GithubHelp home page GithubHelp logo

tjas / postgrad-ai-nlp-chatbot Goto Github PK

View Code? Open in Web Editor NEW
0.0 2.0 0.0 18 KB

Brazilian Portuguese chatbot built with Python and Rasa NLU Framework to solve the proposed exercise in "Cognitive Computing: Chatbots" discipline.

License: MIT License

Python 100.00%
ai artificial-intelligence artificial-neural-networks chat-bot chatbot chatbots python rasa rasa-chatbot rasa-nlu virtual-assistant machine-learning natural-language-processing natural-language-understanding nlp nlp-machine-learning nlu nlu-chatbot

postgrad-ai-nlp-chatbot's Introduction

postgrad-ai-nlp-chatbot

Status Hits Licence Commits Last commit Repo size Code size Stars Watchers Forks

Python Rasa

⭐ Mark the project with a star. 👀 Watch the project for receive news.

🇧🇷 Acesse esta página em Português do Brasil.

Artificial Intelligence postgraduation's Cognitive Computing: Chatbots discipline exercise using Python and Rasa NLU Framework to build a Brazilian Portuguese chatbot. The postgraduate course was held at Centro de Educação Superior de Brasília (IESB) and the referred discipline took place in 2020.

Proposed Exercise

Create a chatbot using the Rasa Framework, with the same characteristics as the first exercise (performed in Watson Assitant, to build a chatbot similar to the one built in this project, but not publicly available), meeting the following requirements:

  • Basic structure: Greeting, Ending, Action, Other questions (optional);
  • Run at least one custom action within a form flow that connects to an external service (using Python requests);
  • The pipeline must be configured allowing training of the chatbot in Brazilian Portuguese;
  • Excellence in configuration and training will not be charged, but the training must be sufficient for the dialog flows to be executed;
  • It is not necessary to integrate with any Front-end messenger, just deliver the Rasa Framework project;
  • Submit the Rasa Framework project folder without the model files. To deliver, delete the files from the model folder, zip and send it. If you wish, you can version the files on GitHub.

Instructions to Run the Project Locally

Example using Linux operational system.

This is an example of how you may set up the project locally in your computer. We strongly recommended that you use virtual environments to run the application, we recommend Virtualenv (or any other of your choice). Read documentation, create and activate the virtual environment inside the project folder before steps 6.

To get a local copy up and running follow these steps:

  1. Make sure you have Python 3.8 installed or do it from Python.org or from Anaconda;
  2. Make sure you have Git installed or do it from Git-scm.com;
  3. Access the folder you want to save the project, then clone the repo there
    git clone https://github.com/tjas/postgrad-ai-nlp-chatbot
  4. Access the project folder;
  5. Create and activate the virtual environment
    virtualenv venv --python=/usr/local/bin/python3.8
    source venv/bin/activate
  6. Install the project dependencies
    pip install -r requirements.txt
  7. Train the model
    rasa train
  8. Run the Action Server:
    rasa run actions
  9. Run the Rasa Shell:
    rasa shell

Contact

Thiago Jorge Almeida dos Santos, project author and maintainer.

LinkedIn YouTube Instagram Website GitHub

Licence

postgrad-ai-nlp-chatbot's People

Contributors

tjas avatar

Watchers

 avatar  avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.