code review

1.调整了一些方法和类的关系
2.优化了数据加载
3.修正了错误的文件路径

app/vocabulary.py

@@ -1,16 +1,34 @@
-"""
-词汇难度评估系统
-功能：根据单词在不同考试中的出现情况评估其难度级别，并计算用户或文章的词汇水平
-"""
-
 import re
 import pickle
+import os
 from typing import Dict, List, Tuple, Union
+from collections import Counter
 
 # 预编译正则表达式提高性能
 WORD_PATTERN = re.compile(r'\b[\w-]+\b')
 
 
+class VocabularyLevelEstimator:
+    """
+    词汇难度评估基类
+    使用预定义的单词-考试类型数据评估单词难度级别
+
+    类属性:
+    _test_raw: 原始单词-考试类型数据
+    _difficulty_dict: 转换后的单词-难度级别映射
+    """
+    _test_raw = None
+    _difficulty_dict = None
+    PICKLE_PATH = 'static/words_and_tests.p'  # 默认数据文件路径
+
+    @classmethod
+    def _load_data(cls):
+        """延迟加载数据，避免不必要的文件操作"""
+        if cls._test_raw is None:
+            cls._test_raw = cls.load_record(cls.PICKLE_PATH)
+            cls._difficulty_dict = cls.convert_test_type_to_difficulty_level(cls._test_raw)
+
+    @staticmethod
     def load_record(pickle_fname: str) -> Dict[str, List[str]]:
         """
         加载pickle格式的单词-考试类型数据
@@ -26,14 +44,18 @@ def load_record(pickle_fname: str) -> Dict[str, List[str]]:
         ValueError: 当pickle文件损坏时抛出
         """
         try:
+            # 文件校验
+            if not os.path.exists(pickle_fname):
+                raise FileNotFoundError(f"词汇数据文件 {pickle_fname} 未找到")
+            if not pickle_fname.endswith('.p'):
+                raise ValueError("仅支持.pickle格式文件")
+
             with open(pickle_fname, 'rb') as f:
                 return pickle.load(f)
-    except FileNotFoundError:
+        except pickle.PickleError as e:
-        raise FileNotFoundError(f"Pickle文件 {pickle_fname} 未找到")
+            raise ValueError(f"Pickle文件 {pickle_fname} 损坏: {str(e)}")
-    except pickle.PickleError:
-        raise ValueError(f"Pickle文件 {pickle_fname} 损坏或格式不正确")
-
 
+    @staticmethod
     def convert_test_type_to_difficulty_level(d: Dict[str, List[str]]) -> Dict[str, int]:
         """
         将考试类型映射为难度级别
@@ -54,42 +76,21 @@ def convert_test_type_to_difficulty_level(d: Dict[str, List[str]]) -> Dict[str,
         """
         result = {}
         for word, test_types in d.items():
+            word_lower = word.lower()  # 统一小写处理
             if 'CET4' in test_types:
-            result[word] = 4
+                result[word_lower] = 4
             elif 'OXFORD3000' in test_types:
-            result[word] = 5
+                result[word_lower] = 5
             elif 'CET6' in test_types or 'GRADUATE' in test_types:
-            result[word] = 6
+                result[word_lower] = 6
-        elif 'IELTS' in test_types:
+            elif 'IELTS' in test_types or 'OXFORD5000' in test_types:
-            result[word] = 7
+                result[word_lower] = 7
-        elif 'OXFORD5000' in test_types:
-            result[word] = 7
             elif 'BBC' in test_types:
-            result[word] = 8
+                result[word_lower] = 8
             else:
-            result[word] = 0
+                result[word_lower] = 0
         return result
 
-
-class VocabularyLevelEstimator:
-    """
-    词汇难度评估基类
-    使用预定义的单词-考试类型数据评估单词难度级别
-
-    类属性:
-    _test_raw: 原始单词-考试类型数据
-    _difficulty_dict: 转换后的单词-难度级别映射
-    """
-    _test_raw = None
-    _difficulty_dict = None
-
-    @classmethod
-    def _load_data(cls):
-        """延迟加载数据，避免不必要的文件操作"""
-        if cls._test_raw is None:
-            cls._test_raw = load_record('words_and_tests.p')
-            cls._difficulty_dict = convert_test_type_to_difficulty_level(cls._test_raw)
-
     @classmethod
     def get_word_level(cls, word: str) -> int:
         """
@@ -102,27 +103,32 @@ class VocabularyLevelEstimator:
         单词的难度级别(0-8)
         """
         cls._load_data()
-        return cls._difficulty_dict.get(word, 0)
+        return cls._difficulty_dict.get(word.lower(), 0)
+
+    @classmethod
+    def reload_data(cls, new_path=None):
+        """强制重新加载词汇数据"""
+        if new_path:
+            cls.PICKLE_PATH = new_path
+        cls._test_raw = None
+        cls._difficulty_dict = None
+        cls._load_data()
 
 
 class UserVocabularyLevel(VocabularyLevelEstimator):
-    """
+    """用户词汇水平评估"""
-    用户词汇水平评估
-    根据用户最近查询的单词评估其词汇水平
-    """
 
-    def __init__(self, d: Dict[str, List[int]]):
+    def __init__(self, user_data: Dict[str, List[int]]):
         """
         初始化用户词汇数据
 
         参数:
-        d: 单词到时间戳列表的映射
+        user_data: 单词到时间戳列表的映射
         """
-        self.d = d
         # 获取每个单词的最新查询时间并排序
-        word_time = [(word, max(times)) for word, times in d.items() if times]
+        word_time = [(word, max(times)) for word, times in user_data.items() if times]
         sorted_words = sorted(word_time, key=lambda x: x[1], reverse=True)
-        self.recent_words = [word for word, _ in sorted_words[:3]]
+        self.recent_words = [word for word, _ in sorted_words[:3]]  # 取最近3个单词
 
     @property
     def level(self) -> float:
@@ -139,10 +145,7 @@ class UserVocabularyLevel(VocabularyLevelEstimator):
 
 
 class ArticleVocabularyLevel(VocabularyLevelEstimator):
-    """
+    """文章词汇水平评估"""
-    文章词汇水平评估
-    根据文章中出现的最高难度单词评估文章词汇水平
-    """
 
     def __init__(self, content: str):
         """
@@ -150,19 +153,19 @@ class ArticleVocabularyLevel(VocabularyLevelEstimator):
 
         参数:
         content: 文章内容字符串
-
-        异常:
-        ValueError: 当内容为空或不是字符串时抛出
         """
         if not content or not isinstance(content, str):
-            raise ValueError("文章内容必须是非空字符串")
+            self.top_levels = []
+            return
 
-        self.content = content
+        # 文本预处理：转换为小写并提取单词
-        # 提取所有单词并计算难度
         words = WORD_PATTERN.findall(content.lower())
+
+        # 计算单词难度并筛选有效值
         word_levels = [self.get_word_level(word) for word in words]
-        # 筛选有效难度并排序
         valid_levels = sorted([lvl for lvl in word_levels if lvl > 0], reverse=True)
+
+        # 取难度最高的5个单词
         self.top_levels = valid_levels[:5] if valid_levels else []
 
     @property
@@ -177,3 +180,17 @@ class ArticleVocabularyLevel(VocabularyLevelEstimator):
         if not self.top_levels:
             return 0
         return sum(self.top_levels) / len(self.top_levels)
+
+    def word_frequency(self, top_n=10) -> Dict[str, int]:
+        """
+        获取文章词频统计
+
+        参数:
+        top_n: 返回的最高频单词数量
+
+        返回:
+        词频最高的top_n个单词及其频率
+        """
+        words = WORD_PATTERN.findall(self.content.lower())
+        word_freq = Counter(words)
+        return dict(word_freq.most_common(top_n))