{"id":29190855,"url":"https://github.com/bforbilly24/folderingbyimagecaptioning-fastapi-backend-restapi","last_synced_at":"2026-04-27T00:31:51.020Z","repository":{"id":301362560,"uuid":"1009014565","full_name":"bforbilly24/folderingbyimagecaptioning-fastapi-backend-restapi","owner":"bforbilly24","description":"FastAPI backend for AI-powered image categorization using ResNet50+LSTM. 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This service supports both individual image uploads and folder uploads with real-time progress tracking.\n\n## 🚀 Features\n\n- **AI-Powered Image Captioning**: Uses ResNet50 + LSTM model for generating accurate image descriptions\n- **Automatic Categorization**: Groups images into categories like \"people\", \"activities\", \"objects\", etc.\n- **Multiple Upload Methods**: Support for both individual images and folder uploads\n- **Real-time Progress Tracking**: WebSocket-like progress updates for frontend integration\n- **ZIP Export**: Download organized images with Excel spreadsheet summary\n- **Modern API**: RESTful API with OpenAPI/Swagger documentation\n- **CORS Support**: Ready for frontend integration\n\n## 📋 Prerequisites\n\n- Python 3.11 or higher\n- TensorFlow 2.15.0\n- FastAPI\n- OpenCV, Pillow for image processing\n\n## 🛠️ Installation\n\n### Method 1: Using UV (Recommended)\n\n```bash\n# Clone the repository\ngit clone https://github.com/bforbilly24/folderingbyimagecaptioning-fastapi-backend-restapi.git\ncd folderingbyimagecaptioning-fastapi-backend-restapi\n\n# Install uv if you haven't already\npip install uv\n\n# Install dependencies\nuv sync\n```\n\n### Method 2: Using Virtual Environment\n\n```bash\n# Clone the repository\ngit clone https://github.com/bforbilly24/folderingbyimagecaptioning-fastapi-backend-restapi.git\ncd folderingbyimagecaptioning-fastapi-backend-restapi\n\n# Create virtual environment\npython -m venv .venv\n\n# Activate virtual environment\n# On Windows:\n.venv\\Scripts\\activate\n# On macOS/Linux:\nsource .venv/bin/activate\n\n# Install dependencies\npip install -r requirements.txt\n```\n\n### Method 3: Using Docker\n\n```bash\n# Build the Docker image\ndocker build -t image-foldering-api .\n\n# Run the container\ndocker run -p 8000:8000 image-foldering-api\n```\n\n## 🚀 Running the Application\n\n### Development Mode\n\n```bash\n# Recommended - with hot reload\nuvicorn src.main:app --reload --host 0.0.0.0 --port 8000\n\n# Alternative\npython src/main.py\n```\n\n### Production Mode\n\n```bash\nuvicorn src.main:app --host 0.0.0.0 --port 8000 --workers 4\n```\n\nThe API will be available at:\n- **API Base URL**: http://localhost:8000\n- **Interactive API Documentation**: http://localhost:8000/docs\n- **ReDoc Documentation**: http://localhost:8000/redoc\n\n## 📚 API Documentation\n\n### Core Endpoints\n\n#### 1. Upload Multiple Images\n```http\nPOST /v1/upload-images\nContent-Type: multipart/form-data\n```\n\nUpload multiple images for processing and categorization.\n\n**Parameters:**\n- `files`: Multiple image files (JPG, PNG, JPEG)\n\n**Response (Synchronous):**\n```json\n{\n  \"message\": \"Images processed successfully\",\n  \"zip_path\": \"path/to/hasil_folderisasi.zip\",\n  \"processed_count\": 5,\n  \"spreadsheet_data\": [\n    {\n      \"filename\": \"image1.jpg\",\n      \"caption\": \"person riding bicycle\",\n      \"category\": \"kegiatan\",\n      \"cosine_similarity\": 0.8765,\n      \"bleu_score\": 0.4321\n    }\n  ]\n}\n```\n\n**Response (Asynchronous with Progress Tracking):**\n```json\n{\n  \"task_id\": \"uuid-string\",\n  \"message\": \"Processing started\"\n}\n```\n\n#### 2. Upload Folder\n```http\nPOST /v1/upload-folder\nContent-Type: multipart/form-data\n```\n\nUpload an entire folder of images for processing.\n\n#### 3. Check Progress\n```http\nGET /v1/progress/{task_id}\n```\n\nGet real-time progress of background processing tasks.\n\n**Response:**\n```json\n{\n  \"task_id\": \"uuid-string\",\n  \"status\": \"processing\",\n  \"current_step\": \"Generating captions\",\n  \"progress_percentage\": 45.5,\n  \"processed_images\": 15,\n  \"total_images\": 33,\n  \"result\": null\n}\n```\n\n#### 4. Download Results\n```http\nGET /v1/download/{zip_filename}\n```\n\nDownload the ZIP file containing categorized images and Excel summary.\n\nFor complete API documentation, visit: http://localhost:8000/docs\n\n## 🏗️ Project Structure\n\n```\n├── src/\n│   ├── main.py                 # FastAPI application entry point\n│   └── app/\n│       ├── __init__.py\n│       ├── config/\n│       │   └── settings.py     # Configuration settings\n│       ├── controllers/\n│       │   └── api/\n│       │       └── ImageFolderController.py\n│       ├── models/\n│       │   └── ImageModel.py   # Pydantic models\n│       ├── routes/\n│       │   └── v1.py          # API routes\n│       ├── services/\n│       │   ├── ImageCaptionService.py\n│       │   ├── ProgressTracker.py\n│       │   └── ServiceFactory.py\n│       └── ml_models/\n│           ├── v2_image_captioning_resnet50_lstm.h5\n│           └── v2_tokenizer.pkl\n├── assets/                     # Sample images for testing\n├── API_DOCUMENTATION.md        # Detailed API documentation\n├── requirements.txt\n├── pyproject.toml\n└── README.md\n```\n\n## 🎯 Usage Examples\n\n### Frontend Integration (JavaScript)\n\n#### Synchronous Upload\n```javascript\nconst formData = new FormData();\nfiles.forEach(file =\u003e formData.append('files', file));\n\nconst response = await fetch('http://localhost:8000/v1/upload-images', {\n    method: 'POST',\n    body: formData\n});\n\nconst result = await response.json();\nconsole.log('Processing complete:', result);\n```\n\n#### Asynchronous Upload with Progress Tracking\n```javascript\n// Start upload\nconst uploadResponse = await fetch('http://localhost:8000/v1/upload-images', {\n    method: 'POST',\n    body: formData\n});\n\nconst { task_id } = await uploadResponse.json();\n\n// Poll for progress\nconst checkProgress = async () =\u003e {\n    const progressResponse = await fetch(`http://localhost:8000/v1/progress/${task_id}`);\n    const progress = await progressResponse.json();\n    \n    if (progress.status === 'completed') {\n        console.log('Processing complete:', progress.result);\n        return;\n    }\n    \n    console.log(`Progress: ${progress.progress_percentage}% - ${progress.current_step}`);\n    setTimeout(checkProgress, 1000); // Check every second\n};\n\ncheckProgress();\n```\n\n### cURL Examples\n\n#### Upload Images\n```bash\ncurl -X POST \"http://localhost:8000/v1/upload-images\" \\\n     -F \"files=@image1.jpg\" \\\n     -F \"files=@image2.jpg\" \\\n     -F \"files=@image3.jpg\"\n```\n\n#### Check Progress\n```bash\ncurl -X GET \"http://localhost:8000/v1/progress/your-task-id\"\n```\n\n#### Download Results\n```bash\ncurl -X GET \"http://localhost:8000/v1/download/hasil_folderisasi.zip\" \\\n     --output categorized_images.zip\n```\n\n## 🔧 Configuration\n\nKey configuration options in `src/app/config/settings.py`:\n\n```python\nUPLOAD_DIR = \"uploads\"           # Directory for uploaded files\nOUTPUT_DIR = \"folderisasi\"       # Directory for processed results\nMODEL_PATH = \"src/app/ml_models/v2_image_captioning_resnet50_lstm.h5\"\nTOKENIZER_PATH = \"src/app/ml_models/v2_tokenizer.pkl\"\n```\n\n## 🐳 Docker Deployment\n\n### Dockerfile Example\n```dockerfile\nFROM python:3.11-slim\n\nWORKDIR /app\n\nCOPY requirements.txt .\nRUN pip install --no-cache-dir -r requirements.txt\n\nCOPY . .\n\nEXPOSE 8000\n\nCMD [\"uvicorn\", \"src.main:app\", \"--host\", \"0.0.0.0\", \"--port\", \"8000\"]\n```\n\n### Docker Compose\n```yaml\nversion: '3.8'\nservices:\n  api:\n    build: .\n    ports:\n      - \"8000:8000\"\n    volumes:\n      - ./uploads:/app/uploads\n      - ./folderisasi:/app/folderisasi\n    environment:\n      - PYTHONPATH=/app\n```\n\n## 🧪 Testing\n\n### Sample Images\nUse the images in the `assets/` folder for testing:\n\n```bash\n# Test with sample images\ncurl -X POST \"http://localhost:8000/v1/upload-images\" \\\n     -F \"files=@assets/tes_1.jpeg\" \\\n     -F \"files=@assets/tes_2.jpeg\"\n```\n\n### API Testing with Swagger UI\nVisit http://localhost:8000/docs for interactive API testing.\n\n## 🚨 Troubleshooting\n\n### Common Issues\n\n#### Model Files Missing\n```bash\n# Ensure model files exist\nls src/app/ml_models/\n# Should contain:\n# - v2_image_captioning_resnet50_lstm.h5\n# - v2_tokenizer.pkl\n```\n\n#### Permission Issues\n```bash\n# On Unix systems, ensure proper permissions\nchmod -R 755 uploads/ folderisasi/\n```\n\n#### CORS Issues\nUpdate CORS settings in `src/main.py`:\n```python\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=[\"http://localhost:3000\", \"http://localhost:5173\"],\n    allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"],\n)\n```\n\n#### Memory Issues\nFor large image batches, consider:\n- Reducing batch size\n- Increasing system RAM\n- Using Docker with memory limits\n\n### Logs and Debugging\n\nEnable debug logging:\n```python\nimport logging\nlogging.basicConfig(level=logging.DEBUG)\n```\n\n## 📈 Performance Considerations\n\n- **Image Size**: Larger images take more time to process\n- **Batch Size**: Processing 10-20 images at once is optimal\n- **Memory Usage**: Each image requires ~50MB RAM during processing\n- **Storage**: Processed results are stored temporarily and cleaned up on shutdown\n\n## 🤝 Contributing\n\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add some amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n## 📄 License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n## 🙋‍♂️ Support\n\nFor questions and support:\n- Check the [API Documentation](API_DOCUMENTATION.md)\n- Visit the interactive docs at http://localhost:8000/docs\n- Open an issue on GitHub\n\n---\n\n**Happy Coding! 🚀**\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbforbilly24%2Ffolderingbyimagecaptioning-fastapi-backend-restapi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbforbilly24%2Ffolderingbyimagecaptioning-fastapi-backend-restapi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbforbilly24%2Ffolderingbyimagecaptioning-fastapi-backend-restapi/lists"}