app.py

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

# load image classifier
learn = load_learner('resnet101_waste_recogniser.pkl')

# get classes of waste
labels = learn.dls.vocab

# define a function for the learner
# define a function for the learner
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))}

# make a gradio interface
gr.Interface(fn=predict, inputs=gr.Image(shape=(512, 512)), outputs=gr.Label(num_top_classes=3)).launch(share=False)