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()