ChickTech is an AI-powered chicken disease diagnosis platform that leverages deep learning to identify poultry diseases. It combines a powerful Flask backend for AI inference and a Next.js frontend for an interactive, cinematic user experience.
The platform provides:
- Real-time Disease Detection: Using trained CNN models for Coccidiosis and External Lesions.
- External Lesion Detection: New module for identifying Fowlpox and Bumblefoot with high accuracy.
- Cinematic UI: Interactive fluid simulation (Splash Cursor) and smooth GSAP animations.
- Comprehensive Recovery Guides: Dynamic, doctor-level treatment and prevention protocols for each detected disease.
- Cloud-Ready: Scalable architecture ready for deployment on Render, AWS, or Azure.
| Layer | Technologies Used |
|---|---|
| Frontend | Next.js 14, TypeScript, Tailwind CSS, GSAP, OGL (WebGL) |
| Backend / API | Flask (Python), Flask-Limiter, cachetools, pybloom_live |
| Machine Learning | MobileNetV2, OpenCV, Sentence-Transformers (RAG embeddings) |
| Generative AI | Google Gemini (GenAI), Sarvam AI (Translate, STT, TTS) |
| Database / Auth | Firebase (Firestore & Authentication) |
| Deployment | Render (Backend), Vercel (Frontend), Docker |
✅ Dual Diagnosis Modes: Choose between Coccidiosis (Fecal) and External Lesion (Skin/Foot) detection.
✅ AI Treatment Plans (RAG): Gemini-powered, context-aware recovery plans generated instantly by retrieving data from a comprehensive 47-chunk custom Knowledge Base across 9 diseases.
✅ Backend Security Layer: Enterprise-grade security using flask-limiter for rate limiting, pybloom_live Bloom filters for cache-miss attack prevention, and strict payload sanitization (magic bytes, MIME types).
✅ Multilingual Translation (Sarvam AI): Instantly translate the entire application into 10+ Indic languages with high-speed batching.
✅ Voice-to-Text Symptom Logger (Sarvam AI): Speak symptoms naturally and let the AI automatically suggest the right diagnostic path.
✅ Text-to-Speech Accessibility (Sarvam AI): Listen to diagnosis results and treatment steps completely in regional languages.
✅ Cinematic Glassmorphism UI: Premium WebGL fluid simulation (Splash Cursor), custom-styled themed dropdowns, and GSAP animations.
✅ High Accuracy Models: Trained on curated poultry datasets with 98%+ accuracy for the external lesion module.
✅ Prediction History: Securely save and view past diagnosis results using Firebase.
ChickTech-AI-Diagnosis/
│
├── frontend/ # Next.js app (UI)
│ ├── app/ # Routes and Layouts
│ ├── components/ # React Components
│ │ ├── reactbits/ # Premium UI components (SplashCursor, etc.)
│ │ └── ui/ # Base UI components
│ └── public/ # Static assets
│
├── backend/ # Flask Backend
│ ├── models/ # Trained .h5 models and metrics
│ ├── app.py # API Entry Point (with strict security)
│ ├── predict.py # Generic Predictor Class
│ ├── rag_engine.py # Gemini + SentenceTransformers RAG Pipeline
│ ├── knowledge_base.json # Vector database seed chunks
│ └── train_external_lesion.py # Training script for new model
│
├── datasets/ # Training data (ignored)
├── requirements.txt # Python dependencies
├── Dockerfile # Container setup
└── README.md # You are here
git clone https://github.com/KunalBishwal/ChickTech-AI-Diagnosis.git
cd ChickTech-AI-Diagnosis# Recommended: Create a virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
python backend/app.pycd frontend
npm install
npm run dev.envHandling: All API keys and environment variables are managed via.envfiles (ignored in Git).- Git LFS: Used for tracking large model files (
.h5). - Data Protection:
minor_project.txtand other sensitive notes are excluded via.gitignore.
Kunal Bishwal
📍 AI Developer | Full-Stack Engineer