#  Smart IoT Gas Monitoring System

A complete **end-to-end IoT monitoring system** built using **ESP32, MicroPython, Flask, and AI-based logic** to detect abnormal gas levels and visualize live sensor data on a web dashboard.

![ESP32](https://img.shields.io/badge/ESP32-MicroPython-blue?style=for-the-badge)
![Python](https://img.shields.io/badge/Python-Flask-green?style=for-the-badge)
![IoT](https://img.shields.io/badge/IoT-Real--Time-orange?style=for-the-badge)
![AI](https://img.shields.io/badge/AI-Anomaly%20Detection-red?style=for-the-badge)
![ChartJS](https://img.shields.io/badge/Chart.js-Live%20Graphs-yellow?style=for-the-badge)



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##  Project Overview

This project collects real-time data from multiple sensors connected to an **ESP32**, sends it to a **Flask backend**, stores it in a CSV file, and displays **live graphs and status prediction** on a modern web dashboard.

An **AI-inspired moving average logic** is used to classify the environment as **SAFE or DANGER** based on gas behavior.

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##  Features

-  Real-time Gas monitoring (MQ Gas Sensor)
- Temperature & Humidity monitoring (DHT11)
-  Soil / Rain sensor support
-  Live web dashboard (Flask + Chart.js)
-  Dynamic **SAFE / DANGER** prediction
-  AI-style anomaly detection (Moving Average)
-  CSV data logging for analytics
-  Responsive & clean UI

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##  Tech Stack

###  Hardware
- ESP32
- MQ Gas Sensor
- DHT11 Temperature & Humidity Sensor
- Soil / Rain Sensor
###  Software
- MicroPython (ESP32)
- Python (Flask Backend)
- HTML, CSS, JavaScript
- Chart.js (Live Graphs)

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##  AI Logic (Simple ML Concept)

The system uses a **moving average–based anomaly detection** approach:

- Maintains a window of recent gas values
- Calculates average gas level
- If current gas value exceeds  
  **Average + Safety Margin → DANGER**
- Else → **SAFE**

This approach simulates **rule-based machine learning**, suitable for beginner AI/ML concepts.

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##  How It Works

1. ESP32 reads sensor values  
2. Data is sent to Flask server using HTTP POST  
3. Flask backend:
   - Applies AI logic
   - Stores data in CSV
   - Updates live state  
4. Web dashboard:
   - Fetches live data
   - Displays real-time graphs
   - Shows **SAFE / DANGER** status  

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##  Screenshots

-  **Dashboard UI**
-  **Circuit Diagram**
![Circuit Diagram](Circuit_Diagram/circuit_image.png)
##  ESP32 Pin Connections

| Sensor              | ESP32 Pin | Description |
|---------------------|-----------|-------------|
| MQ Gas Sensor (AO)  | GPIO 34   | Analog gas value |
| Soil / Rain Sensor | GPIO 35   | Analog moisture value |
| DHT11 Data         | GPIO 4    | Temperature & Humidity |
| MQ / Soil VCC      | 3.3V / 5V | Power supply |
| All Sensors GND    | GND       | Common ground |

 **Note:**  
ESP32 ADC pins used: GPIO 34 & GPIO 35 (input-only, perfect for sensors).

![Circuit Diagram](Circuit_Diagram/vis_1.png)
![Circuit Diagram](Circuit_Diagram/vis_2.png)
![Circuit Diagram](Circuit_Diagram/vis_3.png)



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##  Learning Outcomes

- Practical ESP32 + MicroPython usage
- Backend development with Flask
- Real-time data visualization
- Basic AI/ML logic for anomaly detection
- End-to-end IoT system design

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##  Future Improvements

-  Cloud deployment (AWS / Firebase)
-  Mobile dashboard
-  Advanced ML model
-  Alert notifications (Email / WhatsApp)

## Author
**Kritish Mohapatra**  
B.Tech Electrical Engineering (3rd Year)  
IoT | Embedded Systems | MicroPython | ESP32  

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## ⭐ Support

If you like this project, give it a ⭐ on GitHub and feel free to fork it!

Happy hacking