def test_all_classifier_are_unique_for_incremental_builds(home_assistant_app_path):
nlp = NaturalLanguageProcessor(home_assistant_app_path)
nlp.build(incremental=True)
example_cache = os.listdir(
MODEL_CACHE_PATH.format(app_path=home_assistant_app_path)
)[0]
unique_hashs = set()
for domain in nlp.domains:
for intent in nlp.domains[domain].intents:
_, cached_path = get_entity_model_paths(
home_assistant_app_path, domain, intent, timestamp=example_cache
)
hash_val = open(cached_path + ".hash", "r").read()
assert hash_val not in unique_hashs
unique_hashs.add(hash_val)
for entity in (
nlp.domains[domain].intents[intent].entity_recognizer.entity_types
):
_, cached_path = get_role_model_paths(
home_assistant_app_path,
domain,
intent,
entity,
timestamp=example_cache,
)
hash_val = open(cached_path + ".hash", "r").read()
assert hash_val not in unique_hashs
unique_hashs.add(hash_val)
def test_model_accuracies_are_similar_before_and_after_caching(home_assistant_app_path):
# clear model cache
model_cache_path = MODEL_CACHE_PATH.format(app_path=home_assistant_app_path)
try:
shutil.rmtree(MODEL_CACHE_PATH.format(app_path=home_assistant_app_path))
except FileNotFoundError:
pass
# Make sure no cache exists
assert os.path.exists(model_cache_path) is False
nlp = NaturalLanguageProcessor(home_assistant_app_path)
nlp.build(incremental=True)
nlp.dump()
entity_eval = (
nlp.domains["times_and_dates"]
.intents["change_alarm"]
.entity_recognizer.evaluate()
)
role_eval = (
nlp.domains["times_and_dates"]
.intents["change_alarm"]
.entities["sys_time"]
.role_classifier.evaluate()
)
entity_accuracy_no_cache = entity_eval.get_accuracy()
role_accuracy_no_cache = role_eval.get_accuracy()
example_cache = os.listdir(
MODEL_CACHE_PATH.format(app_path=home_assistant_app_path)
)[0]
nlp = NaturalLanguageProcessor(home_assistant_app_path)
nlp.load(example_cache)
# make sure cache exists
assert os.path.exists(model_cache_path) is True
entity_eval = (
nlp.domains["times_and_dates"]
.intents["change_alarm"]
.entity_recognizer.evaluate()
)
role_eval = (
nlp.domains["times_and_dates"]
.intents["change_alarm"]
.entities["sys_time"]
.role_classifier.evaluate()
)
entity_accuracy_cached = entity_eval.get_accuracy()
role_accuracy_cached = role_eval.get_accuracy()
assert role_accuracy_no_cache == role_accuracy_cached
assert entity_accuracy_no_cache == entity_accuracy_cached
def test_model_cache_files_present_after_second_incremental_build(
kwik_e_mart_app_path):
# clear model cache
model_cache_path = MODEL_CACHE_PATH.format(app_path=kwik_e_mart_app_path)
try:
shutil.rmtree(MODEL_CACHE_PATH.format(app_path=kwik_e_mart_app_path))
except FileNotFoundError:
pass
# Make sure no cache exists
assert os.path.exists(model_cache_path) is False
nlp = NaturalLanguageProcessor(kwik_e_mart_app_path)
nlp.build(incremental=True)
nlp.dump()
initial_timestamp = nlp.incremental_timestamp
nlp = NaturalLanguageProcessor(kwik_e_mart_app_path)
nlp.build(incremental=True)
nlp.dump()
new_timestamp = nlp.incremental_timestamp
nlp.load(initial_timestamp)
nlp.load(new_timestamp)
def test_model_accuracies_are_similar_before_and_after_caching(
kwik_e_mart_app_path):
# clear model cache
model_cache_path = MODEL_CACHE_PATH.format(app_path=kwik_e_mart_app_path)
try:
shutil.rmtree(MODEL_CACHE_PATH.format(app_path=kwik_e_mart_app_path))
except FileNotFoundError:
pass
# Make sure no cache exists
assert os.path.exists(model_cache_path) is False
nlp = NaturalLanguageProcessor(kwik_e_mart_app_path)
nlp.build(incremental=True)
nlp.dump()
intent_eval = nlp.domains["store_info"].intent_classifier.evaluate()
entity_eval = (nlp.domains["store_info"].intents["get_store_hours"].
entity_recognizer.evaluate())
intent_accuracy_no_cache = intent_eval.get_accuracy()
entity_accuracy_no_cache = entity_eval.get_accuracy()
example_cache = os.listdir(
MODEL_CACHE_PATH.format(app_path=kwik_e_mart_app_path))[0]
nlp = NaturalLanguageProcessor(kwik_e_mart_app_path)
nlp.load(example_cache)
# make sure cache exists
assert os.path.exists(model_cache_path) is True
intent_eval = nlp.domains["store_info"].intent_classifier.evaluate()
entity_eval = (nlp.domains["store_info"].intents["get_store_hours"].
entity_recognizer.evaluate())
intent_accuracy_cached = intent_eval.get_accuracy()
entity_accuracy_cached = entity_eval.get_accuracy()
assert intent_accuracy_no_cache == intent_accuracy_cached
assert entity_accuracy_no_cache == entity_accuracy_cached
def test_df_converter():
df_project_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"dialogflow_sample_project")
# This is the dialogflow app converted to mindmeld app
mm_df_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"mm_df_converted_project")
df_init = DialogflowConverter(df_project_path, mm_df_path)
df_init.convert_project()
mm_df_nlp = NaturalLanguageProcessor(app_path=mm_df_path)
mm_df_nlp.build()
# check to make sure the NLP object contains the correct hierarchy
assert set(mm_df_nlp.domains.keys()) == {"app_specific", "unrelated"}
assert set(mm_df_nlp.domains["app_specific"].intents.keys()) == {
"accountopen_en",
"accountbalancecheck_en",
"accountearningcheck_context__earning_date_en",
"transfermoney_no_en",
"accountbalancecheck_context__account_en",
"transfermoney_yes_en",
"transfermoney_en",
"transferamountcheck_en",
"paymentdue_date_en",
"accountspendingcheck_context__spending_date_en",
"transferdatecheck_en",
"accountspendingcheck_en",
"transfersendercheck_en",
"accountbalancecheck_context__balance_en",
"accountearningcheck_en",
}
assert set(mm_df_nlp.domains["unrelated"].intents.keys()) == {
"default_welcome_intent_en",
"default_fallback_intent_en",
}
entities = set()
for domain in mm_df_nlp.domains:
for intent in mm_df_nlp.domains[domain].intents:
for entity in mm_df_nlp.domains[domain].intents[intent].entities:
entities.add(entity)
for expected_entity in {
"category_en",
"transfer_type_en",
"merchant_en",
"account_en",
}:
assert expected_entity in entities
mm_df_app = importlib.import_module("mm_df_converted_project").app
mm_df_app.lazy_init(mm_df_nlp)
conv = TestConversation(app=mm_df_app)
conv.process("what is my balance")
conv.assert_text("Here's your latest balance:")
conv.assert_domain("app_specific")
conv.assert_intent("accountbalancecheck_en")
conv.assert_frame({})
conv.process("when is the due date")
conv.assert_text("The due date is:")
conv.assert_domain("app_specific")
conv.assert_intent("paymentdue_date_en")
conv.assert_frame({})
conv.process("transfer money")
conv.assert_text("Sure. Transfer from which account?")
conv.process("checking account")
conv.assert_text("To which account?")
conv.process("transfer to savings account")
conv.assert_text("And, how much do you want to transfer?")
conv.process("transfer $200")
conv.assert_text(
"All right. So, you're transferring $200 from your checking to a savings. Is that right?"
)
conv.process("hello!")
conv.assert_text([
"Hello, thanks for choosing ACME Bank.", "Hello. Welcome to ACME Bank."
])
conv.process("I dont know what the laptop")
conv.assert_text([
"Sorry, I didn’t get that.",
"I'm afraid I don't understand.",
"Sorry, say that again?",
"Sorry, can you say that again?",
"I didn't get that. Can you say it again?",
"Sorry, could you say that again?",
"Sorry, can you tell me again?",
"Sorry, tell me one more time?",
"Sorry, can you say that again?",
])
# delete generated files
shutil.rmtree(mm_df_path)
# -*- coding: utf-8 -*-
import os
from flask import Flask
from mindmeld.components import NaturalLanguageProcessor
from mindmeld.components.dialogue import Conversation
from mindmeld.bot import WebexBotServer
from mindmeld import configure_logs
if __name__ == '__main__':
app = Flask(__name__)
# Create web hook here: https://developer.webex.com/docs/api/v1/webhooks/create-a-webhook
WEBHOOK_ID = os.environ.get('WEBHOOK_ID')
# Create bot access token here: https://developer.webex.com/my-apps/new
ACCESS_TOKEN = os.environ.get('BOT_ACCESS_TOKEN')
configure_logs()
nlp = NaturalLanguageProcessor('.')
nlp.build()
conv = Conversation(nlp=nlp, app_path='.')
server = WebexBotServer(app, WEBHOOK_ID, ACCESS_TOKEN, conv)
port_number = 8080
print('Running server on port {}...'.format(port_number))
server.run(host='localhost', port=port_number)
def home_assistant_nlp(home_assistant_app_path):
"""Provides a built processor instance"""
nlp = NaturalLanguageProcessor(app_path=home_assistant_app_path)
nlp.build()
nlp.dump()
return nlp
def kwik_e_mart_nlp(kwik_e_mart_app_path):
"""Provides a built processor instance"""
nlp = NaturalLanguageProcessor(app_path=kwik_e_mart_app_path)
nlp.build()
nlp.dump()
return nlp
def food_ordering_nlp(food_ordering_app_path):
"""Provides a built processor instance"""
nlp = NaturalLanguageProcessor(app_path=food_ordering_app_path)
nlp.build()
nlp.dump()
return nlp