Purpose: To provide round-the-clock customer service that can efficiently handle common queries and issues, improving customer satisfaction and freeing up staff for more complex tasks.
Requirements:
- An AI-based text chatbot integrated into the web application.
- Pre-programmed responses for common customer queries like "What is your operation hours?", "What is the status of my order?", etc.
- User-friendly interface for customers to interact with the chatbot.
Metrics for success: Decrease in basic customer inquiries handled by human staff and positive user feedback about the chatbot.
- Serve only the Flask prediction API. The used html and javascript files can be included in any Frontend application (with only a slight modification) and can run completely separate from the Flask App then.
Clone repo and create a virtual environment
$ git clone
$ cd chatbot-deployment
$ python3 -m venv venv
$ . venv/bin/activate
Install dependencies
$ (venv) pip install Flask torch torchvision nltk
Install nltk package
$ (venv) python
>>> import nltk
>>> nltk.download('punkt')
Modify intents.json with different intents and responses for your Chatbot
Run
$ (venv) python train.py
This will dump data.pth file. And then run the following command to test it in the console.
$ (venv) python chat.py
Now for deployment follow my tutorial to implement app.py and app.js.