Kibet-Rotich/NDIZIAI

A Django-based AI-powered system for classifying banana ripeness from images and providing value addition suggestions. The project integrates a trained machine learning model for ripeness detection, a backend API, and a frontend interface for users. # NDIZIAI AI-powered banana ripeness classification with value-addition recommendations. This project combines: - A Django + Django REST Framework backend - A YOLO model for banana ripeness classification - A simple frontend for image upload and result display - A value-addition knowledge base stored in SQLite ## What The System Does 1. Accepts a banana image upload. 2. Classifies the banana stage (for example: unripe, ripe, overripe, rotten). 3. Returns prediction confidence scores. 4. Retrieves value-addition methods for the predicted ripeness stage. ## Tech Stack - Backend: Django, Django REST Framework - AI inference: Ultralytics YOLO - Database: SQLite - Frontend: HTML, Tailwind CSS (CDN), JavaScript - API docs UI: Swagger / ReDoc via drf-yasg ## Project Structure NDIZIAI/ - api/ : Classification endpoint and model inference logic - db/ : Value-addition models, serializers, and API endpoint - backend/ : Placeholder app (currently minimal) - ndiziai/ : Django project settings and root URLs - frontend/ : Static frontend demo (HTML/CSS/JS) - dataset/ : Training/validation/test image dataset - best.pt : Trained YOLO weights file used by inference - manage.py : Django management entry point - requirements.txt : Python dependencies - documentation/ : Detailed technical documentation ## Quick Start 1. Create and activate a virtual environment. 2. Install dependencies: pip install -r requirements.txt 3. Run migrations: python manage.py migrate 4. Start server: python manage.py runserver 5. Open frontend demo: Open frontend/index.html in a browser. Default API base URL: - 127.0.0.1 ## Main API Endpoints - POST /api/classify/ Upload an image file under form key image. - GET /db/value-addition/?ripeness_stage= Fetch value-addition methods for a ripeness stage. - GET /swagger/ Interactive Swagger API docs. - GET /redoc/ ReDoc API documentation UI. ## Detailed Documentation - Setup guide: documentation/setup.md - Architecture: documentation/arc …