In this project, many of the variables such as MQTT topics, MQTT brokers, and data metrics are currently set as dummy values. These are placeholder values used during development to simulate the functionality of the system. They allow the system to run in a controlled environment without requiring actual IoT devices or data sources to be connected.
Make sure to use this command before commit and push changes to the remote repository to prevent this warning: LF will be replaced by CRLF.
git config --global core.autocrlf false
Description: Retrieves Firebase configuration required for the frontend application.
| Parameter | Type | Description |
|---|---|---|
| None | N/A | Retrieves Firebase configuration such as API Key, Project ID, and other necessary details. |
Response
{
"apiKey": "your-api-key",
"authDomain": "your-auth-domain",
"projectId": "your-project-id",
"storageBucket": "your-storage-bucket",
"messagingSenderId": "your-messaging-sender-id",
"appId": "your-app-id",
"measurementId": "your-measurement-id"
}
Description: Logs the user in using a Firebase ID token.
| Parameter | Type | Description |
|---|---|---|
| idToken | string | Firebase ID token obtained after a successful login using Firebase Authentication. |
Response :
200 OK: Redirects to the main page (/index) if login is successful.
301 Moved Permanently: Redirects back to the login page if login fails.
Description: Displays the dashboard page after successful login.
| Parameter | Type | Description |
|---|---|---|
| None | N/A | Accesses the dashboard page after a successful login. |
Response :
200 OK: Returns the HTML page of the user's dashboard.
Description: Logs the user out and clears the session.
| Parameter | Type | Description |
|---|---|---|
| None | N/A | Clears the user session and redirects to the login page. |
Response :
200 OK: User is redirected back to the login page.
Description: Retrieves historical data based on the specified time range (startdate and enddate).
| Parameter | Type | Description |
|---|---|---|
| startdate | string | Start time in YYYY-MM-DD%20HH:MM:SS format. |
| enddate | string | End time in YYYY-MM-DD%20HH:MM:SS format. |
Example:
http://localhost:5000/api/fetch_data?startdate=2024-11-01%2000:12:23&endddate=2024-11-01%2000:15:23
Response:
200 OK: User is redirected back to the login page.
Documentation is underconstruction
Documentation is underconstruction
chat reyhan for creds config
- Gunicorn will handle multiple concurrent requests, ensuring better performance and scalability than the built-in Flask development server.
- The application will be set up to run with Gunicorn in the production environment.
- Supervisor will be used to ensure that Gunicorn is always running.
- If the Gunicorn process crashes or fails, Supervisor will automatically restart it. This is crucial for maintaining the stability of the system in a production environment.
- The machine learning models will be stored in cloud storage (Google Cloud Storage) for easy access and version control.
- The endpoints will interact with the machine learning models to provide real-time anomaly detection for the system.
- The Dockerfile should be reviewed and updated
- The system will be connected to the Evomo platform’s MQTT broker, subscribing to real data topics (e.g., energy readings from IoT devices).
- The real-time data will be processed by the backend (including anomaly detection) and stored for analysis.
- The dashboard will display real-time data and anomalies, making the monitoring system fully operational with live inputs from Evomo.
- Set up Firebase in the backend to send notifications to mobile devices via FCM. This involves configuring Firebase credentials and initializing Firebase in the Flask app.