🤖 Coeus - EARIST A.C.E 💬

Coeus is an Artificial Conversational Entity for queries in Eulogio "Amang" Rodriguez Institute of Science and Technology, using Open Source Machine Learning Framework RASA NLU.

Implementation

⚠️ Before Cloning this project make sure you have python and pip installed on your machine.

• Machine Learning Framework Used: RASA https://rasa.com/docs/rasa/
• Repository Software for Python: PythonPackageIndex https://pypi.org/
  

✔️ Check if Pip is Already Installed: Open a command prompt type the command below:

pip --version

✔️ Check if Python is Already Installed: Open a command prompt type the command below:

python --version

✔️ Confirm that Python is installed: Open a command prompt type python then hit enter.
If Python is installed correctly, you should see output similar to what is shown below.

Python 3.7.0 (v3.7.0:1bf9cc5093, Jun 27 2018, 04:59:51) [MSC v.1914 64 bit (AMD64)] on win32
 Type "help", "copyright", "credits" or "license" for more information.

Usage

⚠️ Before proceeding to this step, make sure that python and pip are installed.

I. Virtual Environment Setup

Create a new virtual environment by choosing a Python interpreter and making a .\venv directory to hold it:

python3 -m venv ./venv

Activate the virtual environment:

source ./venv/bin/activate

test addition

II. Quick Installation of RASA Open Source

pip install rasa

III. Clone Repository and Train/Run

git clone https://github.com/skedaddl3/Coeus-A.C.E.git

After cloning this repository open a prompt/terminal inside the directory where the files are located.

Train Model and Test it on your own machine: Run Command below if rasa run did not work.

Make sure that you're running this command inside the COEUS-A.C.E directory.

Trained Model is not included in this repository.

Git Large File is not used in this repository. That's why the repository does not contain any pre-trained model.

In order to create model in your local machine run or type the command below:

rasa train

Wait for the model to be trained, it usually takes 10-20+ minutes depending on the specifications of your machine.

After model training, Test it in your terminal, run:

rasa shell

Front-End Widget Used: RASA Webchat https://github.com/botfront/rasa-webchat.git

<script>!(function () {
    let e = document.createElement("script"),
      t = document.head || document.getElementsByTagName("head")[0];
    (e.src =
      "https://cdn.jsdelivr.net/npm/rasa-webchat@1.0.0/lib/index.js"),
      (e.async = !0),
      (e.onload = () => {
        window.WebChat.default(
          {
            selector: "#webchat",
            initPayload: "/hello",
            customData: { language: "en" },
            socketUrl: "http://localhost:5005",
            title: "Coeus",
            subtitle: "Earist Artificial Conversational Entity"
          },
          null
        );
      }),
      t.insertBefore(e, t.firstChild);
  })();
  </script>

If you have a Front-End website to run the bot, Embed the Script above in your local website. And enter the command:

rasa run --m ./models --endpoints endpoints.yml --port 5005 -vv --enable-api --cors “*”

Support this project by buying the dev a cup of coffee ☕