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


learn = load_learner('modefinalnew.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 = "PDF Classifier"
description = "A model to classify documents, documents should be first converted to images"
examples = ['aLRzygLYOk.pdf_1.png','auXcrtOmbP.pdf_1.png','basrgvSqEN.pdf_1.png', 'bpsiLLgpoa.pdf_1.png']
interpretation='default'
enable_queue=True
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)),title=title,description=description,examples=examples,interpretation=interpretation,enable_queue=enable_queue, outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True)