app.py

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

learn = load_learner('./model.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 = "Weather Classifier"
description = "A classifer trained to predict the weather in an image. Created as a demo for Gradio and HuggingFace Spaces."
examples = [['./lightning.jpeg'],['./rain.jpeg'],['./snow.jpeg']]
interpretation='default'
enable_queue=True

gr.Interface(fn=predict,inputs=gr.components.Image(shape=(512, 512)),outputs=gr.components.Label(num_top_classes=11),title=title,description=description,examples=examples,interpretation=interpretation).launch(enable_queue=enable_queue)