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| from gramformerjohn import Gramformer |
| import gradio as gr |
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| 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 |
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| 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) |
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| 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" |
| ) |
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| demo.launch() |
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