def load_model(self, model_path, session_id):
from shutil import copyfile
endpoints_file = './database_files/try_now_endpoints.yml'
logger.debug("Making Temporary Try now model Path ")
model_home_path = "/".join(model_path.split('/')[:-1]) + "/" + session_id
os.mkdir(model_home_path)
model_name = model_path.split('/')[-1]
try_now_model_path = model_home_path + "/" + model_name
copyfile(model_path, try_now_model_path)
self.agent = create_agent(try_now_model_path, endpoints=endpoints_file)
return {"Status": "Success", "Message": "Agent Loaded"}
def chat(
model_path: Optional[Text] = None,
endpoints: Optional[Text] = None,
agent: Optional["Agent"] = None,
interpreter: Optional[NaturalLanguageInterpreter] = None,
) -> None:
"""Chat to the bot within a Jupyter notebook.
Args:
model_path: Path to a combined Rasa model.
endpoints: Path to a yaml with the action server is custom actions are defined.
agent: Rasa Core agent (used if no Rasa model given).
interpreter: Rasa NLU interpreter (used with Rasa Core agent if no
Rasa model is given).
"""
if model_path:
from rasa.run import create_agent
agent = create_agent(model_path, endpoints)
elif agent is not None and interpreter is not None:
# HACK: this skips loading the interpreter and directly
# sets it afterwards
nlu_interpreter = RasaNLUInterpreter(
"skip this and use given interpreter", lazy_init=True
)
nlu_interpreter.interpreter = interpreter
agent.interpreter = interpreter
else:
print_error(
"You either have to define a model path or an agent and an interpreter."
)
return
print("Your bot is ready to talk! Type your messages here or send '/stop'.")
loop = asyncio.get_event_loop()
while True:
message = input()
if message == "/stop":
break
responses = loop.run_until_complete(agent.handle_text(message))
for response in responses:
_display_bot_response(response)
def chat(
model_path: Text = None,
agent: "Agent" = None,
interpreter: NaturalLanguageInterpreter = None,
) -> None:
"""Chat to the bot within a Jupyter notebook.
Args:
model_path: Path to a Rasa Stack model.
agent: Rasa Core agent (used if no Rasa Stack model given).
interpreter: Rasa NLU interpreter (used with Rasa Core agent if no
Rasa Stack model is given).
"""
if model_path:
from rasa.run import create_agent
unpacked = model.get_model(model_path)
agent = create_agent(unpacked)
elif agent and interpreter:
# HACK: this skips loading the interpreter and directly
# sets it afterwards
nlu_interpreter = RasaNLUInterpreter(
"skip this and use given interpreter", lazy_init=True)
nlu_interpreter.interpreter = interpreter
agent.interpreter = interpreter
else:
print_error(
"You either have to define a model path or an agent and an interpreter."
)
print(
"Your bot is ready to talk! Type your messages here or send '/stop'.")
loop = asyncio.get_event_loop()
while True:
message = input()
if message == "/stop":
break
responses = loop.run_until_complete(agent.handle_text(message))
for response in responses:
_display_bot_response(response)
import speech_recognition as sr
import asyncio
import pprint as pretty_print
import typing
from typing import Any, Dict, Text, Optional
from rasa.cli.utils import print_success, print_error
from rasa.core.interpreter import NaturalLanguageInterpreter, RasaNLUInterpreter
import rasa.model as model
from rasa.run import create_agent
model_path = 'model'
agent = create_agent(model_path)
loop = asyncio.get_event_loop()
while True:
r = sr.Recognizer()
with sr.Microphone() as source:
print('say something')
audio = r.listen(source)
res = r.recognize_google(audio)
print(res)
message = res
if message == "/stop":
break
responses = loop.run_until_complete(agent.handle_text(message))
for response in responses:
print(str(response) + '\n')