system: You are an expert security code reviewer. You will be shown a source code file along with a multi-select question listing several CWE (Common Weakness Enumeration) categories. Your task is to identify which of the listed CWEs are present in the code.

OUTPUT FORMAT: Respond with the letter(s) of all correct options, sorted alphabetically and comma-separated, wrapped in a single \boxed{...}. Examples: `\boxed{A}` for one correct, `\boxed{A,C}` for two, `\boxed{A,C,E}` for three. Output the boxed answer ONLY -- no explanation, no other text.

user: ```python
from flask import render_template, jsonify, Flask, redirect, url_for, request
import random
import os
import numpy as np
from keras.applications.mobilenet import MobileNet 
from keras.preprocessing import image
from keras.applications.mobilenet import preprocess_input, decode_predictions
from keras.models import model_from_json
import keras
from keras import backend as K
from werkzeug.utils import secure_filename



UPLOAD_FOLDER = 'F:/Sonal/Skin-Lesian-Analyzer/assets'
ALLOWED_EXTENSIONS = {'txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'}


app = Flask(__name__)
app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024


SKIN_CLASSES = {
  0: 'Actinic Keratoses (Solar Keratoses) or intraepithelial Carcinoma (Bowen’s disease)',
  1: 'Basal Cell Carcinoma',
  2: 'Benign Keratosis',
  3: 'Dermatofibroma',
  4: 'Melanoma',
  5: 'Melanocytic Nevi',
  6: 'Vascular skin lesion'

}

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

@app.route('/')
def index():
    return render_template('index.html', title='Home')

@app.route('/uploaded', methods = ['GET', 'POST'])
def upload_file():
    if request.method == 'POST':
        f = request.files['file']
        if f and allowed_file(f.filename):
            filename = secure_filename(f.filename)
            path='static/data/'+f.filename
            f.save(path)
        j_file = open('modelnew.json', 'r')
        loaded_json_model = j_file.read()
        j_file.close()
        model = model_from_json(loaded_json_model)
        model.load_weights('modelnew.h5')
        img1 = image.load_img("F:/Sonal/skin-disease-detection/static/data/bcc.jpg",target_size=(224,224))
        img1 = np.array(img1)
        img1 = img1.reshape((1,224,224,3))
        img1 = img1/255
        prediction = model.predict(img1)
        pred = np.argmax(prediction)
        disease = SKIN_CLASSES[pred]
        accuracy = prediction[0][pred]
        K.clear_session()
    return render_template('uploaded.html', title='Success', predictions=disease, acc=accuracy*100, img_file=f.filename)

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

Which of the following CWE vulnerability categories are present in this code? Multiple options may be correct.

  A) CWE-94: Improper Control of Generation of Code ('Code Injection')
  B) CWE-776: Improper Restriction of Recursive Entity References in DTDs ('XML Entity Expansion')
  C) CWE-22: Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
  D) CWE-215: Insertion of Sensitive Information Into Debugging Code
  E) CWE-918: Server-Side Request Forgery (SSRF)
  F) CWE-89: Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')
Answer: C,D