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
import os
import requests
import json
import pandas as pd

from flask import Flask, request, Response

# constants
TOKEN = ''

## Info about the Bot
#https://api.telegram.org/bot/getMe
#
## get updates
#https://api.telegram.org/bot/getUpdates

## Webhook
#https://api.telegram.org/bot/setWebhook?url=https://.localhost.run

## Webhook Heroku
#https://api.telegram.org/botsetWebhook?url=https://rossmann-telegram-bot-bp.herokuapp.com
#
## send message
#https://api.telegram.org//sendMessage?chat_id=357750840&text=Hi Bruna, I am doing good, tks!


def send_message( chat_id, text ):
    url = 'https://api.telegram.org/bot{}/'.format( TOKEN ) 
    url = url + 'sendMessage?chat_id={}'.format( chat_id ) 

    r = requests.post( url, json={'text': text } )
    print( 'Status Code {}'.format( r.status_code ) )

    return None


def load_dataset( store_id ):
    # loading test dataset
    df10 = pd.read_csv( 'test.csv' )
    df_store_raw = pd.read_csv( 'store.csv' )

    # merge test dataset + store
    df_test = pd.merge( df10, df_store_raw, how='left', on='Store' )

    # choose store for prediction
    df_test = df_test[df_test['Store'] == store_id]

    if not df_test.empty:
        # remove closed days
        df_test = df_test[df_test['Open'] != 0]
        df_test = df_test[~df_test['Open'].isnull()]
        df_test = df_test.drop( 'Id', axis=1 )

        # convert Dataframe to json
        data = json.dumps( df_test.to_dict( orient='records' ) )

    else:
        data = 'error'

    return data


def predict( data ):
    # API Call
    url = 'https://bp-rossmann-pred-model.herokuapp.com/rossmann/predict'
    header = {'Content-type': 'application/json' }
    data = data

    r = requests.post( url, data=data, headers=header )
    print( 'Status Code {}'.format( r.status_code ) )

    d1 = pd.DataFrame( r.json(), columns=r.json()[0].keys() )

    return d1


def parse_message( message ):
    chat_id = message['message']['chat']['id']
    store_id = message['message']['text']

    store_id = store_id.replace( '/', '' )

    try:
        store_id = int( store_id )

    except ValueError:
        store_id = 'error'

    return chat_id, store_id


# API initialize
app = Flask( __name__ )

@app.route( '/', methods=['GET', 'POST'] )
def index():
    if request.method == 'POST':
        message = request.get_json()

        chat_id, store_id = parse_message( message )

        if store_id != 'error':
            # loading data
            data = load_dataset( store_id )

            if data != 'error':
                # prediction
                d1 = predict( data )

                # calculation
                d2 = d1[['store', 'prediction']].groupby( 'store' ).sum().reset_index()
                
                # send message
                msg = 'Store Number {} will sell R${:,.2f} in the next 6 weeks'.format(
                            d2['store'].values[0],
                            d2['prediction'].values[0] ) 

                send_message( chat_id, msg )
                return Response( 'Ok', status=200 )

            else:
                send_message( chat_id, 'Store Not Available' )
                return Response( 'Ok', status=200 )

        else:
            send_message( chat_id, 'Store ID is Wrong' )
            return Response( 'Ok', status=200 )


    else:
        return '<h1> Rossmann Telegram BOT </h1>'


if __name__ == '__main__':
    port = os.environ.get( 'PORT', 5000 )
    app.run( host='0.0.0.0', port=port )

```

Which of the following functions or classes contains a `CWE-918: Server-Side Request Forgery (SSRF)` vulnerability?

  A) send_message
  B) index
  C) parse_message
  D) predict
Answer: A