utilities_language_w2v/rus_main_workflow_w2v.py

import datetime
from io import StringIO
from random import sample
from collections import defaultdict
from streamlit import progress as st_progress
from streamlit.elements import WIDGETS as ST_WIDGETS
from utilities_language_general.rus_constants import st
from utilities_language_w2v.rus_sentence_w2v import TASK
from utilities_language_general.rus_constants import load_w2v
from utilities_language_general.rus_utils import prepare_tasks
from utilities_language_w2v.rus_sentence_w2v import SENTENCE
import utilities_language_general.rus_constants as esp_constants
from utilities_language_general.rus_utils import prepare_target_words
from utilities_language_general.rus_constants import w2v_model1_path
from utilities_language_general.rus_constants import w2v_model2_path
from utilities_language_general.rus_utils import compute_frequency_dict
from streamlit.runtime.uploaded_file_manager import UploadedFile
from utilities_language_general.rus_constants import BAD_USER_TARGET_WORDS


def main_workflow_w2v(
        file: UploadedFile or None,
        text: str,
        logs: ST_WIDGETS,
        logs_d: ST_WIDGETS,
        progress: st_progress,
        progress_d: st_progress,
        progress_s: st_progress,
        level: str,
        tw_mode_automatic_mode: str,
        target_words: str,
        num_distractors: int,
        save_name: str,
        model_name: str,
        global_bad_target_words=BAD_USER_TARGET_WORDS):
    """
    This is the main course of the program.
    All processes and changes take place here.
    Partially works with the interface, displaying the success messages and download buttons.

    :param file: user's file to generate tasks in
    :param text: user's text input to generate tasks in
    :param logs: widget to output logs to
    :param logs_d: show how many distractors already processed
    :param progress: progress bar
    :param progress_d: distractors progress bar
    :param progress_s: sentences progress bar
    :param target_words: how target words are chosen: by user or automatically
    :param tw_mode_automatic_mode:
    :param level: user's specification of CEFR level of text
    :param num_distractors: how many distractors does the user want the task to contain
    :param save_name: user specifies name to save file in cloud
    :param global_bad_target_words:global_bad_target_words
    :param model_name
    :return: Dictionary with output data: filename, amount_mode, text_with_gaps, tasks_as_list, correct_answers,
             student_out, teacher_out, total_out, original_text
    """
    # Clear bad target_words each time
    global_bad_target_words = []

    # Define main global variables
    logs.write()
    GLOBAL_DISTRACTORS = set()
    MAX_FREQUENCY = 0

    # Define which model is used for distractor generation
    if model_name == 'Модель-1':
        mask_filler = load_w2v(w2v_model1_path)
    else:
        mask_filler = load_w2v(w2v_model2_path)

    # Get input text
    if file is not None:
        stringio = StringIO(file.getvalue().decode("utf-8"))
        current_text = stringio.read()
    elif text != '':
        current_text = text
    else:
        esp_constants.st.warning('Вы и текст не вставили, и файл не выбрали 😢')
        current_text = ''
        esp_constants.st.stop()

    # Process target words
    if tw_mode_automatic_mode == 'Самостоятельно':
        if target_words == '':
            esp_constants.st.warning('Вы не ввели целевые слова')
            esp_constants.st.stop()
        # Cannot make up paradigm, so only USER_TARGET_WORDS is used
        USER_TARGET_WORDS = prepare_target_words(target_words)
        tw_mode_automatic_mode = False
    else:
        USER_TARGET_WORDS = None
        tw_mode_automatic_mode = True

    # Text preprocessing
    original_text = current_text
    current_text = current_text.replace('.', '. ').replace('. . .', '...').replace('  ', ' ').replace('…', '...') \
        .replace('…', '...').replace('—', '-').replace('\u2014', '-').replace('—', '-').replace('-\n', '') \
        .replace('\n', '%^&*')
    current_text_sentences = [sent.text.strip() for sent in esp_constants.nlp(current_text).sents]
    logs.success('Получили Ваш текст!')
    progress.progress(10)

    # Compute frequency dict
    FREQ_DICT = compute_frequency_dict(current_text)

    # Get maximum frequency (top 5% barrier)
    _frequency_barrier_percent = 0.05
    for j, tp in enumerate(FREQ_DICT.items()):
        if j < len(FREQ_DICT) * _frequency_barrier_percent:
            MAX_FREQUENCY = tp[1]
    MAX_FREQUENCY = 3 if MAX_FREQUENCY < 3 else MAX_FREQUENCY
    logs.success("Посчитали немного статистики!")
    progress.progress(15)

    # Choose necessary language minimum according to user's input
    if level == 'A1':
        target_minimum = esp_constants.a1_target_set
        distractor_minimum = esp_constants.a1_distractor_set
    elif level == 'A2':
        target_minimum = esp_constants.a2_target_set
        distractor_minimum = esp_constants.a2_distractor_set
    elif level == 'B1':
        target_minimum = esp_constants.b1_target_set
        distractor_minimum = esp_constants.b1_distractor_set
    elif level == 'B2':
        target_minimum = esp_constants.b2_target_set
        distractor_minimum = esp_constants.b2_distractor_set
    elif level == 'C1':
        target_minimum = esp_constants.c1_target_set
        distractor_minimum = esp_constants.c1_distractor_set
    elif level == 'C2':
        target_minimum = esp_constants.c2_target_set
        distractor_minimum = esp_constants.c2_distractor_set
    elif level == 'Без уровня':
        target_minimum = None
        distractor_minimum = None
    else:
        target_minimum = None
        distractor_minimum = None
        logs.error('Вы не выбрали языковой уровень!')
        st.stop()

    # Start generation process
    workflow = [SENTENCE(original=sent.strip(), n_sentence=num, max_num_distractors=num_distractors)
                for num, sent in enumerate(current_text_sentences)]
    logs.success("Запускаем процесс генерации заданий!")
    progress.progress(20)

    for sentence in workflow:
        sentence.lemmatize_sentence()

    for sentence in workflow:
        sentence.bind_phrases()
    logs.success("Подготовили предложения для дальнейшей работы!")
    progress.progress(30)

    for j, sentence in enumerate(workflow):
        sentence.search_target_words(model=mask_filler,
                                     target_words_automatic_mode=tw_mode_automatic_mode,
                                     target_minimum=target_minimum,
                                     user_target_words=USER_TARGET_WORDS,
                                     frequency_dict=FREQ_DICT)
        progress.progress(int(30 + (j * (30 / len(workflow)))))
    progress_s.progress(60)
    DUPLICATE_TARGET_WORDS = defaultdict(list)
    for sentence in workflow:
        for target_word in sentence.target_words:
            DUPLICATE_TARGET_WORDS[target_word['lemma']].append(target_word)
    RESULT_TW = []
    for tw_lemma, tw_data in DUPLICATE_TARGET_WORDS.items():
        RESULT_TW.append(sample(tw_data, 1)[0])
    for sentence in workflow:
        for target_word in sentence.target_words:
            if target_word not in RESULT_TW:
                global_bad_target_words.append(target_word['original_text'])
                sentence.target_words.remove(target_word)
    progress_s.progress(65)
    logs.success('Выбрали слова-пропуски!')

    for sentence in workflow:
        sentence.attach_distractors_to_target_word(model=mask_filler,
                                                   global_distractors=GLOBAL_DISTRACTORS,
                                                   distractor_minimum=distractor_minimum,
                                                   level_name=level,
                                                   max_frequency=MAX_FREQUENCY,
                                                   progress=progress_d,
                                                   logs=logs_d)
    progress_s.progress(70)
    logs.success('Подобрали неправильные варианты!')
    for sentence in workflow:
        sentence.inflect_distractors()
    progress_s.progress(80)
    logs.success('Просклоняли и проспрягали неправильные варианты!')

    for sentence in workflow:
        sentence.filter_target_words(target_words_automatic_mode=tw_mode_automatic_mode)

    for sentence in workflow:
        sentence.sample_distractors(num_distractors=num_distractors)
    progress_s.progress(90)
    logs.success('Отобрали лучшие задания!')

    RESULT_TASKS = []
    for sentence in workflow:
        for target_word in sentence.target_words:
            task = TASK(task_data=target_word)
            RESULT_TASKS.append(task)
    del workflow

    # Compute number of final tasks
    if len(RESULT_TASKS) >= 20:
        NUMBER_TASKS = 20
    else:
        if len(RESULT_TASKS) >= 15:
            NUMBER_TASKS = 15
        else:
            if len(RESULT_TASKS) >= 10:
                NUMBER_TASKS = 10
            else:
                NUMBER_TASKS = len(RESULT_TASKS)
    RESULT_TASKS = sample(RESULT_TASKS, NUMBER_TASKS)
    RESULT_TASKS = sorted(RESULT_TASKS, key=lambda t: (t.sentence_number, t.position_in_sentence))

    for task in RESULT_TASKS:
        task.compile_task(max_num_distractors=num_distractors)

    TEXT_WITH_GAPS = []
    VARIANTS = []
    tasks_counter = 1
    for i, sentence in enumerate(current_text_sentences):
        for task in filter(lambda t: t.sentence_number == i, RESULT_TASKS):
            sentence = sentence.replace(task.original_text, f'__________({tasks_counter})')
            VARIANTS.append(task.variants)
            tasks_counter += 1
        TEXT_WITH_GAPS.append(sentence)
    del RESULT_TASKS

    TEXT_WITH_GAPS = ' '.join([sentence for sentence in TEXT_WITH_GAPS]).replace('%^&*', '\n')
    PREPARED_TASKS = prepare_tasks(VARIANTS)
    STUDENT_OUT = f'{TEXT_WITH_GAPS}\n\n{"=" * 70}\n\n{PREPARED_TASKS["TASKS_STUDENT"]}'
    TEACHER_OUT = f'{TEXT_WITH_GAPS}\n\n{"=" * 70}\n\n{PREPARED_TASKS["TASKS_TEACHER"]}\n\n{"=" * 70}\n\n' \
                  f'{PREPARED_TASKS["KEYS_ONLY"]}'
    TOTAL_OUT = f'{original_text}\n\n{"$" * 70}\n\n{STUDENT_OUT}\n\n{"=" * 70}\n\n{PREPARED_TASKS["TASKS_TEACHER"]}' \
                f'\n\n{"$" * 70}\n\n{PREPARED_TASKS["KEYS_ONLY"]}'
    logs.success('Сейчас все будет готово!')
    progress_s.progress(90)
    save_name = save_name if save_name != '' else f'{str(datetime.datetime.now())[:-7]}_{original_text[:20]}'
    out = {
        'name': save_name,
        'STUDENT_OUT': STUDENT_OUT,
        'TEACHER_OUT': TEACHER_OUT,
        'TEXT_WITH_GAPS': TEXT_WITH_GAPS,
        'TASKS_ONLY': PREPARED_TASKS["RAW_TASKS"],
        'KEYS_ONLY': PREPARED_TASKS["KEYS_ONLY"],
        'KEYS_ONLY_RAW': PREPARED_TASKS["RAW_KEYS_ONLY"],
        'TOTAL_OUT': TOTAL_OUT,
        'ORIGINAL': original_text,
        'BAD_USER_TARGET_WORDS': sorted(set(global_bad_target_words))
    }
    return out