env_config = EnvConfig(os.path.join(path, "envconfig.json"))
from idare import KerasIDarePolicy
from rasa_core.featurizers import FloatSingleStateFeaturizer, MaxHistoryTrackerFeaturizer
feat = MaxHistoryTrackerFeaturizer(FloatSingleStateFeaturizer(), max_history=1)
policy = KerasIDarePolicy(feat)
from rasa_core.policies.ensemble import SimplePolicyEnsemble
ensemble = SimplePolicyEnsemble([policy])
# In[5]:
from rasa_core.agent import Agent
from rasa_core.interpreter import NaturalLanguageInterpreter
from rasa_core.tracker_store import InMemoryTrackerStore
_interpreter = NaturalLanguageInterpreter.create('./nlu_model/current')
_tracker_store = Agent.create_tracker_store(None, domain, env_config)
# In[7]:
agent = Agent(domain, ensemble, _interpreter, _tracker_store, env_config)
# In[9]:
from rasa_core.run import create_input_channel
from rasa_core.channels.file import FileInputChannel
#usr_channel=FileInputChannel('./testio.txt')
# In[9]:
from rasa_core.run import create_input_channel
from rasa_core.channels.custom_websocket import CustomInput
def create_app(env_json_file,
loglevel="INFO", # type: Optional[Text]
logfile="rasa_core.log", # type: Optional[Text]
cors_origins=None, # type: Optional[List[Text]]
action_factory=None, # type: Optional[Text]
auth_token=None, # type: Optional[Text]
tracker_store=None # type: Optional[TrackerStore]
):
"""Class representing a Rasa Core HTTP server."""
app = Flask(__name__)
CORS(app, resources={r"/*": {"origins": "*"}})
# Setting up logfile
utils.configure_file_logging(loglevel, logfile)
if not cors_origins:
cors_origins = []
with open(env_json_file) as f:
env_json = json.load(f)
domain_file = env_json['domain']
interpreter_file = env_json['interpreter_dir']
path_to_glove = env_json['path_to_glove']
tracker_store = tracker_store
action_factory = action_factory
# this needs to be an array, so we can modify it in the nested functions...
domain = OntologyDomain.load(os.path.join(path, domain_file),
None)
from rasa_core.featurizers import FloatSingleStateFeaturizer,MaxHistoryTrackerFeaturizer
feat=MaxHistoryTrackerFeaturizer(FloatSingleStateFeaturizer(),max_history=1)
policy = idare.KerasIDarePolicy(feat)
from rasa_core.policies.ensemble import SimplePolicyEnsemble
ensemble = SimplePolicyEnsemble([policy])
ensemble = ensemble.load(env_json['dst_model_dir'])
# In[5]:
from rasa_core.agent import Agent
from rasa_core.interpreter import NaturalLanguageInterpreter
from rasa_core.tracker_store import InMemoryTrackerStore
_interpreter = Interpreter(suprath_dir = interpreter_file, rasa_dir = interpreter_file,
path_to_glove = path_to_glove)
logger.info("NLU interpreter loaded successfully")
_tracker_store = Agent.create_tracker_store(None,domain,env_json)
_agent = [Agent(domain, ensemble, _interpreter, _tracker_store,env_json)]
global processor
feedback_logger = sql_feedback_logger
processor = _agent[0]._create_processor(feedback_logger=feedback_logger)
usr_channel = None
ha_channel = None
teams_channel = None
def agent():
if _agent and _agent[0]:
return _agent[0]
else:
return None
@app.route("/",
methods=['GET', 'OPTIONS'])
@cross_origin(origins=cors_origins)
def hello():
"""Check if the server is running and responds with the version."""
return "hello from Rasa Core: " + __version__
@app.route("/version",
methods=['GET', 'OPTIONS'])
@cross_origin(origins=cors_origins)
def version():
"""respond with the version number of the installed rasa core."""
return jsonify({'version': __version__})
# <sender_id> can be be 'default' if there's only 1 client
@app.route("/run",
methods=['POST', 'OPTIONS'])
@cross_origin(origins=cors_origins)
@requires_auth(auth_token)
@ensure_loaded_agent(agent)
def continue_predicting():
"""Continue a prediction started with parse.
Caller should have executed the action returned from the parse
endpoint. The events returned from that executed action are
passed to continue which will trigger the next action prediction.
If continue predicts action listen, the caller should wait for the
next user message."""
from rasa_core.run import create_input_channel
from rasa_core.channels.custom_websocket import CustomInput
input_component1=CustomInput(None)
input_component2=CustomInput(None)
from rasa_core.channels.websocket import WebSocketInputChannel
global usr_channel
global ha_channel
try:
usr_channel = WebSocketInputChannel(int(env_json["userchannelport"]),None,input_component1,http_ip='0.0.0.0')
botf_input_channel = BotFrameworkInput(
app_id=env_json['teams_app_id'],
app_password=env_json['teams_app_password']
)
teams_channel = HttpInputChannel(int(env_json['userchannelport2']),'/webhooks/botframework',botf_input_channel)
except OSError as e:
logger.error(str(e))
return str(e)
usr_channel.output_channel = input_component1.output_channel
teams_channel.output_channel = botf_input_channel.output_channel
op_agent = agent()
op_agent.handle_custom_processor([usr_channel,teams_channel],usr_channel,processor)
return "ok"
@app.route("/conversations/<sender_id>/tracker/events",
methods=['POST', 'OPTIONS'])
@cross_origin(origins=cors_origins)
@requires_auth(auth_token)
@ensure_loaded_agent(agent)
def append_events(sender_id):
"""Append a list of events to the state of a conversation"""
request_params = request.get_json(force=True)
evts = events.deserialise_events(request_params)
tracker = agent().tracker_store.get_or_create_tracker(sender_id)
for e in evts:
tracker.update(e)
agent().tracker_store.save(tracker)
return jsonify(tracker.current_state())
@app.route("/conversations",
methods=['GET', 'OPTIONS'])
@cross_origin(origins=cors_origins)
@requires_auth(auth_token)
@ensure_loaded_agent(agent)
def list_trackers():
return jsonify(list(agent().tracker_store.keys()))
@app.route("/conversations/<sender_id>/tracker",
methods=['GET', 'OPTIONS'])
@cross_origin(origins=cors_origins)
@requires_auth(auth_token)
@ensure_loaded_agent(agent)
def retrieve_tracker(sender_id):
"""Get a dump of a conversations tracker including its events."""
# parameters
use_history = bool_arg('ignore_restarts', default=False)
should_include_events = bool_arg('events', default=True)
until_time = request.args.get('until', None)
# retrieve tracker and set to requested state
tracker = agent().tracker_store.get_or_create_tracker(sender_id)
if until_time is not None:
tracker = tracker.travel_back_in_time(float(until_time))
# dump and return tracker
state = tracker.current_state(
should_include_events=should_include_events,
only_events_after_latest_restart=use_history)
return jsonify(state)
@app.route("/conversations/<sender_id>/tracker",
methods=['PUT', 'OPTIONS'])
@cross_origin(origins=cors_origins)
@requires_auth(auth_token)
@ensure_loaded_agent(agent)
def update_tracker(sender_id):
"""Use a list of events to set a conversations tracker to a state."""
request_params = request.get_json(force=True)
tracker = DialogueStateTracker.from_dict(sender_id,
request_params,
agent().domain)
agent().tracker_store.save(tracker)
# will override an existing tracker with the same id!
agent().tracker_store.save(tracker)
return jsonify(tracker.current_state(should_include_events=True))
@app.route("/conversations/<sender_id>/parse",
methods=['GET', 'POST', 'OPTIONS'])
@cross_origin(origins=cors_origins)
@requires_auth(auth_token)
@ensure_loaded_agent(agent)
def parse(sender_id):
request_params = request_parameters()
if 'query' in request_params:
message = request_params.pop('query')
elif 'q' in request_params:
message = request_params.pop('q')
else:
return Response(
jsonify(error="Invalid parse parameter specified."),
status=400,
mimetype="application/json")
try:
# Fetches the predicted action in a json format
response = agent().start_message_handling(message, sender_id)
return jsonify(response)
except Exception as e:
logger.exception("Caught an exception during parse.")
return Response(jsonify(error="Server failure. Error: {}"
"".format(e)),
status=500,
content_type="application/json")
@app.route("/conversations/<sender_id>/respond",
methods=['GET', 'POST', 'OPTIONS'])
@cross_origin(origins=cors_origins)
@requires_auth(auth_token)
@ensure_loaded_agent(agent)
def respond(sender_id):
request_params = request_parameters()
if 'query' in request_params:
message = request_params.pop('query')
elif 'q' in request_params:
message = request_params.pop('q')
else:
return Response(jsonify(error="Invalid respond parameter "
"specified."),
status=400,
mimetype="application/json")
try:
# Set the output channel
out = CollectingOutputChannel()
# Fetches the appropriate bot response in a json format
responses = agent().handle_message(message,
output_channel=out,
sender_id=sender_id)
return jsonify(responses)
except Exception as e:
logger.exception("Caught an exception during respond.")
return Response(jsonify(error="Server failure. Error: {}"
"".format(e)),
status=500,
content_type="application/json")
@app.route("/nlu_parse",
methods=['GET', 'OPTIONS'])
@cross_origin(origins=cors_origins)
@requires_auth(auth_token)
@ensure_loaded_agent(agent)
def nlu_parse():
request_params = request_parameters()
if 'query' in request_params:
message = request_params.get('query')
elif 'q' in request_params:
message = request_params.get('q')
else:
return Response(jsonify(error="Invalid respond parameter "
"specified."),
status=400,
mimetype="application/json")
return jsonify(_interpreter.parse(message))
@app.route("/email/<sender_id>/respond",
methods=['GET', 'POST', 'OPTIONS'])
@cross_origin(origins=cors_origins)
@requires_auth(auth_token)
@ensure_loaded_agent(agent)
def email(sender_id):
request_params = request_parameters()
if 'query' in request_params:
message = request_params.get('query')
elif 'q' in request_params:
message = request_params.get('q')
else:
return Response(jsonify(error="Invalid respond parameter "
"specified."),
status=400,
mimetype="application/json")
global teams_channel
inter_channel_mapper = read_json_file(env_json.get('inter_channel_mapper'))
teams_channel.output_channel.id_map.update(inter_channel_mapper)
#temporary code follows
teams_channel.output_channel.id_map.update({sender_id:
inter_channel_mapper[list(inter_channel_mapper.keys())[0]]})
teams_channel.output_channel.reverse_id_map.update({list(inter_channel_mapper.keys())[0]:sender_id})
#temporary code ends
email_id = request_params.get('email_id')
preprocessor = partial(idare.email_preprocessor,email_id=email_id)
try:
# Set the output channel
out = CollectingOutputChannel()
# Fetches the appropriate bot response in a json format
agent().handle_email(message, email_preprocessor = preprocessor,
output_channel=out,
alternate_channel = teams_channel,
sender_id=sender_id)
response = out.latest_output()
return jsonify(response)
except Exception as e:
logger.exception("Caught an exception during respond.")
return Response(jsonify(error="Server failure. Error: {}"
"".format(e)),
status=500,
content_type="application/json")
@app.route("/load_nlu", methods=['POST', 'OPTIONS'])
@requires_auth(auth_token)
@cross_origin(origins=cors_origins)
def load_nlu_model():
"""Loads a zipped model, replacing the existing one."""
if 'nlu_model' not in request.files:
# model file is missing
abort(400)
model_file = request.files['nlu_model']
logger.info("Received new nlu_model through REST interface.")
zipped_path = tempfile.NamedTemporaryFile(delete=False, suffix=".zip")
zipped_path.close()
model_file.save(zipped_path.name)
logger.debug("Downloaded model to {}".format(zipped_path.name))
zip_ref = zipfile.ZipFile(zipped_path.name, 'r')
zip_ref.extractall(interpreter_file)
zip_ref.close()
logger.debug("Unzipped model to {}".format(
os.path.abspath(interpreter_file)))
global processor
del processor.interpreter
_interpreter.reload()
processor.interpreter = _interpreter
agent().interpreter = _interpreter
logger.debug("Finished loading new interpreter.")
return jsonify({'success': 1})
@app.route("/load_idare", methods=['GET', 'OPTIONS'])
@requires_auth(auth_token)
@cross_origin(origins=cors_origins)
def load_idare():
"""Reload idare."""
from imp import reload
global idare
try:
idare = reload(idare)
except Exception as e:
return str(e)
return jsonify({'success': 1})
return app
feat = MaxHistoryTrackerFeaturizer(FloatSingleStateFeaturizer(), max_history=2)
policy = KerasIDarePolicy(feat)
from rasa_core.policies.ensemble import SimplePolicyEnsemble
ensemble = SimplePolicyEnsemble([policy])
# In[5]:
from rasa_core.agent import Agent
from suprath_nlu.wrapper import Interpreter
from rasa_core.tracker_store import InMemoryTrackerStore
_interpreter = Interpreter(
'./nlu_model/default/current', './nlu_model/default/current',
'C:/task/rasaextension/coreextension/examples/glove.6B/glove.6B.50d.txt')
#_tracker_store = InMemoryTrackerStore(domain)
_tracker_store = Agent.create_tracker_store(None, domain, None)
#print(_tracker_store)
#print(_tracker_store.red)
# In[7]:
agent = Agent(domain, ensemble, _interpreter, _tracker_store)
# Training
training_data = agent.load_data('./stories.md')
agent.train(training_data, epochs=50)
# In[9]:
from rasa_core.run import create_input_channel
