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 __future__ import division, print_function
from flask import Flask,abort,render_template,request,redirect,url_for
#from werkzeug import secure_filename
#from werkzeug.utils import secure_filename
from werkzeug.utils import secure_filename

# coding=utf-8
import sys
import os
import glob
import re
import numpy as np

import zipfile

# Keras
from keras.applications.imagenet_utils import preprocess_input, decode_predictions
from keras.models import load_model
from keras.preprocessing import image

# Flask utils
from flask import Flask, redirect, url_for, request, render_template, flash
#from gevent.wsgi import WSGIServer
from gevent.pywsgi import WSGIServer
import os
#import magic
import urllib.request
from flask import Flask, flash, request, redirect, render_template



from flask import Flask

UPLOAD_FOLDER = 'uploads/'

app = Flask(__name__)
app.secret_key = "secret key"
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024




ALLOWED_EXTENSIONS = set(['pdf', 'png', 'jpg', 'jpeg', 'gif'])

def allowed_file(filename):
    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS



# Model saved with Keras model.save()
MODEL_PATH = 'models/my_model.h5'

#Load your trained model
model = load_model(MODEL_PATH)
model._make_predict_function()          # Necessary to make everything ready to run on the GPU ahead of time
print('Model loaded. Start serving...')


def model_predict(img_path, model):
    img = image.load_img(img_path, target_size=(50,50)) #target_size must agree with what the trained model expects!!

    # Preprocessing the image
    img = image.img_to_array(img)
    img = np.expand_dims(img, axis=0)

   
    preds = model.predict(img)
    pred = np.argmax(preds,axis = 1)
    return pred

def write_fichier(b):
    fichier = open("test.txt", "a")
    fichier.write(str(b)+ '\n')
    fichier.close()

def cellule_contaminee(file):
    f = open(file)
    cellule_cont = 0
    cellule_normal = 1
    count = 0
    for line in f:
        count += 1
        if "[0]" in line:
            cellule_cont += 1
        else:
            cellule_normal += 1          
    f.close()
    st1 = "Le nombre de cellules contaminées est de {fname} sur un total de cellules uploader de {age}".format(fname = cellule_cont, age = count)
    st2 = "Le nombre de cellules normales est de {fnime} sur un total de cellules".format(fnime = cellule_normal)
    #if (((100*cellule_cont)/count) < 99.92):
    return  '''
<!DOCTYPE html>
<html>
<body>
<h3><span style="color:#cb8cbd"> '''+ st1 + '''</span> </h3>
<hr color="black">
<div style="background-color:black;color:white;padding:20px;">
  <h2>Notes</h2>
  <p>Les résultats obtenus dépendent de la précision du modèle.</p>
  <p>La marge d'erreur de notre modèle est d' 1%.</p>
</div> 
</body>
</html>

''' 
    #else:
    #    return st2 
    
    
@app.route('/')
def upload_form():
    return render_template('upload.html')

@app.route('/', methods=['GET','POST'])
def upload_file():
    if request.method == 'POST':
        if 'files[]' not in request.files:
            flash('No file part')
            return redirect(request.url)
        files = request.files.getlist('files[]')
        for f in request.files.getlist('files[]'):
            basepath = os.path.dirname(__file__)
            file_path = os.path.join(
                basepath, 'uploads', secure_filename(f.filename))
            f.save(file_path)
            flash('File(s) successfully uploaded')
            pred = model_predict(file_path, model)
            str1 = 'Malaria Parasitized'
            str2 = 'Normal'
            results = " "
            if pred == 0:
                results = str1
                write_fichier(pred)
                #return str1
            else:
                results = str2
                write_fichier(pred)
                #return str2
    #return redirect('/')
    #return cellule_contaminee("test.txt")
    #return "The factors of {} are {}".format(num, factors(num))
    return cellule_contaminee("test.txt")


if __name__ == "__main__":
    app.run()
```

Which of the following functions or classes contains a `CWE-601: URL Redirection to Untrusted Site ('Open Redirect')` vulnerability?

  A) allowed_file
  B) upload_form
  C) cellule_contaminee
  D) upload_file
Answer: D