H12 Ошибка тайм-аута запроса при развертывании модели ML с flask в Heroku

Question

Я пытаюсь развернуть одну модель ML, используя flask в Heroku. Вот файлы, которые у меня есть: model.py, app.py, demand.txt, procfile Github link-: https://github.com/dee-walia20/clickbait_detector

Фрагмент кода Model.py

# Importing the libraries

import pandas as pd
import pickle
from sklearn.base import BaseEstimator, TransformerMixin
import nltk
import re
from nltk.stem import WordNetLemmatizer
from nltk.corpus import wordnet, stopwords
from sklearn.pipeline import make_pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

#Import dataset and assign Input & Output
data = pd.read_csv('clickbait_data.csv')
X=data.headline
y=data.clickbait

#Custom Transformer built from sklearn for Text Processing
class TextPreprocessor(BaseEstimator, TransformerMixin):
    def __init__(self, lemmatizer=None, stopwords=None,token=True):
        self.token = token
        self.lemmatizer=lemmatizer
        self.stopwords=stopwords

    def fit(self, X, y=None):
        return self

    def transform(self, X, y=None):
        if self.token:
            nltk.download('averaged_perceptron_tagger')
            nltk.download('wordnet')
            X=X.apply(lambda x: re.sub('\d+',"",x))
            X=X.apply(lambda x: re.sub('\W'," ",x))
            X=X.apply(lambda x: re.sub("  "," ",x))
            X=X.apply(lambda x: x.lower())
            self.lemmatizer=WordNetLemmatizer()
            nltk.download("stopwords")
            self.stopwords=stopwords.words('english')
            X=X.apply(lambda x: " ".join([self.lemmatizer.lemmatize(word, self._get_wordnet_pos(word)) 
                          for word in x.split() if word not in self.stopwords]))
        return X
    def _get_wordnet_pos(self, word):
        tag=nltk.pos_tag([word])[0][1][0].upper()
        tag_dict={'J':wordnet.ADJ,
                  'N':wordnet.NOUN,
                  'V':wordnet.VERB,
                  'R':wordnet.ADV
                 }
        return tag_dict.get(tag,wordnet.NOUN)

#Splitting Training and Test Set
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.20, random_state=10)

#Creating the pipeline for End to end ML model

tp=TextPreprocessor()
tf=TfidfVectorizer()
model=MultinomialNB()
pipeline=make_pipeline(tp,tf,model)

#Fitting model with trainig data
pipeline.fit(X_train, y_train)

# Saving model to disk
pickle.dump(pipeline, open('pipeline.pkl','wb'), protocol=-1)

# Loading model to check performance on Test set

if __name__ == "__main__":
    saved_pipeline = pickle.load(open('pipeline.pkl','rb'))
    print("Classitication Report\n",classification_report(y_test, saved_pipeline.predict(X_test)))

app.py

import pandas as pd
from flask import Flask, request, render_template
import pickle
from model import TextPreprocessor

app = Flask(__name__)

model = pickle.load(open('pipeline.pkl', 'rb'))

@app.route('/')
def home():
    return render_template('index.html')

@app.route('/predict',methods=['GET','POST'])
def predict():
    '''
    For rendering results on HTML GUI
    '''

    final_features = pd.Series(data=str(request.form['headline']))
    prediction = model.predict(final_features)


    output = prediction[0]

    return render_template('result.html', prediction=output)

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

Сообщение об ошибке:

2020-05-02T11: 25: 36.304845 + 00: 00 heroku [router]: at = код ошибки = H12 desc = "Время ожидания запроса" = GET path = "/ favicon.ico" host = clickbait-headline-detector.herokuapp.com request_id = 3aa2f839-109a-4c41-a121-3738e440a62f fwd = "180.151.118.230" dyno = web .1 connect = 1ms service = 30001ms status = 503 bytes = 0 protocol = https

Я прочитал в Heroku тайм-аут на 30 мс для ошибки H12 и возможного решения для создания фоновых / рабочих задач. Но я не знаю, какая задача будет здесь работать и как это сделать?