AWS? ?? ??/AI? ??? ?? ???? ?? Streamlit, ??? ?? ?? NLP? ???? ?? ??? ?? ?????? ??? AWS ?? ?? ???? ???? ??????. S3 ??. ? ??????? ??? ??? ???? AWS ?? ??? ??? ? ????. ?

?, ?? ??? ?????!

????

???? ?? Python? ???? ??? ???? ? ???? ??? ?????. ?? ?? ?? ?????? ?????.

?

pip install streamlit boto3 moto scikit-learn

1??: Mock S3 ??

?? Moto? ???? ?? S3? ???? ??? ?????. ?? AWS? ???? ?? AWS S3? ??????? ? ?????.

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": "Introduction to AWS", "tags": "AWS, Cloud Computing, Basics"},
            {"title": "Deep Learning on AWS", "tags": "AWS, Deep Learning, AI"},
            {"title": "NLP with SageMaker", "tags": "AWS, NLP, Machine Learning"},
            {"title": "Serverless with AWS Lambda", "tags": "AWS, Serverless, Lambda"},
        ]
    }
    s3.Bucket(bucket_name).put_object(Key="mock_resources.json", Body=json.dumps(data))
    return bucket_name

2??: ?? ??

???? ??? ??? ?? ?? NLP? ???? ?? ??? ???? ??? ???????.

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

3??: ???? ?????

?? Streamlit? ???? ??????? ?????? ???? ?????.

import streamlit as st

st.title("AWS Learning Path Recommender")

user_input = st.text_input("What do you want to learn about AWS?", "I want to learn about AWS and AI")

if st.button("Get Recommendations"):
    with mock_s3():
        bucket_name = setup_mock_s3()
        recommendations = recommend_learning_path(user_input, bucket_name)

    st.subheader("Recommended Learning Path:")
    for i, rec in enumerate(recommendations, 1):
        st.write(f"{i}. {rec}")

4??: ????
?? ?? ??? 'app.py'?? ?? Python ??? ?????.

import streamlit as st
import boto3
from moto import mock_s3
import json
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# [Insert setup_mock_s3 function here]

# [Insert recommend_learning_path function here]

st.title("AWS Learning Path Recommender")

user_input = st.text_input("What do you want to learn about AWS?", "I want to learn about AWS and AI")

if st.button("Get Recommendations"):
    with mock_s3():
        bucket_name = setup_mock_s3()
        recommendations = recommend_learning_path(user_input, bucket_name)

    st.subheader("Recommended Learning Path:")
    for i, rec in enumerate(recommendations, 1):
        st.write(f"{i}. {rec}")

5??: ? ??
Streamlit ?? ????? ???? ?? ????? ???? ??? ??? ? ??? ?????.

streamlit run app.py

??? ?? Streamlit ??? ???? ?? ? ?????? ?? ????.

?? ??
?? ? ?? AWS ?? ??? ??? ??? ?? S3 ??? ?????.

? ??????? ??? ?? ???? ????? ???? "?? ??"? ???? ??? ??? ?? TF-IDF? ???? ?? ???? ?? ???? ?????. ?? 3? ??? ????? ?????.

??.

? ??? ??????? Streamlit? ???? NLP ??? AWS ???? ???? ??? ?? ?? ???? ?????. ?? AWS ???? ????? ? ?? ???? ????? ? ??? ?? ????? ???? ? ?? ??? ? ????.

??? ?? ??? ??? ??? ?? ?? ??? ? ????. ?? ??? ?? ??????? ???? ?? ??, ??? ? ??? ??? ?? ????? ?? ??????.

? ?? ????? ?? ?????!

? ??? Streamlit? ???? ??? ??? AWS ?? ?? ???? ?? ?????. ??? ??? PHP ??? ????? ?? ?? ??? ?????!