pip install streamlit boto3 moto scikit-learn

import boto3 from moto import mock_s3 import json  def setup_mock_s3():     s3 = boto3.resource("s3", region_name="us-east-1")     bucket_name = "mock-learning-paths"     s3.create_bucket(bucket=bucket_name)     data = {         "resources": [             {"title": "AWS入门", "tags": "aws, 云计算, 基础"},             {"title": "AWS深度学习", "tags": "aws, 深度学习, ai"},             {"title": "SageMaker自然语言处理", "tags": "aws, nlp, 机器学习"},             {"title": "AWS Lambda无服务器架构", "tags": "aws, 无服务器, lambda"},         ]     }     s3.bucket(bucket_name).put_object(key="mock_resources.json", body=json.dumps(data))     return bucket_name

from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity  def recommend_learning_path(user_input, bucket_name):     s3 = boto3.client("s3", region_name="us-east-1")     obj = s3.get_object(bucket=bucket_name, key="mock_resources.json")     data = json.loads(obj['body'].read().decode('utf-8'))      resources = data["resources"]     titles = [resource["title"] for resource in resources]     tags = [resource["tags"] for resource in resources]      corpus = tags + [user_input]     vectorizer = TfidfVectorizer()     tfidf_matrix = vectorizer.fit_transform(corpus)     similarity = cosine_similarity(tfidf_matrix[-1], tfidf_matrix[:-1])      scores = similarity.flatten()     ranked_indices = scores.argsort()[::-1]      recommendations = [titles[i] for i in ranked_indices[:3]]     return recommendations

import streamlit as st  st.title("AWS学习路径推荐器")  user_input = st.text_input("你想学习哪些AWS相关内容？", "我想学习AWS和AI")  if st.button("获取推荐"):     with mock_s3():         bucket_name = setup_mock_s3()         recommendations = recommend_learning_path(user_input, bucket_name)      st.subheader("推荐学习路径：")     for i, rec in enumerate(recommendations, 1):         st.write(f"{i}. {rec}")

streamlit run app.py