Python医用聊天机器人代码

Python医用聊天机器人代码可以使用自然语言处理库NLTK和机器学习库scikit-learn来实现。以下是一个简单的示例代码：

import nltk
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# 语料库
corpus = [
    '我感觉不舒服',
    '我头痛',
    '我喉咙痛',
    '我发烧',
    '我咳嗽'
]

# 初始化词形还原器
lemmatizer =NetLemmatizer()

# 对语料库进行词形还和分词处理
processed_corpus = []
for sentence in corpus:
    words = nltk.word_tokenize(sentence)
    lemmatized_words = [lemizer.lemmatize(word) for word in words]
    processed_corpus.append(' '.join(lemmatized_words))

# 创建TF-IDF向量化器
vectorizer = TfidfVectorizer()
tfidf_matrix = vectorizer_transform(processed_corpus)

# 用户输入
user_input = input("请输入您的症状：")

# 对用户输入进行处理
processed_user_input = ' '.join([lemmatizer.lemmatize(word) for word in nltk.word_tokenize(user_input)])

# 将用户输入转换为TF-IDF向量
user_tfidf = vectorizer.transform([processed_user_input])

# 计算用户输入与语料库中每个句子的相似度
similarity_scores = cosine_similarity(userfidf, tfidf_matrix)

# 获取最相似的句子
most_similar_sentence_index = similarity_scores.argmax()
response = corpus[most_similar_sentence_index]

print("医生的回答：", response)

# 相关问题
print("