DineBot : Streamlining Food Orders with NLP

Welcome to the DineBot project! This repository contains the code and resources for building an end-to-end intelligent chatbot designed to streamline food orders for culinary websites. The project leverages Dialogflow for natural language understanding, FastAPI for backend services, and MySQL for order management.

Table of Contents

• Project Overview
• Directory Structure
• Tech Stacks
• Installation
• Backend Server Setup
• Ngrok for HTTPS Tunneling
• Future Scope
• Contributing
• License
• Conclusion
• References

Project Overview

In this project, we configured a chatbot using Dialogflow for natural language understanding. We built a backend server using Python and FastAPI to interact with a MySQL database for order placement and retrieval. The chatbot was integrated with a food website to enable seamless user interactions and efficient order management.

Tech Stacks Involved

• Natural Language Processing (NLP): Dialogflow
• Backend Development: Python, FastAPI
• Database Management: MySQL
• Frontend Development: HTML, CSS, JavaScript
• HTTPS Tunneling: Ngrok

Directory Structure

DineBot/
├── backend/              # Contains Python FastAPI backend code
├── db/                   # Contains the dump of the MySQL database
├── dialogflow_assets/    # Contains training phrases and intents for Dialogflow
└── frontend/             # Contains the website code

Installation

First, clone the repository:

git clone https://github.com/yourusername/DineBot.git
cd DineBot

Next, install the necessary Python modules:

pip install mysql-connector-python
pip install "fastapi[all]"

Alternatively, you can install all required modules with one command:

pip install -r backend/requirements.txt

Backend Server Setup

To start the FastAPI backend server, follow these steps:

1. Navigate to the backend directory in your command prompt:

   cd backend

2. Run the following command to start the server:

   uvicorn main:app --reload

Ngrok for HTTPS Tunneling

To expose your local server to the internet using Ngrok, follow these steps:

1. Download and install Ngrok from here for your operating system.

2. Extract the zip file and place ngrok in a folder.

3. Open the command prompt, navigate to that folder, and run this command:

   ngrok http 8000

Note: Ngrok sessions can expire, so you may need to restart the session if you see a session expired message.

Usage

Once the backend server and Ngrok are set up, you can interact with the DineBot through your food website. The chatbot will handle order placement and retrieval seamlessly.

Future Scope

• Enhanced NLP Capabilities: Improve the chatbot's understanding and responses by integrating more advanced NLP techniques.
• Multi-language Support: Expand the chatbot's functionality to support multiple languages.
• AI-driven Recommendations: Implement AI algorithms to provide personalized food recommendations to users.
• Mobile App Integration: Develop a mobile application to complement the web-based chatbot.

Contributing

We welcome contributions to enhance DineBot! To contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch: git checkout -b feature-branch-name.
3. Make your changes and commit them: git commit -m 'Add some feature'.
4. Push to the branch: git push origin feature-branch-name.
5. Create a pull request.

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

Conclusion

DineBot aims to revolutionize the way users place food orders online, offering a seamless and efficient experience through advanced NLP and robust backend services. We look forward to your contributions and feedback to make this project even better.

References

• Dialogflow Documentation
• FastAPI Documentation
• MySQL Documentation
• Ngrok Documentation