projek_padi/README_API.md

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