InoSense : Smart Monitorting of Chemmical Processes Dashboard1 Dashboard2 Dashboard3 Cutting-edge AI-driven solution for real-time chemical process monitoring and optimization.
Overview :
Welcome to InoSense, a full-stack AI/ML-powered industrial monitoring system designed to revolutionize chemical plant management. This project integrates a 4D digital twin, predictive maintenance, anomaly detection, voice control, blockchain data integrity, and a multi-user dashboard with automated reporting. Built with Python, TensorFlow, Dash, and FastAPI, InoSense delivers real-time insights, optimizes efficiency (up to 15-25%), and ensures data security—making it a standout solution for industrial intelligence.
Features :
Real-Time 4D Digital Twin: Visualizes plant thermal profiles with dynamic updates. Predictive Maintenance AI: Utilizes Transformer-based models and Isolation Forest for failure prediction. Anomaly Detection Engine: Identifies deviations with confidence scoring and actionable recommendations. Voice Control Interface: Enables hands-free operation with speech recognition. Blockchain Data Integrity: Ensures tamper-proof data logging with timestamp validation. Multi-User Dashboard: Interactive Dash-based UI with customizable risk thresholds. Automated Reporting: Generates PDF reports with QR codes for traceability.
Tech Stack :
Frontend: Dash, Plotly, Dash Bootstrap Components Backend: FastAPI, WebSockets, Uvicorn AI/ML: TensorFlow, scikit-learn, LSTM, MultiHeadAttention Data Handling: pandas, numpy, qrcode, fpdf Voice: speech_recognition, pyaudio Security: Custom blockchain implementation
Overview: InoSense is a full-stack AI/ML-powered industrial monitoring system designed to enhance chemical plant management. It features a 4D digital twin, predictive maintenance, anomaly detection, voice control, blockchain data integrity, and a multi-user dashboard with automated reporting. Built using Python, TensorFlow, Dash, and FastAPI, InoSense provides real-time insights and optimizes operational efficiency.
Features: Real-time 4D digital twin for plant visualization. Predictive maintenance using advanced AI models. Anomaly detection with actionable recommendations. Voice control interface for hands-free operation. Blockchain-based data integrity for secure logging. Interactive multi-user dashboard with customizable settings. Automated PDF reporting with QR code traceability.
Tech Stack : Frontend: Dash, Plotly, Dash Bootstrap Components Backend: FastAPI, WebSockets, Uvicorn AI/ML: TensorFlow, scikit-learn, LSTM, MultiHeadAttention Data Handling: pandas, numpy, qrcode, fpdf Voice: speech_recognition, pyaudio
Security: Blockchain (custom implementation)
Installation:
Prerequisites Python 3.9+ pip
Setup : Clone the repository: git clone https://github.com/SaiVarunPappla/inosense.git cd inosense
Install dependencies: pip install -r requirements.txt
Run the application: python inosense.py Access the dashboard at http://localhost:8050 Access API documentation at http://localhost:8000/docs
requirements.txt:
Security: Custom blockchain implementation
Installation: Prerequisites Python 3.9 or higher pip (Python package manager)
Steps : 1)Download the project: git clone https://github.com/SaiVarunPappla/inosense.git cd inosense 2)Install required packages: pip install -r requirements.txt 3)Create two folders: Make an assets folder (for pictures and files). Make a templates folder (can be empty). 4)Run the program: python inosense.py Open your browser and go to http://localhost:8050 for the dashboard. Go to http://localhost:8000/docs for API details.
requirements.txt Content :
numpy pandas tensorflow scikit-learn plotly dash dash-bootstrap-components fastapi uvicorn speechrecognition pyaudio sounddevice matplotlib fpdf qrcode websockets
Usage :
Dashboard: Navigate the interactive UI to monitor plant metrics, adjust risk thresholds, and generate reports. Voice Commands: Click "Voice Command" to issue instructions (e.g., "temperature" or "emergency"). API: Use WebSocket (/ws/sensor-data) or REST endpoints (/api/plant-state, /api/ai-prediction) for integration. Reports: Download PDF reports with current system status and QR-coded traceability. voice command
Project Structure : inosense/ ├── assets/ # Static files (images, CSS) ├── templates/ # Jinja2 templates (optional) ├── inosense.py # Main application file ├── requirements.txt # Dependency list └── README.md # This file
Contributing Contributions are welcome! Please fork the repository and submit pull requests. For major changes, open an issue first to discuss.
1)Fork the repo. 2)Create a feature branch (git checkout -b feature/awesome-feature). 3)Commit changes (git commit -m "Add awesome feature"). 4)Push to the branch (git push origin feature/awesome-feature). 5)Open a pull request.
License This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments Developed by Varun Pappla Links: GitHub LinkedIn Use the dashboard to watch plant data, change settings, and create reports. Click "Voice Command" to give voice instructions (e.g., "temperature"). Use the API with WebSocket (/ws/sensor-data) or endpoints (/api/plant-state, /api/ai-prediction). dashboard5.png Download PDF reports from the dashboard.
Screenshots :
ScreenShot1 ScreenShot2 ScreenShot3 ScreenShot4 ScreenShot5 ScreenShot6
Project Structure: inosense/ ├── assets/ # Folder for pictures and files ├── templates/ # Folder for templates (can be empty) ├── inosense.py # Main program file ├── requirements.txt # List of needed packages └── README.md # This file
Contributing :
You can help improve this project! Here’s how: 1)Make a copy of this project (fork it) on GitHub. 2)Create a new branch for your changes: git checkout -b my-new-feature 3)Save your changes: git commit -m "Add my new feature" 4)Send your changes back: git push origin my-new-feature 5)Ask to add your changes by making a pull request on GitHub.
License This project uses the MIT License. See the LICENSE file for more information.
Acknowledgments Created by Varun Pappla Links : Linkedin : https://www.linkedin.com/in/pappla-sai-varun-874902200/
Future Plans: -Add support for monitoring multiple plants. -Create a mobile app version. -Improve AI with advanced optimization techniques.