README.md

---
title: Generative AI With Poultry Disease Detection System V2
emoji: 🐨
colorFrom: pink
colorTo: gray
sdk: docker
pinned: false
license: mit
---

# 🐔 Poultry Farming Assistance and Management System

This project integrates both a **Poultry Farming Assistance System** and a **Poultry Management System**. The system supports farm management tasks like health management, to-do lists, inventory, and notifications, while also providing intelligent assistance for disease detection and recommendations using AI.

## Project Features
1. **User Registration/Login System**
2. **Data Logging and Reporting (dashboards and exporting PDF and XLS)**
3. **Health Management and Disease Detection**
4. **Inventory Management**
5. **Poultry Farming Assistance (AI-based disease detection and recommendations)**
6. **Notification System via Email**

## System Components

### Poultry Farming Assistance System
- AI-based disease detection using machine learning models (e.g., `Final_Chicken_disease_model.h5`).
- Health monitoring and suggestions for poultry care.
- Fecal image analysis to detect common poultry diseases.
- Generate actionable insights based on poultry health data.

### Poultry Management System
- **To-Do List Management**: Manage daily farm tasks and activities.
- **Inventory Management**: Track and manage feed, medicines, and other supplies.
- **Health Records**: Log poultry health issues, treatments, and disease outbreaks.
- **Group Management**: Farmers can join groups, and admins can create and share to-do lists across groups.
- **Reporting**: Generate reports on farm performance, health metrics, and more.

## Use Cases
- **Poultry Farmer**: Can sign up, log in, access disease detection tools, complete to-do lists, view health reports, and receive notifications.
- **Poultry Farm Admin**: Can create and assign tasks, manage inventory, track group activities, and monitor health conditions across the farm.

## 📋 Development To-Do Checklist

### 1. Setup and Configuration
- [x] Set up a virtual environment (e.g., `venv`, `conda`) for the project.
- [x] Install necessary Python packages: `FastAPI`, `AdminLTE`, `MongoDB`, `TensorFlow`, `Keras`, `pymongo`, `transformers`, etc.
- [x] Configure MongoDB connection and test basic CRUD operations with MongoDB.
- [x] Set up environment variables for MongoDB URI, email credentials, and other sensitive data.

### 2. Authentication System
- [x] Build the user registration system (Poultry Farmer, Poultry Admin).
- [x] Implement login and logout functionality using JWT.
- [x] Set up password encryption (e.g., `bcrypt`) for secure storage.
- [x] Implement session management and token validation.

### 3. Poultry Farming Assistance System
- [ ] Integrate poultry disease detection model (`Final_Chicken_disease_model.h5`).
- [ ] Create an image preprocessing pipeline for disease detection.
- [ ] Build routes to upload and analyze poultry fecal images.
- [ ] Develop health-related notifications and treatment suggestions.
- [ ] Implement real-time health monitoring and alert system for farmers.

### 4. To-Do List Management
- [ ] Create MongoDB schema for to-do lists (`todo_list.py`).
- [ ] Implement routes and controllers for farmers to view and complete to-do lists.
- [ ] Implement routes and controllers for admins to create and share to-do lists.
- [ ] Design **AdminLTE 4**-based UI for displaying and managing to-do lists.

### 5. Data Logging and Reporting
- [ ] Develop a data logging system for tracking activities.
- [ ] Set up reporting dashboards using **AdminLTE** (instead of Shiny).
- [ ] Implement export functionality for data as PDF and XLS.
- [ ] Integrate data visualization tools for real-time reporting and insights.

### 6. Health Management
- [ ] Create MongoDB schema for storing poultry health records (`health_record.py`).
- [ ] Integrate the poultry disease detection model with health management.
- [ ] Develop an **AdminLTE** dashboard for tracking health issues and disease management.
- [ ] Implement health-related notifications and treatment suggestions for farmers.

### 7. Inventory Management
- [ ] Build MongoDB schema for inventory management (`inventory.py`).
- [ ] Develop routes for adding, updating, and deleting inventory items.
- [ ] Create an **AdminLTE** dashboard for tracking inventory levels and status.
- [ ] Set up alerts for low stock levels via email notifications.

### 8. Notification System
- [ ] Set up an email service for sending notifications to users (e.g., `smtplib` or `nodemailer`).
- [ ] Implement notification triggers for the following:
  - [ ] To-do list completion.
  - [ ] Health alerts and treatment recommendations.
  - [ ] Inventory updates and low stock alerts.
- [ ] Test email notification system for various scenarios.

### 9. Group Management
- [ ] Create MongoDB schema for group management (`group.py`).
- [ ] Allow farm admins to create and delete groups.
- [ ] Allow poultry farmers to join groups.
- [ ] Implement group sharing functionalities (e.g., sharing to-do lists across groups).

### 10. Testing and Debugging
- [ ] Write unit tests for each module (authentication, to-do lists, health management, inventory, notifications).
- [ ] Conduct integration testing to ensure all components work together.
- [ ] Debug issues related to MongoDB transactions, AdminLTE dashboards, and TensorFlow model predictions.

### 11. Deployment
- [ ] Deploy the system on Hugging Face Spaces.
- [ ] Configure the production environment (e.g., environment variables, security settings).
- [ ] Set up a continuous integration/continuous deployment (CI/CD) pipeline for automatic testing and deployment.

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## Future Enhancements (Backlog)
- [ ] Add mobile app support for poultry farmers using `Flutter`.
- [ ] Implement AI-based inventory prediction for restocking supplies.
- [ ] Enable multi-language support for different regions.
- [ ] Introduce additional disease models for enhanced health management.

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