update space

.env

@@ -0,0 +1,5 @@
+# .env file
+
+MONGO_URI="your_mongo_db_uri"
+EMAIL_USER="your_email_username"
+EMAIL_PASS="your_email_password"

.gitignore

@@ -0,0 +1 @@
+.venv

.idea/Generative_AI_with_poultry_disease_detection_system_v2.iml

@@ -1,8 +1,13 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.12 (Generative_AI_with_poultry_disease_detection_system_v2)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
+  <component name="PackageRequirementsSettings">
+    <option name="removeUnused" value="true" />
+  </component>
 </module>

.idea/git_toolbox_prj.xml

@@ -0,0 +1,15 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="GitToolBoxProjectSettings">
+    <option name="commitMessageIssueKeyValidationOverride">
+      <BoolValueOverride>
+        <option name="enabled" value="true" />
+      </BoolValueOverride>
+    </option>
+    <option name="commitMessageValidationEnabledOverride">
+      <BoolValueOverride>
+        <option name="enabled" value="true" />
+      </BoolValueOverride>
+    </option>
+  </component>
+</project>

.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.12 (Generative_AI_with_poultry)" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.12 (Generative_AI_with_poultry)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.12 (Generative_AI_with_poultry_disease_detection_system_v2)" project-jdk-type="Python SDK" />
 </project>

README.md

@@ -8,4 +8,112 @@ pinned: false
 license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🐔 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
+- [ ] Build the user registration system (Poultry Farmer, Poultry Admin).
+- [ ] Implement login and logout functionality using JWT.
+- [ ] Set up password encryption (e.g., `bcrypt`) for secure storage.
+- [ ] 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.
+
+---
+
+## 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.
+
+---

app.py

@@ -1,7 +1,48 @@
-from fastapi import FastAPI
+from fastapi import FastAPI, Depends, HTTPException
+from pymongo import MongoClient
+from dotenv import load_dotenv
+import os
+import uvicorn
 
+# Load environment variables
+load_dotenv()
+
+# Initialize FastAPI app
 app = FastAPI()
 
+# MongoDB Configuration
+MONGO_URI = os.getenv("MONGO_URI")
+client = MongoClient(MONGO_URI)
+db = client.get_database("poultry_management")
+
+# Dependency to get the MongoDB database
+def get_db():
+    return db
+
+# Root route
 @app.get("/")
 def greet_json():
     return {"Hello": "World!"}
+
+# Example route for testing MongoDB connection
+@app.get("/test_db")
+def test_db_connection(db=Depends(get_db)):
+    try:
+        db.test.insert_one({"test": "connection"})
+        return {"status": "Connection Successful", "message": "Document inserted!"}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Database connection error: {e}")
+
+# Placeholder for authentication routes
+# from routes.authentication import auth_router  # Uncomment when routes are ready
+# app.include_router(auth_router, prefix="/auth")
+
+# Placeholder for other routes
+# from routes.todo import todo_router  # Uncomment when routes are ready
+# from routes.health import health_router  # Uncomment when routes are ready
+# app.include_router(todo_router, prefix="/todos")
+# app.include_router(health_router, prefix="/health")
+
+# Main entry point for running the application
+if __name__ == "__main__":
+    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=True)

requirements.txt

@@ -1,6 +1,7 @@
-fastapi
-uvicorn[standard]
-requests
-sentencepiece
-torch
-transformers
+fastapi~=0.115.4
+passlib~=1.7.4
+pydantic~=2.9.2
+pymongo~=4.9.2
+pyjwt~=2.9.0
+python-dotenv~=1.0.1
+uvicorn~=0.32.0

services/utils.py

@@ -0,0 +1,38 @@
+# services/utils.py
+
+import os
+from dotenv import load_dotenv
+from pymongo import MongoClient
+from pymongo.errors import ConnectionFailure, OperationFailure
+
+# Load environment variables from .env
+load_dotenv()
+
+# MongoDB Connection URI
+MONGO_URI = os.getenv("MONGO_URI")
+
+# Initialize MongoDB client and database connection
+class MongoDB:
+    def __init__(self, uri: str, db_name: str):
+        self.client = MongoClient(uri)
+        self.db = self.client[db_name]
+
+    def test_connection(self) -> str:
+        """Tests MongoDB connection by performing a basic operation."""
+        try:
+            self.db.test_collection.insert_one({"status": "connection_test"})
+            return "Connection successful: Test document inserted!"
+        except (ConnectionFailure, OperationFailure) as e:
+            return f"Database connection error: {e}"
+
+    def get_collection(self, collection_name: str):
+        """Fetches a MongoDB collection."""
+        return self.db[collection_name]
+
+# Instantiate MongoDB with URI and database name
+mongo_instance = MongoDB(uri=MONGO_URI, db_name="poultry_management")
+
+def test_db_connection():
+    """Tests the database connection and prints result."""
+    result = mongo_instance.test_connection()
+    print(result)

static/index.html

@@ -0,0 +1,10 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Title</title>
+</head>
+<body>
+
+</body>
+</html>