derrickml / caelumsense-smart-room

My ALX portfolio project of the Webstack specialization

CaelumSense: Your Personalized SMART Room Monitoring System

CaelumSense is a state-of-the-art room monitoring system designed to provide real-time insights into your room's humidity, temperature, and light levels. Powered by an Arduino-based hardware platform and a user-friendly web interface, CaelumSense makes it easy to monitor and analyze your room's environment. The system also features an AI-driven chatbot, CaelumAI, leveraging the text-davinci-003 model from OpenAI API to provide personalized assistance and support.

Hardware Overview

CaelumSense employs two Arduino boards, an ESP8266 WEMOS D1 mini and an Arduino MEGA ATmega2560, to collect sensor data and transmit it to a remote server using HTTPS POST requests. The front-end dashboard provides users with real-time access to this data through visually engaging graphs and tables.

Table of Contents

• Screenshots
• Features
• Technologies Used
• Hardware Requirements
• Software Requirements
• Caelum Chatbot AI Assistant
• Installation
• Usage
• Contributing
• License

Screenshots

Here are some screenshots of the CaelumSense project:

Dashboard Light mode	Dashboard Dark mode
Table Page in Light Mode	Table Page in Dark Mode
Chat Page in Dark Mode	Chat Page in Dark Mode

Hardware Set-up

Key Features

• Real-time Monitoring: Keep track of humidity, temperature, and light levels in your room with live updates.
• CaelumAI Chatbot: Receive personalized assistance from CaelumAI, our AI-driven chatbot, for an enhanced user experience.
• Web-based User Interface: Access your room's sensor data from anywhere via our intuitive, easy-to-use web interface.
• Customizable Data Visualization: Display your room's sensor data in customizable graphs and tables for quick and easy analysis.
• Remote Monitoring: Monitor your room's environment from anywhere with an internet connection.
• Arduino-powered Hardware: Utilize the reliable and efficient Arduino platform to collect and transmit sensor data.
• MySQL Database Integration: Store your room's sensor data securely in a MySQL database for easy retrieval and analysis.

Technologies Used

CaelumSense is built using the following technologies:

Category	Technologies
Server	Node.js
ChatBot	OpenAI API; Text-davinci-003 model
Frontend	HTML/CSS, JavaScript, Bootstrap, Material-UI, Creative Tim Dashboard Material-UI
Backend	Node.js, SQL
Storage	MySQL database
Hardware	Arduino, DHT22 sensor, LDR sensor, Jumper wires, Breadboard, USB/data cables

Hardware Requirements

To build and use the CaelumSense system, you will need the following hardware components:

Component	Description
Arduino MEGA ATmega2560	Microcontroller board
ESP8266 WEMOS D1 mini	WiFi module
DHT22	Humidity and temperature sensor
LDR	Light sensor
Breadboard	For building the circuit
Jumper wires	For connecting components
USB/data cables	For connecting and powering hardware

Software Requirements

To install and run the CaelumSense system, you will need the following software components:

Component	Description
Node.js	A JavaScript runtime environment for server-side programming and dependencies management
OpenAI API	A cloud-based API to access the Text-davinci-003 model for the CaelumAI chatbot
MySQL	A relational database management system for storing and managing sensor data

CaelumSense Chatbot

The CaelumSense Chatbot is a conversational agent that provides real-time temperature and humidity sensor data from the CaelumSense system. The chatbot is powered by OpenAI's GPT-3.5 architecture and can be accessed through a web-based user interface.

Chatbot Features

• Provides real-time temperature and humidity sensor data from the CaelumSense system
• Allows users to ask questions in natural language
• Responds to user queries with relevant and accurate information
• Can handle multiple users simultaneously
• Uses OpenAI's GPT-3.5 architecture to generate responses

Chatbot Technologies Used

The CaelumSense Chatbot is built using the following technologies:

• Frontend: HTML/CSS, JavaScript, Bootstrap
• Backend: Node.js, Express.js
• API: OpenAI's GPT-3.5

Chatbot Usage

To use the CaelumSense Chatbot, follow these steps:

1. Open a web browser and navigate to the CaelumSense dashboard.
2. Click on the chat icon to open the chatbot interface.
3. Type your question in the input field and press Enter or click the Send button.
4. The chatbot will generate a response based on your query.

Chatbot Limitations

• The chatbot can only provide sensor data from the last 100 readings.
• The chatbot is currently only able to respond to queries related to temperature and humidity data.

Installation

To install and set up the CaelumSense system, follow these steps:

1. Clone the CaelumSense GitHub repository to your local machine.
2. Install Node.js and MySQL on your machine if they are not already installed.
3. Set up the MySQL database using the provided caelumsense.sql file.
4. Navigate to the root directory in a terminal window and run the command npm install to install the required Node.js modules.
5. Follow the instructions in the Arduino directory on how to connect and configure the Arduino boards.
6. After setting up the Arduino and the sensors, navigate to the root directory in a terminal window and run the command node app.js to start the server.
7. Open a web browser and navigate to http://localhost:3000 to view the CaelumSense dashboard.

Usage

To use the CaelumSense system, follow these steps:

1. Connect the Arduino MEGA ATmega2560 and ESP8266 WEMOS D1 mini boards to your room's sensors.
2. Power on the Arduino Arduino MEGA ATmega2560 and WEMOS D1 Mini/ESP8266 module.
3. Open a web browser and navigate to http://localhost:3000 to view the CaelumSense dashboard.
4. The dashboard will display the current humidity, temperature, and light levels in your room in real-time.

Contributing

Contributions to the CaelumSense project are welcome and encouraged! To contribute, follow these steps:

1. Fork the CaelumSense GitHub repository.
2. Clone the forked repository to your local machine.
3. Install the necessary dependencies by running npm install in the root directory of the project.
4. Create a new branch for your feature or bug fix: git checkout -b my-new-feature.
5. Make changes and test thoroughly.
6. Commit your changes: git commit -am 'Add some feature' or git commit -am 'Fix some bug'.
7. Push your branch to GitHub: git push origin my-new-feature.
8. Submit a pull request to the CaelumSense GitHub repository.

License

The CaelumSense project is licensed under the MIT license.

Contact

For any questions, comments, or concerns, please feel free to contact the CaelumSense team at [email protected].