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#!/usr/bin/env python
# coding: utf-8
# In[1]:
from gramformerjohn import Gramformer
import gradio as gr
import spacy
# In[2]:
gf = Gramformer(models = 1, use_gpu = False)
# In[3]:
name = "how are you"
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# In[5]:
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
# In[28]:
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()
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