Inspiration :

Maintaining cleanliness in public spaces is a persistent challenge in many urban environments. Issues such as dirty toilets, garbage accumulation, and unattended waste often go unreported because the process is manual and slow. The need for a faster, AI-driven mechanism to assist municipalities and empower citizens inspired the creation of SmartSanitizeAI—an automated system that detects cleanliness issues and alerts the nearest civic authority instantly.

What it does :

Allows users to upload or capture a photo of toilets or garbage areas Uses an AI model to classify the image as “Clean” or “Dirty” Captures the user’s real-time location Identifies the nearest municipal corporation office using geolocation Sends an email alert to the appropriate officials with the prediction result and location Displays a cleanliness score and official contact details to the user The platform bridges the gap between citizens and municipal authorities, enabling faster sanitation response.

How we built it :

Flask (Python) for backend routing and processing TensorFlow/Keras CNN for cleanliness image classification HTML, CSS, and JavaScript for an interactive and responsive user interface Browser Geolocation API for real-time latitude and longitude A custom JSON dataset of Tamil Nadu municipal offices Haversine distance calculation to find the nearest office Gmail SMTP for sending automated email alerts The solution is optimized for both desktop and mobile browsers, including support for camera capture.

Challenges we ran into :

Training and fine-tuning the CNN model to handle various image conditions Ensuring accurate mapping between user location and nearest municipality Handling mobile browser limitations during camera/photo uploads Resolving Gmail security restrictions and App Password requirements Managing multiple front-end submissions and preventing repeated requests Maintaining UI responsiveness and clarity for all users

Accomplishments that we’re proud of :

Successfully deployed a working AI model for real-time cleanliness detection Achieved seamless integration of geolocation and municipality mapping Built an intuitive drag-and-drop and camera-based upload interface Automated email alerts to officials with complete report details Ensured accurate distance-based office identification across Tamil Nadu Delivered a clean, modern, and user-friendly interface

What we learned :

Designing and deploying lightweight deep-learning models Building full-stack AI systems combining front-end, backend, and ML workflows Handling real-time user data efficiently (images, GPS coordinates, etc.) Improving UI/UX for citizen-centric applications Working with structured government datasets for automation Managing error handling, authentication, and user-side validation

What’s next for SmartSanitizeAI:

A dashboard for municipal authorities to track reports City-wide cleanliness heatmaps and analytics Android and iOS mobile applications A WhatsApp chatbot version for easy reporting Multi-category detection for specific sanitation issues Notification system for users when their complaint is resolved Collaboration with smart city initiatives

SmartSanitizeAI aims to evolve into a comprehensive AI-powered sanitation management system for urban environments.