{  "introduction_by_movie": [    "nm简介",    "nm剧情简介",    "nm的内容是什么",    "nm讲了什么",    "nm讲了什么故事",    "nm演了什么",    "nm的故事梗概是什么",    "nm的剧情简介是什么",    "nm的内容简介是什么",    "nm的剧情介绍是什么",    "nm的情节是什么",    "nm的主要情节是什么"  ],  "rating_by_movie": [    "nm的评分是多少",    "nm得了多少分",    "nm的评分有多少",    "nm的评分",    "nm得分是多少",    "nm的分数是",    "nm电影分数是多少",    "nm电影评分",    "nm评分",    "nm的分数是多少",    "nm这部电影的评分是多少"  ],  "release_date_by_movie": [    "nm上映时间",    "nm定档时间",    "nm的上映时间是什么时候",    "nm的首映时间是什么时候",    "nm什么时候上映",    "nm什么时候首映",    "最早什么时候能看到nm",    "nm什么时候在影院上线",    "什么时候可以在影院看到nm",    "nm什么时候在影院放映",    "nm什么时候首播"  ],  

Forrest Gump nmKill Bill: Vol. 1 nm英雄 nmMiami Vice nmIndiana Jones and the Temple of Doom nm卧虎藏龙 nmPirates of the Caribbean: At World's End nmKill Bill: Vol. 2 nmThe Matrix Reloaded nmThe Matrix Revolutions nmHarry Potter and the Chamber of Secrets nmHarry Potter and the Prisoner of Azkaban nmHarry Potter and the Goblet of Fire nmHarry Potter and the Order of the Phoenix nmThe Last Emperor nmHarry Potter and the Half-Blood Prince nm花样年华 nm2046 nmLethal Weapon 4 nmHannibal Rising nmTMNT nm무사 nmAnna and the King nm满城尽带黄金甲 nm

jiebaneo4jpython-dotenvscikit-learnflaskflask-corsgunicorn

import os
from neo4j import GraphDatabase

class Database:    """    Neo4j 数据库访问层。
    管理数据库连接的生命周期，并提供查询接口。    """
    def __init__(self):        uri = os.environ["DATABASE_URI"]        user = os.environ["DATABASE_USER"]        password = os.environ["DATABASE_PASSWORD"]
        try:            self._driver = GraphDatabase.driver(uri, auth=(user, password))            self._session = self._driver.session()        except Exception as e:            raise Exception("数据库连接失败") from e
    def close(self):        try:            self._session.close()            self._driver.close()        except Exception as e:            raise Exception("数据库断开失败") from e
    def find_one(self, query: str, **parameters):        result = self._session.run(query, parameters).single()        return result.value() if result else None
    def find_many(self, query: str, **parameters):        return self._session.run(query, parameters).value()

if __name__ == "__main__":    import dotenv
    dotenv.load_dotenv()
    database = Database()    genres = database.find_many(        """        MATCH (m:Movie)-[BELONGS_TO]->(g:Genre)        WHERE m.name = $movie_name        RETURN g.name        """,        movie_name="卧虎藏龙",    )    database.close()
    print(genres)

import jsonimport os
import jiebafrom sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.naive_bayes import MultinomialNB
TRAIN_DATASET_PATH = os.path.join("data", "train.json")
jieba.setLogLevel("ERROR")

def normalize(sentence: str):    return " ".join(jieba.cut(sentence))

class BaseClassifier:    """    底层分类器。
    使用 TF-IDF 向量化文本，然后使用朴素贝叶斯预测标签。    """
    def __init__(self):        self._vectorizer = TfidfVectorizer()        self._classifier = MultinomialNB(alpha=0.01)
    def _train(self, x: list, y: list):        X = self._vectorizer.fit_transform(x).toarray()        self._classifier.fit(X, y)
    def _predict(self, x: list):        X = self._vectorizer.transform(x).toarray()        return self._classifier.predict(X)

class Classifier(BaseClassifier):    """    问题分类器。
    根据问题中出现的关键词，将问题归于某一已知类别下。    """
    def __init__(self):        BaseClassifier.__init__(self)        questions, labels = Classifier._read_train_dataset()        self._train(questions, labels)
    def classify(self, sentence: str):        question = normalize(sentence)        return self._predict([question])[0]
    @staticmethod    def _read_train_dataset():        with open(TRAIN_DATASET_PATH, "r", encoding="utf-8") as fr:            train_dataset: dict[str, list[str]] = json.load(fr)
        questions = []        labels = []        for label, sentences in train_dataset.items():            questions.extend([normalize(sentence) for sentence in sentences])            labels.extend([label for _ in sentences])
        return questions, labels

if __name__ == "__main__":    classifier = Classifier()
    while True:        sentence = input("请输入问题：").strip()        label = classifier.classify(sentence)        print(f"问题分类：{label}")

# Neo4j 数据库地址DATABASE_URI=
# Neo4j 用户名DATABASE_USER=
# Neo4j 密码DATABASE_PASSWORD=

cd backendgunicorn app:app

# 后端服务地址VITE_API_BASE_URL=

cd frontendnpm buildnpm preview