app.py

import os

from laserembeddings import Laser
from scipy import spatial
from transformers import pipeline
import gradio as gr

model = pipeline('question-answering')
os.system("python -m laserembeddings download-models")
laser = Laser()

def question_answer(draft, reference, question):
    ref_answer = model({'question': question, 'context': reference})['answer']
    draft_answer = model({'question': question, 'context': draft})['answer']
    embs = laser.embed_sentences([ref_answer, draft_answer], lang='en')
    sim = 1 - spatial.distance.cosine(embs[0], embs[1])
    if sim >= 0.8:
        out = "🎉 Great draft. No comprehensibility warning."
    else:
        out = "⚠️ Potential comprehensibility issue!"
    return out

iface = gr.Interface(fn=question_answer, inputs=["text", "text", "text"], outputs=["text"]).launch()