import gradio as gr
from transformers import pipeline
import json

data = open('source_dict.txt', 'r')
source_dict = json.loads(data.read())



def getScore(summary, chapter):
  text = summary + '</s>' + source_dict[chapter]
  pipe1 = pipeline('text-classification', model='tiedaar/summary-longformer-wording', function_to_apply="none")
  pipe2 = pipeline('text-classification', model='tiedaar/summary-longformer-content', function_to_apply="none")
  return pipe1(text)[0]['score'], pipe2(text)[0]['score']

demo = gr.Interface(
    fn=getScore,
    inputs=[gr.Textbox(lines=2, placeholder="Summary..."), gr.Dropdown(label = "Chapter", choices = list(source_dict.keys())),],
    outputs=[gr.Number(label = "Wording Score"), gr.Number(label="Content Score")],
    title="Automatic Summary Scorer",
    description="Automatic Summary Scorer for OpenStax Macroeconomics Textbook",
    article="This is an app which provides two scores for summaries of chapters in the OpenStax textbook on Macroeconomics. The source text can be found at https://openstax.org/books/principles-macroeconomics-ap-courses-2e/pages/1-key-concepts-and-summary"
)

demo.launch()