system: You are an expert security code reviewer. You will be shown a source code file along with a multiple-choice question listing several function or class names from that file. Your task is to identify which one contains the specified CWE vulnerability.

OUTPUT FORMAT: Respond with the single letter of the correct option, wrapped in \boxed{...}. Example: `\boxed{B}`. Output the boxed letter ONLY -- no explanation, no other text.

user: ```python
from flask import Flask,request, url_for, redirect, render_template
from bs4 import BeautifulSoup
import re
import requests
import heapq
from textblob import TextBlob
from nltk.tokenize import sent_tokenize,word_tokenize
app = Flask(__name__)


@app.route('/')
def home():
    return render_template("index.html")
@app.route('/summary',methods=['POST','GET'])
def data():
    from nltk.corpus import stopwords

    url = request.form['url']
    num = request.form['count']
    num = int(num)

    headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}
    #url = str(input("Paste the url......."))
    res = requests.get(url,headers=headers)
    summary = ""
    soup = BeautifulSoup(res.text,'html.parser') 
    content = soup.findAll("p")
    for text in content:
        summary +=text.text 
    def clean(text):
        text = re.sub(r"\[[0-9]*\]"," ",text)
        text = text.lower()
        text = re.sub(r'\s+'," ",text)
        text = re.sub(r","," ",text)
        return text
    summary = clean(summary)
    
    print("Getting the data......\n")

  
    ##Tokenixing
    sent_tokens = sent_tokenize(summary)

    summary = re.sub(r"[^a-zA-z]"," ",summary)
    word_tokens = word_tokenize(summary)
    ## Removing Stop words

    word_frequency = {}
    stopwords =  set(stopwords.words("english"))

    for word in word_tokens:
        if word not in stopwords:
            if word not in word_frequency.keys():
                word_frequency[word]=1
            else:
                word_frequency[word] +=1
    maximum_frequency = max(word_frequency.values())
    print(maximum_frequency)          
    for word in word_frequency.keys():
        word_frequency[word] = (word_frequency[word]/maximum_frequency)
    print(word_frequency)
    sentences_score = {}
    for sentence in sent_tokens:
        for word in word_tokenize(sentence):
            if word in word_frequency.keys():
                if (len(sentence.split(" "))) <30:
                    if sentence not in sentences_score.keys():
                        sentences_score[sentence] = word_frequency[word]
                    else:
                        sentences_score[sentence] += word_frequency[word]
                        
    print(max(sentences_score.values()))
    def get_key(val): 
        for key, value in sentences_score.items(): 
            if val == value: 
                return key 
    key = get_key(max(sentences_score.values()))
    print(key+"\n")
    print(sentences_score)
    summary = heapq.nlargest(num,sentences_score,key=sentences_score.get)
    print(" ".join(summary))
    summary = " ".join(summary)

    ###  POlarity of the text


    obj = TextBlob(" ".join(summary))
    sentiment = obj.sentiment.polarity
    if sentiment == 0:
        print("\nText is neutral")
    elif sentiment >0:
        print("\nText is positive")
    else :
        print("\nText is negative")

    return render_template(f'index.html',summary=f'{summary}')

  
if __name__ == '__main__':
    app.run(debug=True)

```

Which of the following functions or classes contains a `CWE-20: Improper Input Validation` vulnerability?

  A) clean
  B) get_key
  C) home
  D) data
Answer: D