366 lines
11 KiB
Markdown
366 lines
11 KiB
Markdown
# 📋 API Classification - Implementation Summary
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## ✅ Apa yang Telah Dibuat
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Berikut adalah daftar lengkap file dan komponen yang telah dibuat untuk API klasifikasi model Anda:
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---
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## 🔌 Backend Components
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### 1. **Flask API Server** (`api_server.py`)
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- **Location:** `rice leaf diseases dataset/api_server.py`
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- **Fungsi:** Server untuk menjalankan model TensorFlow
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- **Fitur:**
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- Load model dari `.keras`, `.h5`, atau `.json`
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- Preprocess gambar input (224x224)
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- Prediksi dengan confidence score
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- Error handling
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- CORS enabled untuk cross-origin requests
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### 2. **Laravel Classification Controller** (`ClassificationController.php`)
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- **Location:** `web_TA/app/Http/Controllers/ClassificationController.php`
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- **Fungsi:** Handle API requests dari client
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- **Endpoints:**
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- `POST /api/classification/classify` - Analyze only
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- `POST /api/classification/classify-and-save` - Save image
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- `GET /api/classification/test` - Test connection
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- **Features:**
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- Validate image input
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- Convert to base64
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- Call Python API
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- Store results
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- Return disease information
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### 3. **API Routes** (`api.php`)
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- **Location:** `web_TA/routes/api.php`
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- **Fungsi:** Define API endpoints
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- **Routes:**
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- `GET /api/classification/test`
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- `POST /api/classification/classify`
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- `POST /api/classification/classify-and-save`
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---
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## 🐍 Python Services
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### 4. **Requirements File** (`requirements_api.txt`)
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- **Location:** `rice leaf diseases dataset/requirements_api.txt`
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- **Content:**
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- TensorFlow 2.10+
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- Flask + Flask-CORS
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- OpenCV
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- NumPy, Pandas
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- Requests library
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### 5. **Database Models** (`models.py`)
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- **Location:** `rice leaf diseases dataset/models.py`
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- **Fungsi:** SQLAlchemy model untuk classification history
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- **Fields:**
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- image_path, filename
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- predicted_class, confidence
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- all_predictions (JSON)
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- disease_name, severity
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- notes, timestamps
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---
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## 📱 Mobile Integration
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### 6. **Flutter Integration Guide** (`FLUTTER_INTEGRATION.md`)
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- **Location:** `FLUTTER_INTEGRATION.md`
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- **Content:**
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- ClassificationService class
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- UI Screen component
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- Image picker integration
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- Result display
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- Configuration guide
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---
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## 📚 Documentation
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### 7. **Setup Guide** (`SETUP_GUIDE.md`)
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- **Location:** `SETUP_GUIDE.md`
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- **Content:**
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- Step-by-step installation
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- API endpoints documentation
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- Troubleshooting guide
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- Frontend examples
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- Database setup
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- Deployment notes
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### 8. **Quick Start** (`QUICK_START.md`)
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- **Location:** `QUICK_START.md`
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- **Content:**
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- 5-minute setup
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- Quick endpoint reference
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- Troubleshooting
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- Next steps
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### 9. **Integration Summary** (Dokumen ini)
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- **Location:** `README_API.md`
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- **Content:** Overview lengkap dari implementasi
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---
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## 🧪 Testing & Setup
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### 10. **Test Script** (`test_api.py`)
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- **Location:** `PROJECT TA/test_api.py`
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- **Fungsi:**
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- Test Python API health
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- Test Laravel API connection
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- Test image classification
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- Detailed results reporting
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### 11. **Setup Scripts**
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- **Windows:** `setup.bat`
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- **Linux/Mac:** `setup.sh`
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- **Fungsi:** Automated setup verification
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---
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## 🎯 How It Works
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```
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User (Web/Mobile)
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↓
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[WEB UPLOAD] or [REST API]
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↓
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Laravel API (ClassificationController)
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↓
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[VALIDATION + ENCODING]
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↓
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Flask Python API Server
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↓
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[PREPROCESS + CNN MODEL]
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↓
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TensorFlow Model
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↓
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[PREDICTION RESULTS]
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↓
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Flask API
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↓
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[JSON RESPONSE]
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↓
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Laravel Controller
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↓
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[STORE RESULTS + ADD DISEASE INFO]
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↓
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JSON Response to User
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```
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---
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## 📊 Supported Disease Classes
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```
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1. Bacterialblight (Bercak Bakteri)
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- Confidence score + Symptoms + Treatment
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2. Brownspot (Bercak Coklat)
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- Confidence score + Symptoms + Treatment
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3. Leafsmut (Jamur Daun)
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- Confidence score + Symptoms + Treatment
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```
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---
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## 🚀 Deployment Checklist
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- [ ] Python API server running on port 5000
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- [ ] Laravel server running on port 8000
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- [ ] Model file exists (`.keras` or `.h5`)
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- [ ] Dependencies installed (`pip install -r requirements_api.txt`)
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- [ ] Test API endpoints (`python test_api.py`)
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- [ ] Database configured (MySQL)
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- [ ] Storage directory writable
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- [ ] CORS properly configured
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- [ ] Environment variables set (`.env`)
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---
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## 💾 Default Configurations
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| Setting | Value | Location |
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|---------|-------|----------|
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| Python API URL | `http://127.0.0.1:5000` | `ClassificationController.php` |
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| Flask Port | `5000` | `api_server.py` |
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| Laravel Port | `8000` | Default `php artisan serve` |
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| Input Image Size | `224x224` | `api_server.py` |
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| Max Upload Size | `5MB` | `ClassificationController.php` |
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| Classes | 3 (Bacterialblight, Brownspot, Leafsmut) | `api_server.py` |
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---
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## 🔄 Data Flow Example
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### Request:
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```
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POST /api/classification/classify
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File: leaf_image.jpg
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```
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### Processing:
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1. Laravel receives image
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2. Encodes to base64
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3. Sends to Python API
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4. Python loads model
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5. Preprocesses image (224x224, normalize)
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6. Runs prediction
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7. Returns class + confidence
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### Response:
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```json
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{
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"success": true,
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"predicted_class": "Bacterialblight",
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"confidence": 0.95,
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"disease_info": {
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"name": "Bercak Bakteri...",
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"symptoms": [...],
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"treatment": [...]
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}
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}
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```
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---
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## 🛠️ Customization Options
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### Change API Port
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Edit `api_server.py`:
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```python
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app.run(host='127.0.0.1', port=5000) # Change port here
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```
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### Change Input Image Size
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Edit `api_server.py`:
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```python
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IMG_SIZE = (224, 224) # Change size here
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```
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### Add More Disease Classes
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Update both files:
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```python
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# api_server.py
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CLASS_NAMES = ['Bacterialblight', 'Brownspot', 'Leafsmut', 'NewDisease']
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// ClassificationController.php
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private function getDiseaseInfo($className) { ... }
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```
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### Increase Upload Size Limit
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Edit `ClassificationController.php`:
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```php
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'image' => 'required|image|mimes:jpeg,png,jpg,gif|max:10240' // 10MB
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```
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---
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## 🔐 Security Notes
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1. **API Security:**
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- Add authentication token
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- Rate limiting
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- Input validation
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- HTTPS in production
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2. **Model Security:**
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- Don't expose model file paths
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- Validate image format
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- Handle large files carefully
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3. **File Storage:**
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- Store outside public directory
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- Implement cleanup policy
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- Restrict file access
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---
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## 📞 Support Files
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- [ClassificationController.php](web_TA/app/Http/Controllers/ClassificationController.php)
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- [api_server.py](rice%20leaf%20diseases%20dataset/api_server.py)
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- [api.php routes](web_TA/routes/api.php)
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- [Complete Setup Guide](SETUP_GUIDE.md)
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- [Flutter Integration](FLUTTER_INTEGRATION.md)
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---
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## 🎓 Architecture Diagram
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```
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┌─────────────────────────────────────────────────────────────┐
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│ Client Application │
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│ (Web Browser / Flutter Mobile) │
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└────────────────────────┬────────────────────────────────────┘
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│
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│ HTTP Request
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│ (POST with image)
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↓
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┌─────────────────────────────────────────────────────────────┐
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│ Laravel Web Application │
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│ (Port: 8000) │
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├─────────────────────────────────────────────────────────────┤
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│ ClassificationController │
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│ ├─ /api/classification/classify │
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│ ├─ /api/classification/classify-and-save │
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│ └─ /api/classification/test │
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└────────────────────────┬────────────────────────────────────┘
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│
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│ HTTP Request
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│ (POST with base64)
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↓
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┌─────────────────────────────────────────────────────────────┐
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│ Python Flask API Server │
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│ (Port: 5000) │
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├─────────────────────────────────────────────────────────────┤
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│ /classify endpoint │
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│ ├─ Decode image from base64 │
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│ ├─ Preprocess (224x224, normalize) │
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│ ├─ Load TensorFlow Model │
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│ └─ Run prediction │
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└────────────────────────┬────────────────────────────────────┘
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│
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│
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↓
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┌─────────────────────────────────────────────────────────────┐
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│ TensorFlow/Keras CNN Model │
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│ (rice_leaf_disease_model.keras) │
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├─────────────────────────────────────────────────────────────┤
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│ Input: 224x224x3 image │
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│ Output: 3 class predictions with confidence │
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│ Classes: [Bacterialblight, Brownspot, Leafsmut] │
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└─────────────────────────────────────────────────────────────┘
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```
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---
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## 🎯 Success Criteria
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- ✅ API Server running without errors
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- ✅ Model loads successfully
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- ✅ Endpoints respond to requests
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- ✅ Classification works with test images
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- ✅ Results displayed correctly
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- ✅ No CORS errors
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- ✅ Image storage working
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---
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## 📈 Performance Notes
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- **Model Load Time:** ~2-3 seconds (first request)
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- **Prediction Time:** ~0.5-1 second per image
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- **Memory Usage:** ~500MB (TensorFlow + Model)
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- **Network Latency:** ~100-500ms (depends on connection)
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---
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**Status:** 🟢 Ready for Use
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**Last Updated:** March 9, 2026
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**Version:** 1.0
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