async def chat(request_data: TextData,
current_user: User = Depends(auth.get_current_user)):
"""
Fetches a bot response for a given text/query.
It is basically used to test the chat functionality of the agent
"""
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}}
async def predict_intent(request_data: TextData,
current_user: User = Depends(auth.get_current_user)):
"""
Fetches the predicted intent of the entered text form the loaded agent
"""
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 chat(
request_data: TextData, current_user: User = Depends(auth.get_current_user)
):
"""
Fetches a bot response for a given text/query.
It is basically used to test the chat functionality of the agent
"""
if Utility.environment.get('model') and Utility.environment['model']['train'].get('agent_url'):
agent_url = Utility.environment['model']['train'].get('agent_url')
token = auth.create_access_token(data={"sub": current_user.email})
response = Utility.http_request('post', urljoin(agent_url, "/api/bot/chat"), token.decode('utf8'), current_user.get_user(), json={'data': request_data.data})
else:
model = AgentProcessor.get_agent(current_user.get_bot())
response = await model.handle_text(
request_data.data, sender_id=current_user.get_user()
)
response = {"data": {"response": response}}
return response