Delete api_example.py

api_example.py

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-"""
-API Example untuk Sentiment Analysis
-Contoh penggunaan model secara programmatic (tanpa Gradio UI)
-Berguna untuk integrasi dengan sistem lain
-"""
-
-from app import SentimentAnalyzer
-import json
-
-def example_basic_usage():
-    """Contoh penggunaan dasar"""
-    print("=" * 60)
-    print("EXAMPLE 1: Basic Usage")
-    print("=" * 60)
-    
-    # Initialize analyzer
-    analyzer = SentimentAnalyzer()
-    
-    # Analyze single text
-    text = "Bantuan bencana sangat lambat, sudah 3 hari belum dapat makanan!"
-    result = analyzer.analyze(text)
-    
-    print(f"\nText: {text}")
-    print(f"Result: {json.dumps(result, indent=2, ensure_ascii=False)}")
-
-def example_batch_processing():
-    """Contoh batch processing untuk admin bencana"""
-    print("\n" + "=" * 60)
-    print("EXAMPLE 2: Batch Processing for Emergency Admin")
-    print("=" * 60)
-    
-    analyzer = SentimentAnalyzer()
-    
-    # Simulasi pesan dari masyarakat
-    messages = [
-        "Posko pengungsian penuh, tidak ada tempat tidur!",
-        "Terima kasih atas bantuan yang cepat",
-        "Kapan distribusi bantuan selanjutnya?",
-        "Air bersih habis, kondisi darurat!",
-        "Tim medis sangat membantu, terima kasih",
-        "Bagaimana cara mendapatkan bantuan?",
-        "Hadeh lambat banget nih pelayanan!",
-        "Alhamdulillah bantuan sudah sampai"
-    ]
-    
-    # Batch analysis
-    results = analyzer.batch_analyze(messages)
-    
-    # Categorize by priority
-    high_priority = []  # NEGATIVE with high confidence
-    medium_priority = []  # NEGATIVE with medium confidence
-    low_priority = []  # POSITIVE or NEUTRAL
-    
-    for msg, result in zip(messages, results):
-        if result['label'] == 'NEGATIVE':
-            if result['confidence'] >= 0.8:
-                high_priority.append((msg, result))
-            else:
-                medium_priority.append((msg, result))
-        else:
-            low_priority.append((msg, result))
-    
-    # Display results
-    print(f"\n📊 Processing Summary:")
-    print(f"   Total messages: {len(messages)}")
-    print(f"   🔴 High Priority (Urgent): {len(high_priority)}")
-    print(f"   🟡 Medium Priority: {len(medium_priority)}")
-    print(f"   🟢 Low Priority: {len(low_priority)}")
-    
-    print(f"\n🚨 HIGH PRIORITY COMPLAINTS (Need immediate action):")
-    for i, (msg, result) in enumerate(high_priority, 1):
-        print(f"   {i}. {msg}")
-        print(f"      → Confidence: {result['confidence']:.1%}")
-    
-    if not high_priority:
-        print("   ✅ No urgent complaints!")
-
-def example_filtering_workflow():
-    """Contoh workflow filtering untuk admin"""
-    print("\n" + "=" * 60)
-    print("EXAMPLE 3: Admin Workflow - Smart Filtering")
-    print("=" * 60)
-    
-    analyzer = SentimentAnalyzer()
-    
-    # Simulasi 1000 pesan (simplified to 20 for demo)
-    all_messages = [
-        "Bantuan lambat sekali!",
-        "Terima kasih",
-        "Kapan bantuan tiba?",
-        "Kondisi darurat, tidak ada air!",
-        "Tim bantuan sangat baik",
-        "Bagaimana cara daftar?",
-        "Parah banget pelayanan!",
-        "Sudah dapat bantuan, terima kasih",
-        "Tolong segera kirim bantuan!",
-        "Lokasi kami masih terisolasi!",
-        "Alhamdulillah selamat",
-        "Apa syarat bantuan?",
-        "Gak ada koordinasi sama sekali!",
-        "Tim medis cepat tanggap",
-        "Berapa lama proses bantuan?",
-        "Posko penuh, gak bisa masuk!",
-        "Relawan sangat membantu",
-        "Info jalur evakuasi?",
-        "Hadeh ribet banget!",
-        "Sukses untuk tim bantuan"
-    ]
-    
-    print(f"\n📥 Receiving {len(all_messages)} messages...")
-    
-    # Analyze all
-    results = analyzer.batch_analyze(all_messages)
-    
-    # Smart filtering
-    needs_action = []
-    for msg, result in zip(all_messages, results):
-        if result['label'] == 'NEGATIVE' and result['confidence'] >= 0.7:
-            needs_action.append({
-                'message': msg,
-                'confidence': result['confidence'],
-                'priority': 'HIGH' if result['confidence'] >= 0.8 else 'MEDIUM'
-            })
-    
-    # Sort by confidence (most confident first)
-    needs_action.sort(key=lambda x: x['confidence'], reverse=True)
-    
-    print(f"\n✅ Filtered results:")
-    print(f"   Original messages: {len(all_messages)}")
-    print(f"   Need action: {len(needs_action)}")
-    print(f"   Time saved: ~{100 - (len(needs_action)/len(all_messages)*100):.0f}%")
-    
-    print(f"\n📋 Messages requiring action (sorted by confidence):")
-    for i, item in enumerate(needs_action, 1):
-        priority_icon = "🔴" if item['priority'] == 'HIGH' else "🟡"
-        print(f"   {i}. {priority_icon} [{item['priority']}] {item['message']}")
-        print(f"      Confidence: {item['confidence']:.1%}")
-
-def example_json_export():
-    """Contoh export hasil ke JSON untuk integrasi sistem lain"""
-    print("\n" + "=" * 60)
-    print("EXAMPLE 4: JSON Export for System Integration")
-    print("=" * 60)
-    
-    analyzer = SentimentAnalyzer()
-    
-    messages = [
-        "Bantuan sangat lambat!",
-        "Terima kasih atas bantuan",
-        "Kapan bantuan tiba?"
-    ]
-    
-    # Analyze and prepare for export
-    export_data = {
-        'timestamp': '2026-01-31T10:30:00',
-        'total_analyzed': len(messages),
-        'results': []
-    }
-    
-    for msg in messages:
-        result = analyzer.analyze(msg)
-        export_data['results'].append({
-            'text': msg,
-            'sentiment': result['label'],
-            'category': result['kategori'],
-            'confidence': round(result['confidence'], 4),
-            'interpretation': result['interpretation']
-        })
-    
-    # Convert to JSON
-    json_output = json.dumps(export_data, indent=2, ensure_ascii=False)
-    
-    print("\n📤 JSON Export:")
-    print(json_output)
-    
-    # Save to file (optional)
-    with open('/tmp/sentiment_results.json', 'w', encoding='utf-8') as f:
-        f.write(json_output)
-    
-    print("\n✅ Results exported to: /tmp/sentiment_results.json")
-
-def example_custom_threshold():
-    """Contoh custom threshold untuk use case spesifik"""
-    print("\n" + "=" * 60)
-    print("EXAMPLE 5: Custom Threshold Configuration")
-    print("=" * 60)
-    
-    analyzer = SentimentAnalyzer()
-    
-    text = "Pelayanan agak lambat tapi masih oke"
-    result = analyzer.analyze(text)
-    
-    print(f"\nText: {text}")
-    print(f"Sentiment: {result['label']}")
-    print(f"Confidence: {result['confidence']:.2%}")
-    
-    # Custom threshold untuk prioritas
-    print("\n🔧 Custom Priority Rules:")
-    
-    if result['label'] == 'NEGATIVE':
-        if result['confidence'] >= 0.9:
-            priority = "CRITICAL - Immediate action required"
-        elif result['confidence'] >= 0.7:
-            priority = "HIGH - Action needed within 1 hour"
-        elif result['confidence'] >= 0.5:
-            priority = "MEDIUM - Review within 24 hours"
-        else:
-            priority = "LOW - Monitor"
-    else:
-        priority = "INFO - No action needed"
-    
-    print(f"Priority Level: {priority}")
-
-if __name__ == "__main__":
-    print("\n" + "="*60)
-    print("🔧 SENTIMENT ANALYSIS API - USAGE EXAMPLES")
-    print("="*60)
-    print("Model: w11wo/indonesian-roberta-base-sentiment-classifier")
-    print("="*60)
-    
-    # Run all examples
-    example_basic_usage()
-    example_batch_processing()
-    example_filtering_workflow()
-    example_json_export()
-    example_custom_threshold()
-    
-    print("\n" + "="*60)
-    print("✅ ALL EXAMPLES COMPLETED")
-    print("="*60)
-    print("\n💡 Tips:")
-    print("   - Gunakan batch_analyze() untuk efisiensi tinggi")
-    print("   - Set custom threshold sesuai kebutuhan use case")
-    print("   - Export hasil ke JSON untuk integrasi sistem lain")
-    print("   - Prioritas keluhan berdasarkan confidence score")