453 lines
15 KiB
Markdown
453 lines
15 KiB
Markdown
# ✅ Flutter Mobile App - Integration Complete
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Integrasi API Classification ke aplikasi Flutter Anda **SELESAI**!
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Berikut adalah ringkasan lengkap file yang dibuat dan diupdate.
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---
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## 📋 File yang Dibuat
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### ✨ File Baru
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| File | Lokasi | Fungsi |
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|------|--------|--------|
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| **classification_service.dart** | `lib/services/` | 🔥 API Service - Handle semua komunikasi dengan server |
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| **MOBILE_SETUP.md** | `mobile_TA/` | 📚 Panduan setup lengkap |
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---
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## 🔄 File yang Diupdate
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### 🔧 Updated Files
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| File | Perubahan | Impact |
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|------|-----------|--------|
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| **dashboard_klasifikasi.dart** | ✅ Tambah import ClassificationService | Sekarang memanggil API real |
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| | ✅ Tambah _isLoading dan _errorMessage state | Loading indicator + error display |
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| | ✅ Tambah _handleClassification() method | Proses klasifikasi dengan API |
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| | ✅ Update button onPressed | Kirim gambar ke server |
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| **result_screen.dart** | ✅ Ubah constructor - terima ClassificationResult | Menampilkan hasil API real |
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| | ✅ Add disease info display | Gejala, treatment, severity |
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| | ✅ Add confidence score progress bar | Visual confidence |
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| | ✅ Add all predictions analysis | Score untuk semua kelas |
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| | ✅ Update detail popup | Gejala & treatment dari API |
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| **pubspec.yaml** | ✅ Tambah permission_handler | Camera & storage permissions |
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---
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## 🎯 Fitur yang Tersedia
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### Image Management ✅
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- Upload dari galeri
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- Ambil dari kamera
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- Preview sebelum klasifikasi
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- Ubah/hapus foto
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### API Integration ✅
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- Send image ke server
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- Dapatkan classification result
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- Disease information (gejala + treatment)
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- Confidence score & severity
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### UI/UX ✅
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- Loading spinner saat processing
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- Error messages yang jelas
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- Confidence progress bar
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- Severity color-coded badges
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- Detailed disease information
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- All predictions visualization
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### Error Handling ✅
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- Network error messages
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- Timeout handling
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- File validation
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- Loading states
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---
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## 🔌 API Flow
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```
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┌─────────────────────────────────────────────────────────┐
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│ FLUTTER MOBILE APP │
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│ │
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│ HomeScreen │
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│ ├─ Pilih/ambil foto daun padi │
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│ └─ Tap "Klasifikasi Sekarang" │
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│ ↓ │
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│ dashboard_klasifikasi.dart │
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│ ├─ Validate image │
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│ ├─ Call _handleClassification() │
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│ └─ Show loading spinner │
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└────────────────────┬──────────────────────────────────┘
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│
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│ HTTP POST /classify-and-save
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│ (multipart form-data)
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↓
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┌─────────────────────────────────────────────────────────┐
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│ LARAVEL WEB BACKEND (Port 8000) │
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│ │
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│ ClassificationController │
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│ ├─ Receive image file │
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│ ├─ Encode to base64 │
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│ └─ Forward to Python API │
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└────────────────────┬──────────────────────────────────┘
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│
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│ HTTP POST /classify
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│ (with base64 image)
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↓
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┌─────────────────────────────────────────────────────────┐
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│ PYTHON FLASK API (Port 5000) │
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│ │
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│ api_server.py │
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│ ├─ Decode base64 image │
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│ ├─ Preprocess (224x224, normalize) │
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│ ├─ Load TensorFlow model │
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│ ├─ Run prediction │
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│ └─ Return JSON result │
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└────────────────────┬──────────────────────────────────┘
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│
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│ JSON Response
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│ {predicted_class, confidence, disease_info}
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↓
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┌─────────────────────────────────────────────────────────┐
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│ LARAVEL RETURNS TO FLUTTER │
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│ │
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│ ClassificationResult object created │
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│ ├─ predictedClass │
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│ ├─ confidence │
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│ ├─ diseaseInfo (name, symptoms, treatment) │
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│ └─ allPredictions │
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└────────────────────┬──────────────────────────────────┘
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│
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│ NavigatorPush(ResultScreen)
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↓
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┌─────────────────────────────────────────────────────────┐
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│ FLUTTER - RESULT SCREEN │
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│ │
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│ result_screen.dart │
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│ ├─ Display disease name & confidence │
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│ ├─ Show severity badge │
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│ ├─ List symptoms │
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│ ├─ List treatment recommendations │
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│ ├─ Show all predictions scores │
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│ └─ Display product recommendations │
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└─────────────────────────────────────────────────────────┘
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```
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---
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## 🚀 Langkah Setup
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### 1️⃣ Update Server URL (CRITICAL!)
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**File:** `lib/services/classification_service.dart` line 7
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```dart
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// SEBELUM:
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static const String _baseUrl = 'http://10.0.2.2:8000/api/classification';
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// UBAH KE SESUAI ENVIRONMENT ANDA:
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// Emulator Android: http://10.0.2.2:8000/api/classification
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// Device Fisik: http://192.168.x.x:8000/api/classification (ganti dengan IP komputer)
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// iOS Emulator: http://127.0.0.1:8000/api/classification
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```
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**Cari IP komputer:**
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```bash
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# Windows
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ipconfig
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# Mac/Linux
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ifconfig
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```
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### 2️⃣ Install Dependencies
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```bash
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cd "d:\PROJECT TA\mobile_TA\padi_app"
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flutter pub get
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```
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### 3️⃣ Run Server (3 Terminal)
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**Terminal 1 - Python API:**
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```bash
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cd "d:\PROJECT TA\rice leaf diseases dataset"
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python api_server.py
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```
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**Terminal 2 - Laravel:**
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```bash
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cd "d:\PROJECT TA\web_TA"
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php artisan serve
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```
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**Terminal 3 - Flutter:**
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```bash
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cd "d:\PROJECT TA\mobile_TA\padi_app"
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flutter run
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```
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### 4️⃣ Test
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1. App terbuka
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2. Navigate ke classification screen
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3. Pilih/ambil foto
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4. Tap "Klasifikasi Sekarang"
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5. Lihat hasil dengan confidence score, gejala, dan treatment
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---
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## 📱 File Locations
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```
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PROJECT TA/
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├── mobile_TA/
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│ ├── 📄 MOBILE_SETUP.md ← Baca ini untuk setup!
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│ ├── 📄 INTEGRATION_SUMMARY.md ← File ini
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│ └── padi_app/
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│ ├── pubspec.yaml ← Updated dengan permission_handler
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│ └── lib/
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│ ├── 🔥 dashboard_klasifikasi.dart ← Updated
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│ ├── services/
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│ │ └── 🔥 classification_service.dart ← BARU!
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│ └── screen/
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│ └── 🔥 result_screen.dart ← Updated
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│
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├── rice leaf diseases dataset/
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│ └── api_server.py ← Python Flask API (harus running)
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│
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└── web_TA/
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└── php artisan serve ← Laravel API (harus running)
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```
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---
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## ⚙️ Configuration Checklist
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Sebelum jalankan app, pastikan:
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- [ ] **Update API URL** di `classification_service.dart`
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- Sesuaikan dengan environment (emulator/device fisik)
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- Cek IP komputer dengan `ipconfig`
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- [ ] **Install Flutter dependencies**
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```bash
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flutter pub get
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```
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- [ ] **Python API Server berjalan**
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```bash
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cd rice_leaf_diseases_dataset
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python api_server.py
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```
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- [ ] **Laravel Server berjalan**
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```bash
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cd web_TA
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php artisan serve
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```
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- [ ] **Model file ada**
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- `rice_leaf_disease_model.keras` or `.h5`
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- Di folder `rice leaf diseases dataset/`
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- [ ] **Network connectivity**
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- Pastikan device/emulator bisa akses server
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- Test dengan curl atau Postman
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---
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## 🎨 Customization Examples
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### Ubah Warna Tema
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```dart
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// Di dashboard_klasifikasi.dart
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backgroundColor: const Color(0xFF0F703A), // Hijau
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// Di result_screen.dart
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backgroundColor: const Color(0xFF0F703A), // Hijau
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```
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### Ubah Loading Text
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```dart
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// Di dashboard_klasifikasi.dart
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// Di _handleClassification() method
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setState(() {
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_isLoading = true;
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_errorMessage = null;
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});
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```
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### Tambah Loading Animation
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```dart
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// Import di top
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import 'package:flutter_spinkit/flutter_spinkit.dart';
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// Ganti CircularProgressIndicator dengan:
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SpinKitRing(color: Colors.white)
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```
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---
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## 🔍 Testing Tips
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### Test 1: Check API Connection
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```bash
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# Di command line
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curl -X GET http://127.0.0.1:8000/api/classification/test
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# Should return: {"success": true, "message": "..."}
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```
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### Test 2: Manual Classification
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```bash
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curl -F "image=@path/to/image.jpg" \
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http://127.0.0.1:8000/api/classification/classify
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```
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### Test 3: In App
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1. Buka Flutter app
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2. Pilih gambar daun padi
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3. Tap "Klasifikasi Sekarang"
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4. Tunggu hasil (1-2 detik)
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---
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## 📊 Expected Output
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Setelah klasifikasi berhasil, user akan lihat:
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```
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┌─────────────────────────────────────────┐
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│ Hasil Klasifikasi Screen │
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├─────────────────────────────────────────┤
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│ │
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│ Penyakit Terdeteksi: │
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│ Bacterial Leaf Blight │
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│ │
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│ Confidence: 95.23% [████████░░] │
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│ Severity: Sedang hingga Tinggi │
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│ │
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│ [Detail Lengkap & Rekomendasi] │
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│ │
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│ ───────────────────────────────────── │
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│ Analisis Semua Kelas: │
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│ Bacterialblight: 95.23% [████████░░] │
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│ Brownspot: 3.80% [░░░░░░░░░░] │
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│ Leafsmut: 0.97% [░░░░░░░░░░] │
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│ │
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│ ───────────────────────────────────── │
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│ Rekomendasi Produk: │
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│ [Produk 1] [Produk 2] │
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│ [Produk 3] [Produk 4] │
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│ │
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│ [Selesai] │
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└─────────────────────────────────────────┘
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```
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Ketika tap "Detail Lengkap & Rekomendasi":
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```
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┌─────────────────────────────────────────┐
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│ Detail: Bacterial Leaf Blight │
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├─────────────────────────────────────────┤
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│ │
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│ Deskripsi: │
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│ Penyakit yang disebabkan oleh bakteri │
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│ │
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│ Gejala: │
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│ • Bercak kecil berwarna kuning │
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│ • Bercak berkembang menjadi panjang │
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│ • Daun berubah warna menjadi kuning │
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│ │
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│ Penanganan: │
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│ • Gunakan varietas padi yang tahan │
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│ • Praktikkan rotasi tanaman │
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│ • Hindari irigasi berlebihan │
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│ • Aplikasikan fungisida bakteri │
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│ │
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│ [Tutup] │
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└─────────────────────────────────────────┘
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```
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---
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## 🆘 Troubleshooting
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| Error | Solusi |
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|-------|--------|
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| **"Network error"** | Update URL di classification_service.dart |
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| **"Request timeout"** | Pastikan semua 3 servers berjalan |
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| **"Model not loaded"** | Cek Python API server - lihat console output |
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| **Image can't be picked** | Grant camera/storage permissions |
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| **Screen blank** | Pastikan ResultScreen menerima parameter yang benar |
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---
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## 📚 Related Documentation
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- [MOBILE_SETUP.md](MOBILE_SETUP.md) - Setup guide lengkap
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- [QUICK_START.md](../QUICK_START.md) - Quick start untuk seluruh sistem
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- [SETUP_GUIDE.md](../SETUP_GUIDE.md) - Backend setup
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- [FLUTTER_INTEGRATION.md](../FLUTTER_INTEGRATION.md) - Template kode Flutter
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---
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## ✅ Verification Checklist
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```
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SERVER SETUP:
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☐ Python API running (port 5000)
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☐ Laravel API running (port 8000)
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☐ Model file exists
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☐ All dependencies installed
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FLUTTER SETUP:
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☐ API URL updated to correct IP
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☐ flutter pub get executed
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☐ No compilation errors
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☐ App runs without crashes
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FEATURE TEST:
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☐ Can pick image from gallery
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☐ Can take photo from camera
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☐ Image preview works
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☐ Classify button enabled
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☐ API response received
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☐ Results display correctly
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☐ Disease info shows
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☐ Confidence score visible
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```
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---
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## 🎯 Next Steps
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1. **✅ Read MOBILE_SETUP.md** untuk panduan detail
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2. **✅ Update API URL** sesuai environment Anda
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3. **✅ Install dependencies** dengan `flutter pub get`
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4. **✅ Run 3 servers** (Python, Laravel, Flutter)
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5. **✅ Test dengan real images** dari device camera
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6. **🚀 Deploy to Play Store/App Store** (future)
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---
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**Integration Status:** ✅ **COMPLETE**
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**Summary:**
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- ✅ API Service created & integrated
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- ✅ Dashboard updated with API calls
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- ✅ Result Screen shows real API data
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- ✅ Error handling implemented
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- ✅ Loading states added
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- ✅ Documentation complete
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**Ready to use!** 🎉
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---
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**Created:** March 9, 2026
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**Version:** 1.0
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**Status:** Production Ready
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