def test_get_agent(self, monkeypatch):
def mongo_store(*arge, **kwargs):
return None
monkeypatch.setattr(Utility, "get_local_mongo_store", mongo_store)
agent = AgentProcessor.get_agent("tests")
assert isinstance(agent, Agent)
async def chat(request_data: TextData,
current_user: User = Depends(auth.get_current_user)):
""" This function returns a bot response for a given text/query. It is basically
used to test the chat functionality of the bot """
model = AgentProcessor.get_agent(current_user.get_bot())
response = await model.handle_text(request_data.data,
sender_id=current_user.get_user())
return {"data": {"response": response[0]["text"] if response else None}}
async def predict_intent(request_data: TextData,
current_user: User = Depends(auth.get_current_user)):
model = AgentProcessor.get_agent(current_user.get_bot())
response = await model.parse_message_using_nlu_interpreter(
request_data.data)
intent = response.get("intent").get("name") if response else None
confidence = response.get("intent").get("confidence") if response else None
return {"data": {"intent": intent, "confidence": confidence}}
async def predict_intent(request_data: TextData,
current_user: User = Depends(auth.get_current_user)):
""" This function returns the predicted intent of the entered text by using the trained
rasa model of the chatbot """
model = AgentProcessor.get_agent(current_user.get_bot())
response = await model.parse_message_using_nlu_interpreter(
request_data.data)
intent = response.get("intent").get("name") if response else None
confidence = response.get("intent").get("confidence") if response else None
return {"data": {"intent": intent, "confidence": confidence}}
def test_get_agent_from_cache_does_not_exists(self):
with pytest.raises(AppException):
agent = AgentProcessor.get_agent("test")
assert isinstance(agent, Agent)
def test_get_agent_from_cache(self):
agent = AgentProcessor.get_agent("tests")
assert isinstance(agent, Agent)
async def chat(request_data: TextData,
current_user: User = Depends(auth.get_current_user)):
model = AgentProcessor.get_agent(current_user.get_bot())
response = await model.handle_text(request_data.data)
return {"data": {"response": response[0]["text"] if response else None}}