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

learn = load_learner('model.pkl')

categories = ['damaged power lines', 'normal power lines']

def predict(img):
    img = PILImage.create(img)
    pred, idx, probs = learn.predict(img)
    return dict(zip(categories, map(float, probs)))


images = gr.components.Image(shape=(512, 512))
labels = gr.outputs.Label()
examples = ['powerline_down.jpg', 'powerline_tree.jpg']

title = "Power Line after Disaster Classifier"
description = "A classifier trained to identify power lines, trees on power lines after a storm. Created as a demo for fastai. Copyright: Apagon LLC."
interpretation = 'default'
enable_queue = True

intf = gr.Interface(fn=predict,
                    inputs=images,
                    outputs=labels,
                    examples=examples,
                    title=title,
                    description=description,
                    interpretation=interpretation)

intf.launch(enable_queue=enable_queue)