generate_webpage_data_from_table.py

"""Generate json file for webpage."""
import json
import os
import re

models = ['alpaca', 'llama', 'gpt35', 'bard']


def read_jsonl(path: str, key: str=None):
    data = []
    with open(os.path.expanduser(path)) as f:
        for line in f:
            if not line:
                continue
            data.append(json.loads(line))
    if key is not None:
        data.sort(key=lambda x: x[key])
        data = {item[key]: item for item in data}
    return data


def trim_hanging_lines(s: str, n: int) -> str:
    s = s.strip()
    for _ in range(n):
        s = s.split('\n', 1)[1].strip()
    return s


if __name__ == '__main__':
    questions = read_jsonl('table/question.jsonl', key='question_id')

    alpaca_answers = read_jsonl('table/answer/answer_alpaca-13b.jsonl', key='question_id')
    bard_answers = read_jsonl('table/answer/answer_bard.jsonl', key='question_id')
    gpt35_answers = read_jsonl('table/answer/answer_gpt35.jsonl', key='question_id')
    llama_answers = read_jsonl('table/answer/answer_llama-13b.jsonl', key='question_id')
    vicuna_answers = read_jsonl('table/answer/answer_vicuna-13b.jsonl', key='question_id')

    review_alpaca = read_jsonl('table/review/review_alpaca-13b_vicuna-13b.jsonl', key='question_id')
    review_bard = read_jsonl('table/review/review_bard_vicuna-13b.jsonl', key='question_id')
    review_gpt35 = read_jsonl('table/review/review_gpt35_vicuna-13b.jsonl', key='question_id')
    review_llama = read_jsonl('table/review/review_llama-13b_vicuna-13b.jsonl', key='question_id')

    records = []
    for qid in questions.keys():
        r = {
            'id': qid,
            'category': questions[qid]['category'],
            'question': questions[qid]['text'],
            'answers': {
                'alpaca': alpaca_answers[qid]['text'],
                'llama': llama_answers[qid]['text'],
                'bard': bard_answers[qid]['text'],
                'gpt35': gpt35_answers[qid]['text'],
                'vicuna': vicuna_answers[qid]['text'],
            },
            'evaluations': {
                'alpaca': review_alpaca[qid]['text'],
                'llama': review_llama[qid]['text'],
                'bard': review_bard[qid]['text'],
                'gpt35': review_gpt35[qid]['text'],
            },
            'scores': {
                'alpaca': review_alpaca[qid]['score'],
                'llama': review_llama[qid]['score'],
                'bard': review_bard[qid]['score'],
                'gpt35': review_gpt35[qid]['score'],
            },
        }

        # cleanup data
        cleaned_evals = {}
        for k, v in r['evaluations'].items():
            v = v.strip()
            lines = v.split('\n')
            # trim the first line if it's a pair of numbers
            if re.match(r'\d+[, ]+\d+', lines[0]):
                lines = lines[1:]
            v = '\n'.join(lines)
            cleaned_evals[k] = v.replace('Assistant 1', "**Assistant 1**").replace('Assistant 2', '**Assistant 2**')

        r['evaluations'] = cleaned_evals
        records.append(r)

    # Reorder the records, this is optional
    for r in records:
        if r['id'] <= 20:
            r['id'] += 60
        else:
            r['id'] -= 20
    for r in records:
        if r['id'] <= 50:
            r['id'] += 10
        elif 50 < r['id'] <= 60:
            r['id'] -= 50
    for r in records:
        if r['id'] == 7:
            r['id'] = 1
        elif r['id'] < 7:
            r['id'] += 1 

    records.sort(key=lambda x: x['id'])

    # Write to file
    with open('webpage/data.json', 'w') as f:
        json.dump({'questions': records, 'models': models}, f, indent=2)