https://github.com/varunon9/chat-reply-suggestions

Auto reply suggestions to chat messages/emails (like gmail and linkedin) built using rasa_nlu framework.
https://github.com/varunon9/chat-reply-suggestions

chat-reply chatbot nlp rasa rasa-nlu

Last synced: over 1 year ago
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Auto reply suggestions to chat messages/emails (like gmail and linkedin) built using rasa_nlu framework.

• Host: GitHub
• URL: https://github.com/varunon9/chat-reply-suggestions
• Owner: varunon9
• License: mit
• Created: 2018-06-19T18:10:39.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2018-06-20T17:22:23.000Z (about 8 years ago)
• Last Synced: 2025-03-16T17:39:04.448Z (over 1 year ago)
• Topics: chat-reply, chatbot, nlp, rasa, rasa-nlu
• Language: JavaScript
• Homepage:
• Size: 659 KB
• Stars: 15
• Watchers: 3
• Forks: 2
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

          
# chat-reply-suggestions

Auto reply suggestions to chat messages/emails built using rasa_nlu framework.

### How to install and train chatbot

1. Make sure you have git, python, virtualenv and npm installed

2. Clone the repository `git clone https://github.com/varunon9/chat-reply-suggestions.git`

3. Go to project directory `cd chat-reply-suggestions/`

4. Create a virtual environment `virtualenv venv`

5. Activate virtual environment `source venv/bin/activate`

6. Install Rasa NLU as well as spacy `pip install rasa_nlu[spacy] --default-timeout=100`

7. Install language model for the english language `python -m spacy download en_core_web_md`

8. Link to model data `python -m spacy link en_core_web_md en`

9. Install some additional dependencies `pip install -r requirements.txt`

10. Place your nlu_data.json file inside data directory (a sample file is provided).

11. Train the intent models using below command 

12. `python -m rasa_nlu.train --config config_spacy.yml --data data/nlu_data.json --path projects`

13. Install node dependencies `npm install`

14. Start nlu server in current terminal `python -m rasa_nlu.server --path projects`

15. Open another terminal in same project directory (no need to activate virtual environment)

16. Start the app `node index.js`

### How to improve efficiency

- The above app uses sample data (collected by me) for demo. Bot can be made more efficient by 

  training with more real world data.

- Currently suggestions are bunch of hardcoded arrays based on intent. Entity recognition can be 

  done and suggestions can be generated on the fly.

## Screenshots

|  |

| --- |

|![Chat suggestions](./screenshots/chat-suggestions.png) |

#### To see working demo on youtube watch this video:

* https://youtu.be/MCBmbJyoamY

###### For query/issues create Github issue or contact: varunon9@gmail.com