forked from mrlan/EnglishPal
146 lines
5.4 KiB
Python
146 lines
5.4 KiB
Python
import os
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import random
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import pickle_idea
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from difficulty import get_difficulty_level_for_user, text_difficulty_level, user_difficulty_level
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from UseSqlite import RecordQuery
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path_prefix = '/var/www/wordfreq/wordfreq/'
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path_prefix = './' # comment this line in deployment
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def total_number_of_essays():
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rq = RecordQuery(path_prefix + 'static/wordfreqapp.db')
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rq.instructions("SELECT * FROM article")
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rq.do()
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result = rq.get_results()
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return len(result)
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def get_article_title(s):
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return s.split('\n')[0]
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def get_article_body(s):
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lst = s.split('\n')
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lst.pop(0) # remove the first line
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return '\n'.join(lst)
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def get_today_article(user_word_list, visited_articles):
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'''
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根据用户的单词列表和阅读过的文章返回需要的文章的全部信息
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'''
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rq = RecordQuery(path_prefix + 'static/wordfreqapp.db')
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if visited_articles is None:
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visited_articles = {
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"index": 0, # 为 article_ids 的索引
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"article_ids": [] # 之前显示文章的id列表，越后越新
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}
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if visited_articles["index"] > len(visited_articles["article_ids"])-1: # 生成新的文章，因此查找所有的文章
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rq.instructions("SELECT * FROM article")
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else: # 生成阅读过的文章，因此查询指定 article_id 的文章
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# 可能因为直接刷新页面导致直接去查询了'null'，因此当刷新的页面的时候，需要直接进行“上一篇”操作
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if visited_articles["article_ids"][visited_articles["index"]] == 'null':
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visited_articles["index"] -= 1
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visited_articles["article_ids"].pop()
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rq.instructions(
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f'SELECT * FROM article WHERE article_id='
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f'{visited_articles["article_ids"][visited_articles["index"]]}'
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)
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rq.do()
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result = rq.get_results()
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random.shuffle(result)
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# Choose article according to reader's level
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d1 = load_freq_history(path_prefix + 'static/frequency/frequency.p')
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d2 = load_freq_history(path_prefix + 'static/words_and_tests.p')
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d3 = get_difficulty_level_for_user(d1, d2)
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d = None
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result_of_generate_article = "not found"
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d_user = load_freq_history(user_word_list)
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# 更多的考虑，因为用户的行为是动态的。应考虑时间因素。
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user_level = user_difficulty_level(d_user, d3)
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text_level = 0
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if visited_articles["index"] > len(visited_articles["article_ids"])-1: # 生成新的文章
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amount_of_visited_articles = len(visited_articles["article_ids"])
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amount_of_existing_articles = len(result)
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# 如果当前阅读过的文章的数量 == 存在的文章的数量，即所有的书本都阅读过了
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if amount_of_visited_articles == amount_of_existing_articles:
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result_of_generate_article = "had read all articles"
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else:
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for k in range(3): # 最多尝试3次
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for reading in result:
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text_level = text_difficulty_level(reading['text'], d3)
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# 从高斯分布中得出的平均值为 0.8，站位偏差为 1 的数字
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factor = random.gauss(0.8, 0.1)
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# 新的文章之前没有出现过且符合一定范围的水平
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if reading['article_id'] not in visited_articles["article_ids"] and within_range(text_level, user_level, (8.0 - user_level) * factor):
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d = reading
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visited_articles["article_ids"].append(d['article_id']) # 列表添加新的文章id；下面进行
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result_of_generate_article = "found"
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break
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if result_of_generate_article == "found": # 用于成功找到文章后及时退出外层循环
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break
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if result_of_generate_article != "found": # 阅读完所有文章，或者循环3次没有找到适合的文章，则放入空（“null”）
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visited_articles["article_ids"].append('null')
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else: # 生成已经阅读过的文章
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d = random.choice(result)
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text_level = text_difficulty_level(d['text'], d3)
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result_of_generate_article = "found"
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today_article = None
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if d:
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today_article = {
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"user_level": f'{user_level:4.2f}',
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"text_level": f'{text_level:4.2f}',
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"date": d['date'],
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"article_title": get_article_title(d['text']),
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"article_body": get_article_body(d['text']),
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"source": d["source"],
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"question": get_question_part(d['question']),
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"answer": get_answer_part(d['question'])
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}
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return visited_articles, today_article, result_of_generate_article
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def load_freq_history(path):
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d = {}
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if os.path.exists(path):
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d = pickle_idea.load_record(path)
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return d
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def within_range(x, y, r):
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return x > y and abs(x - y) <= r
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def get_question_part(s):
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s = s.strip()
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result = []
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flag = 0
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for line in s.split('\n'):
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line = line.strip()
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if line == 'QUESTION':
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result.append(line)
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flag = 1
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elif line == 'ANSWER':
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flag = 0
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elif flag == 1:
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result.append(line)
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return '\n'.join(result)
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def get_answer_part(s):
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s = s.strip()
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result = []
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flag = 0
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for line in s.split('\n'):
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line = line.strip()
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if line == 'ANSWER':
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flag = 1
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elif flag == 1:
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result.append(line)
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return '\n'.join(result)
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