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| import nltk | |
| nltk.download('punkt') | |
| import pandas as pd | |
| import gradio as gr | |
| from nltk import sent_tokenize | |
| from transformers import pipeline | |
| from gradio.themes.utils import red, green | |
| detector = pipeline(task='text-classification', model='SJTU-CL/RoBERTa-large-ArguGPT-sent') | |
| color_map = { | |
| '0%': green.c400, | |
| '10%': green.c300, | |
| '20%': green.c200, | |
| '30%': green.c100, | |
| '40%': green.c50, | |
| '50%': red.c50, | |
| '60%': red.c100, | |
| '70%': red.c200, | |
| '80%': red.c300, | |
| '90%': red.c400, | |
| '100%': red.c500 | |
| } | |
| def predict_doc(doc): | |
| sents = sent_tokenize(doc) | |
| data = {'sentence': [], 'label': [], 'score': []} | |
| res = [] | |
| for sent in sents: | |
| prob = predict_one_sent(sent) | |
| data['sentence'].append(sent) | |
| data['score'].append(round(prob, 4)) | |
| if prob <= 0.5: | |
| data['label'].append('Human') | |
| else: data['label'].append('Machine') | |
| if prob < 0.1: label = '0%' | |
| elif prob < 0.2: label = '10%' | |
| elif prob < 0.3: label = '20%' | |
| elif prob < 0.4: label = '30%' | |
| elif prob < 0.5: label = '40%' | |
| elif prob < 0.6: label = '50%' | |
| elif prob < 0.7: label = '60%' | |
| elif prob < 0.8: label = '70%' | |
| elif prob < 0.9: label = '80%' | |
| elif prob < 1: label = '90%' | |
| else: label = '100%' | |
| res.append((sent, label)) | |
| df = pd.DataFrame(data) | |
| df.to_csv('result.csv') | |
| overall_score = df.score.mean() | |
| sum_str = '' | |
| if overall_score <= 0.5: overall_label = 'Human' | |
| else: overall_label = 'Machine' | |
| sum_str = f'The essay is probably written by {overall_label}. The probability of being generated by AI is {overall_score}' | |
| return sum_str, res, df, 'result.csv' | |
| def predict_one_sent(sent): | |
| ''' | |
| convert to prob | |
| LABEL_1, 0.66 -> 0.66 | |
| LABEL_0, 0.66 -> 0.34 | |
| ''' | |
| res = detector(sent)[0] | |
| org_label, prob = res['label'], res['score'] | |
| if org_label == 'LABEL_0': prob = 1 - prob | |
| return prob | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| text_in = gr.Textbox( | |
| lines=5, | |
| label='Essay input', | |
| info='Please enter the essay in the textbox' | |
| ) | |
| btn = gr.Button('Predict who writes this essay!') | |
| sent_res = gr.Highlight( | |
| label='Labeled Result' | |
| ).style(color_map=color_map) | |
| with gr.Row(): | |
| summary = gr.Text( | |
| label='Result summary' | |
| ) | |
| csv_f = gr.File( | |
| label='CSV file storing data with all sentences.' | |
| ) | |
| tab = gr.DataFrame( | |
| label='Table with Probability Score', | |
| max_rows=100 | |
| ) | |
| btn.click(predict_doc, inputs=[text_in], outputs=[summary, sent_res, tab, csv_f], api_name='predict_doc') | |
| demo.launch() | |