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Simple interface for working with intents and chatbots. A built upon version of Neuralnines package.

Project description

neuralintents

Still in a buggy alpha state.

Setting Up A Basic Assistant

from neuralintents import GenericAssistant



assistant = GenericAssistant('intents.json', model_name="test_model")

assistant.train_model()

assistant.save_model()



done = False



while not done:

    message = input("Enter a message: ")

    if message == "STOP":

        done = True

    else:

        assistant.request(message)

Binding Functions To Requests

from neuralintents import GenericAssistant



def function_for_greetings():

    print("You triggered the greetings intent!")

    # Some action you want to take



def function_for_stocks():

    print("You triggered the stocks intent!")

    # Some action you want to take



mappings = {'greeting' : function_for_greetings, 'stocks' : function_for_stocks}



assistant = GenericAssistant('intents.json', intent_methods=mappings ,model_name="test_model")

assistant.train_model()

assistant.save_model()



done = False



while not done:

    message = input("Enter a message: ")

    if message == "STOP":

        done = True

    else:

        assistant.request(message)

Sample intents.json File

{"intents": [

  {"tag": "greeting",

    "patterns": ["Hi", "How are you", "Is anyone there?", "Hello", "Good day", "Whats up", "Hey", "greetings"],

    "responses": ["Hello!", "Good to see you again!", "Hi there, how can I help?"],

    "context_set": ""

  },

  {"tag": "goodbye",

    "patterns": ["cya", "See you later", "Goodbye", "I am Leaving", "Have a Good day", "bye", "cao", "see ya"],

    "responses": ["Sad to see you go :(", "Talk to you later", "Goodbye!"],

    "context_set": ""

  },

  {"tag": "stocks",

    "patterns": ["what stocks do I own?", "how are my shares?", "what companies am I investing in?", "what am I doing in the markets?"],

    "responses": ["You own the following shares: ABBV, AAPL, FB, NVDA and an ETF of the S&P 500 Index!"],

    "context_set": ""

  }

]

}

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