REDO: Starting Flask in a thread, ca. 2020 - multithreading

Question

I have an old question looking for a fresh answer. I've tried the recipes presented in the similar but somewhat aged question "Start a flask application in a seperate thread", and some other similar solutions found in other posts.
The long and short of it is, I need to start a flask application in a 'background' thread, such that a wxPython GUI can run in the foreground. The solutions presented here seem to no longer have the desired effect. The flask app starts and the GUI never runs.
My suspicion is, the existing answers are out of date. That said, I'm open to the possibility that I've mangled something else that's hosing it up, please have a peek and advise accordingly.
Thanks for your eyeballs and brain cycles :)
My code follows.
#!/usr/bin/env python
"""
integrator.py (the app)
"""
import wx
from pubsub import pub
from flask import Flask
from flask_graphql import GraphQLView
from models import db_session
from schema import schema
from models import engine, db_session, Base, Idiom
flaskapp = Flask(__name__)
flaskapp.debug = True
flaskapp.add_url_rule(
'/graphql',
view_func=GraphQLView.as_view(
'graphql',
schema=schema,
graphiql=True
)
)
flaskapp.run(threaded=True,use_reloader=False)
#flaskapp.teardown_appcontext
def shutdown_session(exception=None):
db_session.remove()
class IntegratorTarget(wx.TextDropTarget):
def __init__(self, object):
wx.DropTarget.__init__(self)
self.object = object
def OnDropText(self, x, y, data):
print(f"<publish>{data}</publish>")
pub.sendMessage('default', arg1=data)
return True
class IntegratorFrame(wx.Frame):
def __init__(self, parent, title):
super(IntegratorFrame, self).__init__(parent, title = title,size = wx.DisplaySize())
self.panel = wx.Panel(self)
box = wx.BoxSizer(wx.HORIZONTAL)
dropTarget = IntegratorTarget(self.panel)
self.panel.SetDropTarget(dropTarget)
pub.subscribe(self.catcher, 'default')
self.panel.SetSizer(box)
self.panel.Fit()
self.Centre()
self.Show(True)
def catcher(self,arg1):
data = arg1
print(f"<subscribed>{data}</subscribed>\n\n")
return
ex = wx.App()
Base.metadata.create_all(bind=engine)
IntegratorFrame(None,'Praxis:Integrator')
ex.MainLoop()
-- eof --
""" models.py """
from sqlalchemy import *
from sqlalchemy.orm import (scoped_session, sessionmaker, relationship, backref)
from sqlalchemy.ext.declarative import declarative_base
engine = create_engine('sqlite:///.praxis/lexicon/unbound.db3', convert_unicode=True)
db_session = scoped_session(sessionmaker(autocommit=False,
autoflush=False,
bind=engine))
Base = declarative_base()
# We will need this for querying
Base.query = db_session.query_property()
class Idiom(Base):
__tablename__ = "idiomae"
id = Column(Integer, primary_key=True)
src = Column(String) # the text of the drag/paste operation
taxonomy = Column(String) # the type of resource referenced in the drag/paste operation
localblob = Column(String) # local path to media referenced in 'src'
timestamp = Column(DateTime) # date and time of capture
-- eof --
""" schema.py """
import graphene
from graphene import relay
from graphene_sqlalchemy import SQLAlchemyObjectType, SQLAlchemyConnectionField
from models import db_session, Idiom as IdiomaticModel
class Idiom(SQLAlchemyObjectType):
class Meta:
model = IdiomaticModel
interfaces = (relay.Node, )
class Query(graphene.ObjectType):
node = relay.Node.Field()
# Allows sorting over multiple columns, by default over the primary key
all_idioms = SQLAlchemyConnectionField(Idiom.connection)
# Disable sorting over this field
# all_departments = SQLAlchemyConnectionField(Department.connection, sort=None)
schema = graphene.Schema(query=Query)

Answer 1

I see where you tell Flask to be multi-threaded, but I don't see where you're starting up the Flask app in a thread. I expected to see something like
app = Flask(__name__)
# any extra configuration
def webserver():
app.run(use_reloader=False)
web_thread = threading.Thread(target=webserver)
web_thread.start()
... continue on with the main thread
I have a working example you can crib from here. Note the need to use appropriate locking of any data structures shared between the primary thread and the threads running Flask.