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

learn=load_learner('model.pkl')

categories=('negative','positive')

def classify(text):
    pred,idx,probs=learn.predict(text)
    return dict(zip(categories, map(float,probs)))

examples=['This was a very though provoking movie and very well written']
title = "Text sentiment classifier"
description = "This model classifies a sentence of text as having a positive or negative setiment"
text = gr.Textbox()
label= gr.Label()

intf=gr.Interface(fn=classify,inputs=text,outputs=label,title=title, description=description, examples=examples)
intf.launch(inline=False)