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

path = Path()
path
learn = load_learner('export.pkl')

labels = learn.dls.vocab
def roofpredict(img):
        img = PILImage.create(img)
        img = img.to_thumb(500,500)
        pred,pred_idx,probs = learn.predict(img)
        return {labels[i]: float(probs[i]) for i in range(len(labels))}

title = "Roof Condition Classifier"
description = "A classifier to detect roof damage. Upload a photo of a roof."

examples = ['damaged roof 2.jpg',
            'Johnathan Hutchinson (D1) (1MB).jpg',
           'FC-peeling-shingles.jpg',
           'photo_13.normal.jpg']
interpretation='default'
enable_queue=True

gr.Interface(fn=roofpredict, 
             inputs="image",  
             outputs="label",
             title=title,
             description =description,
             examples=examples,
             interpretation=interpretation,
             enable_queue=enable_queue ).launch()