After installing chatterbot i have trained the bot for some couple of times through corpus data provided within the chatterbot and also using some of my own conversation list. After that when i trained the bot with some corpus data written by me. Then when i am trying to talk with the bot it is answering from its knowledge base. So if somebody could tell me where can i find this knowledge base data or how can i clear the knowledge base data created by chatterbot
ChatterBot stores the conversational dialog it learns in whatever database you are using it with.
One common way to remove all content from the database is like this:
chatbot = ChatBot("...")
chatbot.storage.drop()
The 'drop' function will empty the content from the database your chat bot is using.
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I have developed my chatbot in PyTorch framework for college purposes. The model is working fine with log loss value of 0.5 and is able to answer questions appropriately. I have seen few productionization suggestions like fast.ai, flask and Django. But I want the model to be deployed on Google Assistant so that my end users can utilize the service of bot without any external installations. How do I have to integrate my PyTorch model to the Google Assistant on Dialogflow ?
Google has published a series of Codelabs to help developers start building actions for the Google Assistant. Each module can be taken standalone or in a learning sequence with other modules.
In each module, the codelabs provide you with end-to-end instructions on how to build Actions from given software requirements and how to test your code. They also teach the necessary concepts and best practices for implementing Actions that give users high-quality conversational experiences.
You can start here.
After learning database - A, I want to delete learned things once and then learn database - B.
However, in the method I tried, I can not erase what I learned once.
I will learn both database - A and database - B.
What you want to hear
ยท How to delete learned data
I upload chatterbot to web using django.
Use python 3
I am a Japanese and I use a translation site, so it may be a bit confusing question.
To delete all dialog data (statements, responses, and tags) from a chat bot's database, the drop method can be called on the storage adapter for the ChatBot instance.
Here is a brief example:
from chatterbot import ChatBot
chatbot = ChatBot('Example Bot')
chatbot.storage.drop()
In addition to the programmatic method of deletion, because you are using Django, you might also find the Django Admin convenient for deleting and modifying individual responses. More information on the Django admin can be found in their documentation.
https://docs.djangoproject.com/en/2.1/ref/contrib/admin/
I'm working on a music player for the Google Assistant Action. Are there pre-built agents for Intents and Trainingsphases available for languages other than English?
It seems possible to upload a JSON file with intents.
Are there resource available for spanish and/or german intents?
I would suggest you to take Intents from pre-built intents, from each intent fetch the training phrase and translate them into desired language then compile intent of your own.
This process requires interacting with Dialogflow using rest API's.
This reference page will help you understand different required API's.
Also, as you said, it is possible to upload json of intents so maybe convert the translated intents into json file and upload them manually.
I have created and saved weka model that predicts disease according to the symptoms. So i want to integrate that model to a web application. The model should be accessed from the web app. That means user enters the symptoms from the web app and model should predict the result. How to do this. Is there any way to use saved weka model as API?
I could not find a good documentation for the Java API, but I did find some examples of using it in Java. This link will unfortunately be broken soon as it looks like wikispaces is closing down.
I need a very simple NLP engine that can do the following:
Tell if the user answered Yes or No (in any way - including variations)
Tell if the user has just said Hello (including all variations like Hi, hi, hey, helllooo, hey there, etc...)
The built-in NLP feature uses wit.ai, and is about as simple as it gets. When you enable it, it automatically includes an nlp object with every message.
https://developers.facebook.com/docs/messenger-platform/built-in-nlp