I have been trying to deploy chat bot that uses a pre trained transformer model, DistilBertForQuestionAnswering and using flask, flask_socketio, tensorflow. Here is my nginx file,

upstream socketio_nodes {
    ip_hash;
#    keepalive 60;
    #server http://unix:/home/web_chatbot/web_chatbot.sock/socket.io:/socket.io/;
    server 127.0.0.1:5000;
    server 127.0.0.1:5001;
    server 127.0.0.1:5002;
    # to scale the app, just add more nodes here!
}

server {
    listen 80;
    server_name xxx.xxx.xxx.194;

location / {
  include proxy_params;
  proxy_pass http://127.0.0.1:5000;
#  proxy_read_timeout     1200;
    }

location /static {
        alias /home/web_chatbot/web_chatbot/static;
        expires 30d;
    }

    location /socket.io {
        include proxy_params;
        proxy_http_version 1.1;
        proxy_buffering off;
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection "";
        proxy_pass http://socketio_nodes/socket.io;
 #       proxy_read_timeout     1200;
 #       proxy_connect_timeout  1200;
   }

}

my flask app file

from flask import Flask, request, render_template, flash, url_for
from flask_socketio import SocketIO
from web_chatbot.toweb import Conversation
# import redis
from flask_cors import CORS

app = Flask(__name__)
# cache = redis.Redis(host='redis', port=6379)
CORS(app, resources={
    r"/*": {
        "origins": "*"
    }
})
# app.config['SECRET_KEY'] = 'xxxxxxx'
socketio = SocketIO(app)


@app.route("/")
def home():

    return render_template('index.html')


def messageReceived(message, methods=['GET', 'POST']):
    msg_ = str(message['message'])
    if (msg_ != 'User Connected'):

        botReply(str(msg_))
        print('message was received! ')
    else:
        pass

@socketio.on('chat-event')
def handleEvents(json_, methods=['GET', 'POST']):
    dict_object = json_
    print('received my event tada: ' + str(dict_object))
    socketio.emit('msg', json_, callback=messageReceived(dict_object))


def botReply(msg):
    print('BOT INPUT: ' + msg)
    chat_engine = Conversation()
    bot_reply = chat_engine.model_ans(msg)
    print('Bot Reply: ' + bot_reply)
    socketio.emit('reply', bot_reply)


@socketio.on('connected')
def connectedGuest(methods=['GET', 'POST']):
    print('Connected')


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

my toweb.py file is

from transformers import pipeline
from functools import lru_cache

import multiprocessing
import codecs

class Model():

    context = "./sw_merge.txt"

    @lru_cache(maxsize=10000)
    def __init__(self):
        print('processing - iniit in model')
        self.model = pipeline('question-answering')
        with codecs.open(self.context, 'rb', errors = 'ignore', encoding='utf-8') as f:
            self.lines = f.read()

    def run_qa(self, qn):
        print('run_qa - on proccessing')
        ans = self.model(context = self.lines, question = qn)
        return ans


class Conversation():
    #incoming messages - receives an input from the user
    def incoming(self, question):
        usr_qn = []
        usr_qn.append(question)
        return usr_qn

    #model prediction
    def model_ans(self, input_qn):
        y = Model()
        ans = y.run_qa(input_qn)
        ans_text = ans.get("answer")
        print('model_ans - running')
        return ans_text




if __name__ == '__main__':

    p1 = multiprocessing.Process(target=Conversation)
    p2 = multiprocessing.Process(target=Model)

    p1.start()
    p2.start()

    p1.join()
    p2.join()


    # check if processes are alive 
    print("Process p1 is alive: {}".format(p1.is_alive())) 
    print("Process p2 is alive: {}".format(p2.is_alive()))

    # question = ''
    # chat_conv = Conversation()

    # incoming_text = chat_conv.incoming(question)
    # outgoing_text = chat_conv.model_ans(incoming_text)

So when i type something on my chart bubble the text start being processed but the processes is terminetated with Broken Pipe Error

my gunicorn has been configured to have 3 workers and use gevelent.

My chart app works fine on local, I’m not sure if the problem is package version or there is something wrong on my configurations. I will rely appreciate for some help please!!!

here is my requirements env

absl-py==0.9.0
astor==0.8.1
astunparse==1.6.3
cachetools==4.1.1
certifi==2020.6.20
chardet==3.0.4
click==7.1.2
dataclasses==0.7
dnspython==1.16.0
filelock==3.0.12
Flask==1.1.2
Flask-Cors==3.0.8
Flask-SocketIO==4.3.1
gast==0.3.3
gevent==20.6.2
gevent-websocket==0.10.1
google-auth==1.20.1
google-auth-oauthlib==0.4.1
google-pasta==0.2.0
greenlet==0.4.16
grpcio==1.31.0
gunicorn==20.0.4
h5py==2.10.0
idna==2.10
importlib-metadata==1.7.0
itsdangerous==1.1.0
Jinja2==2.11.2
joblib==0.16.0
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.2
Markdown==3.2.2
MarkupSafe==1.1.1
monotonic==1.5
numpy==1.18.5
oauthlib==3.1.0
opt-einsum==3.3.0
packaging==20.4
protobuf==3.13.0
pyasn1==0.4.8
pyasn1-modules==0.2.8
pyparsing==2.4.7
python-engineio==3.13.1
python-socketio==4.6.0
regex==2020.7.14
requests==2.24.0
requests-oauthlib==1.3.0
rsa==4.6
sacremoses==0.0.43
scipy==1.4.1
sentencepiece==0.1.91
six==1.15.0
tb-nightly==1.14.0a20190603
tensorboard==2.3.0
tensorboard-plugin-wit==1.7.0
tensorflow==2.3.0
tensorflow-estimator==2.3.0
termcolor==1.1.0
tf-estimator-nightly==1.14.0.dev2019060501
tokenizers==0.8.1rc1
tqdm==4.48.2
transformers==3.0.2
urllib3==1.25.10
Werkzeug==1.0.1
wrapt==1.12.1
zipp==3.1.0
zope.event==4.4
zope.interface==5.1.0