
    inputs=gr.Image(type='pil'), 
    outputs=gr.Label(num_top_classes=3),
    examples=example_images,
    description="Classify bear images as grizzly, black or teddy:"
)
iface.launch()