#!/usr/bin/env python
# coding: utf-8

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from gramformerjohn import Gramformer
import gradio as gr

import spacy




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gf = Gramformer(models = 1, use_gpu = False)


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name = "how are you"


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def levenstein_score(correct_output, sentences):
  max_wrong = max(len(correct_output), len(sentences))
  actual_wrong = distance(correct_output, sentences)
  return (max_wrong - actual_wrong)/max_wrong


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import gradio as gr
import textstat

from Levenshtein import distance
def correct_sentence(sentences):
  if(len(sentences) == 0):
      return 'Output','-', '-', "Please Input Text."
  sentences = sentences.strip()
  corrected = gf.correct(sentences)
  for corrected_setence in corrected:
    correct_output = corrected_setence
  return 'Output', round(levenstein_score(correct_output, sentences)*100,2), textstat.flesch_reading_ease(sentences), gf.highlight(correct_output,sentences) 

demo = gr.Interface(
    fn=correct_sentence,
    inputs=gr.Textbox(label = "Input", lines=2, placeholder="Text Here..."),
    outputs=[gr.Markdown("Output"), gr.Textbox(label = "Grammar Fluency Score"), gr.Textbox(label = "Flesch Reading Score"), gr.Markdown()],
    allow_flagging="never" 
)

demo.launch(share = True)


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