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https://github.com/katanaml/katana-assistant

Text based assistant powered by Machine Learning and NLP
https://github.com/katanaml/katana-assistant

javascript jupyter-notebook keras machine-learning neural-network python tensorflow

Last synced: 1 day ago
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Text based assistant powered by Machine Learning and NLP

Host: GitHub
URL: https://github.com/katanaml/katana-assistant
Owner: katanaml
License: apache-2.0
Created: 2019-04-02T15:46:29.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2022-12-09T18:06:30.000Z (almost 2 years ago)
Last Synced: 2024-05-01T10:10:33.821Z (7 months ago)
Topics: javascript, jupyter-notebook, keras, machine-learning, neural-network, python, tensorflow
Language: JavaScript
Homepage: http://katanaml.io
Size: 5.96 MB
Stars: 99
Watchers: 6
Forks: 61
Open Issues: 22
Metadata Files:
- Readme: README.MD
- Funding: .github/FUNDING.yml
- License: LICENSE

Awesome Lists containing this project

README

# Katana Assistant

Machine Learning based agent, helps to enable business automation.

Technology: TensorFlow, Keras, Flask, Python, Node.js, JavaScript

Author: Katana, Red Samurai Consulting, Andrejus Baranovskis

## Instructions

#### - Machine Learning

Install TensorFlow

```
pip install tensorflow
```

Install Keras

```
pip install keras
```

Model code is located in mlmodels folder.

Sample set of intents is available in the file mlmodels/intents.json. There is pre-built model in mlmodels/katana-assistant-model.pkl. If you want to rebuild model - run Jupyter notebook katana-assistant-model.ipynb

To start Katana assistant model endpoint in the background process run it with PM2 manager:

```
pm2 start katana-assistant-endpoint.py
```

This will start endpoint on port 5001

#### - Node.js Backend

Backend code is located in mlbackend folder.

Run backend with PM2 manager on port 3000:

```
PORT=3000 pm2 start -l 0 ./bin/www
```

Socket.IO endpoint will be started on port 8000. Check mlbackend/routes/assistant.js

#### - JavaScript Frontend

Frontend code is located in mlfrontend folder.

UI client is implemented with [Oracle JET](https://www.oracle.com/webfolder/technetwork/jet/index.html). Follow instructions to install Oracle JET.

Navigate to folder mlfrontend/socketiojet and run this command to setup required libraries:

```
ojet restore
```

Run UI client:

```
ojet --server-port=8010 serve
```

This will start frontend on port 8010.

## License