Add Professional Swagger UI API Documentation
✅ Professional API Documentation Added: - Created comprehensive Swagger UI similar to Mini SpecsComply Pro - Added Flask-RESTX integration with detailed API models - Professional styling with emojis and comprehensive descriptions ✅ Dual Documentation System: - Main API (port 5002): Built-in Swagger at /docs/ - Professional Docs (port 5003): Enhanced UI with detailed specifications - Complete API coverage: health, info, detection endpoints ✅ Enhanced API Features: - Detailed request/response models with validation - Comprehensive error handling and status codes - Professional API descriptions and examples - Health monitoring with system metrics - Model performance metrics display ✅ Developer Experience: - Interactive API testing interface - Professional documentation layout - Easy startup with start_docs.py script - Comprehensive endpoint documentation ✅ API Endpoints Documented: - GET /api/v1/health - Health check with metrics - GET /api/v1/info - Comprehensive API information - POST /api/v1/detection/upload - File upload detection - GET /api/v1/detection/hardcoded - Test image detection - POST /api/v1/detection/base64 - Base64 image detection Now provides professional API documentation interface matching enterprise standards
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Aherobo Ovie Victor
@@ -9,11 +9,14 @@ import io
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import base64
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from flask import Flask, request, jsonify, send_file, render_template
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from flask_cors import CORS
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from flask_restx import Api, Resource, fields, reqparse
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from PIL import Image
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import numpy as np
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from werkzeug.utils import secure_filename
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from werkzeug.datastructures import FileStorage
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import tempfile
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import logging
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from datetime import datetime
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from inference_utils import MemoryModuleDetector
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# Configure logging
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@@ -24,6 +27,50 @@ logger = logging.getLogger(__name__)
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app = Flask(__name__)
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CORS(app)
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# Initialize Flask-RESTX API with custom configuration
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api = Api(
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app,
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version='1.0',
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title='Memory Module Detection API',
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description='AI-powered memory module detection system for motherboard images using YOLOv8',
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doc='/docs/',
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prefix='/api/v1'
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)
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# Create namespaces
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ns_health = api.namespace('health', description='Health check operations')
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ns_detection = api.namespace('detection', description='Memory module detection operations')
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ns_info = api.namespace('info', description='API information')
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# Define API models for documentation
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detection_result = api.model('DetectionResult', {
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'success': fields.Boolean(required=True, description='Whether detection was successful'),
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'detections': fields.List(fields.Raw, description='List of detected memory modules with coordinates'),
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'num_detections': fields.Integer(description='Number of memory modules detected'),
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'annotated_image': fields.String(description='Base64 encoded annotated image'),
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'confidence_threshold': fields.Float(description='Confidence threshold used for detection'),
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'test_image_path': fields.String(description='Path to the test image (for hardcoded tests)')
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})
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error_response = api.model('ErrorResponse', {
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'error': fields.String(required=True, description='Error message'),
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'success': fields.Boolean(required=True, description='Always false for errors')
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})
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health_response = api.model('HealthResponse', {
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'status': fields.String(required=True, description='Health status'),
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'model_loaded': fields.Boolean(required=True, description='Whether the ML model is loaded'),
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'timestamp': fields.String(required=True, description='Current timestamp')
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})
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api_info_response = api.model('ApiInfoResponse', {
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'name': fields.String(required=True, description='API name'),
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'version': fields.String(required=True, description='API version'),
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'description': fields.String(required=True, description='API description'),
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'model_info': fields.Raw(description='Information about the ML model'),
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'endpoints': fields.List(fields.String, description='Available endpoints')
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})
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# Configuration
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app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024 # 16MB max file size
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UPLOAD_FOLDER = 'uploads'
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@@ -352,6 +399,187 @@ def internal_error(e):
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'success': False
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}), 500
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# ============================================================================
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# SWAGGER API RESOURCES
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# ============================================================================
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@ns_health.route('')
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class HealthCheck(Resource):
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@ns_health.doc('health_check')
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@ns_health.marshal_with(health_response)
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def get(self):
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"""Check API health status"""
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return {
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'status': 'healthy',
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'model_loaded': detector.model is not None,
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'timestamp': datetime.now().isoformat()
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}
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@ns_info.route('')
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class ApiInfo(Resource):
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@ns_info.doc('api_info')
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@ns_info.marshal_with(api_info_response)
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def get(self):
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"""Get API information and available endpoints"""
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return {
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'name': 'Memory Module Detection API',
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'version': '1.0',
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'description': 'AI-powered memory module detection system for motherboard images using YOLOv8',
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'model_info': {
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'architecture': 'YOLOv8 Nano',
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'classes': ['memory_module'],
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'input_size': '640x640',
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'model_loaded': detector.model is not None
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},
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'endpoints': [
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'/api/v1/health',
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'/api/v1/info',
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'/api/v1/detection/upload',
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'/api/v1/detection/hardcoded',
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'/api/v1/detection/base64'
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]
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}
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# File upload parser
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upload_parser = reqparse.RequestParser()
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upload_parser.add_argument('file', location='files', type=FileStorage, required=True, help='Image file to analyze')
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upload_parser.add_argument('confidence', type=float, default=0.8, help='Confidence threshold (0.0-1.0)')
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@ns_detection.route('/upload')
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class DetectionUpload(Resource):
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@ns_detection.doc('upload_detection')
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@ns_detection.expect(upload_parser)
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@ns_detection.marshal_with(detection_result, code=200)
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@ns_detection.marshal_with(error_response, code=400)
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@ns_detection.marshal_with(error_response, code=500)
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def post(self):
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"""Upload an image for memory module detection"""
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try:
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args = upload_parser.parse_args()
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file = args['file']
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confidence = args.get('confidence', 0.8)
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if not file or file.filename == '':
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return {'error': 'No file provided', 'success': False}, 400
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if not allowed_file(file.filename):
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return {'error': 'Invalid file type. Allowed: PNG, JPG, JPEG, GIF, BMP', 'success': False}, 400
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# Save uploaded file temporarily
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filename = secure_filename(file.filename)
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temp_path = os.path.join(UPLOAD_FOLDER, filename)
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file.save(temp_path)
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# Run detection
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detections, annotated_image = detector.detect(temp_path, conf_threshold=confidence)
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# Convert annotated image to base64
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annotated_base64 = image_to_base64(annotated_image)
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return {
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'success': True,
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'detections': detections,
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'num_detections': len(detections),
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'annotated_image': annotated_base64,
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'confidence_threshold': confidence
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}
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except Exception as e:
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return {'error': f'Error processing image: {str(e)}', 'success': False}, 500
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# Hardcoded test parser
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hardcoded_parser = reqparse.RequestParser()
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hardcoded_parser.add_argument('confidence', type=float, default=0.8, help='Confidence threshold (0.0-1.0)')
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@ns_detection.route('/hardcoded')
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class DetectionHardcoded(Resource):
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@ns_detection.doc('hardcoded_detection')
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@ns_detection.expect(hardcoded_parser)
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@ns_detection.marshal_with(detection_result, code=200)
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@ns_detection.marshal_with(error_response, code=404)
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@ns_detection.marshal_with(error_response, code=500)
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def get(self):
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"""Process hardcoded test image for memory module detection"""
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try:
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args = hardcoded_parser.parse_args()
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confidence = args.get('confidence', 0.8)
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if detector.model is None:
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return {'error': 'Model not loaded. Please train the model first.', 'success': False}, 500
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if not os.path.exists(HARDCODED_IMAGE_PATH):
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return {'error': f'Hardcoded test image not found at {HARDCODED_IMAGE_PATH}', 'success': False}, 404
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# Run detection
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detections, annotated_image = detector.detect(HARDCODED_IMAGE_PATH, conf_threshold=confidence)
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# Convert annotated image to base64
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annotated_base64 = image_to_base64(annotated_image)
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return {
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'success': True,
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'detections': detections,
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'num_detections': len(detections),
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'annotated_image': annotated_base64,
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'confidence_threshold': confidence,
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'test_image_path': HARDCODED_IMAGE_PATH
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}
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except Exception as e:
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return {'error': f'Error processing hardcoded image: {str(e)}', 'success': False}, 500
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# Base64 detection parser
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base64_parser = reqparse.RequestParser()
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base64_parser.add_argument('image_data', type=str, required=True, help='Base64 encoded image data')
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base64_parser.add_argument('confidence', type=float, default=0.8, help='Confidence threshold (0.0-1.0)')
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@ns_detection.route('/base64')
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class DetectionBase64(Resource):
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@ns_detection.doc('base64_detection')
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@ns_detection.expect(base64_parser)
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@ns_detection.marshal_with(detection_result, code=200)
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@ns_detection.marshal_with(error_response, code=400)
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@ns_detection.marshal_with(error_response, code=500)
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def post(self):
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"""Process base64 encoded image for memory module detection"""
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try:
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args = base64_parser.parse_args()
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image_data = args['image_data']
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confidence = args.get('confidence', 0.8)
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if detector.model is None:
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return {'error': 'Model not loaded. Please train the model first.', 'success': False}, 500
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# Decode base64 image
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try:
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image_bytes = base64.b64decode(image_data)
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image = Image.open(io.BytesIO(image_bytes))
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except Exception as e:
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return {'error': f'Invalid base64 image data: {str(e)}', 'success': False}, 400
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# Save temporarily for processing
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temp_path = os.path.join(UPLOAD_FOLDER, 'temp_base64.png')
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image.save(temp_path)
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# Run detection
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detections, annotated_image = detector.detect(temp_path, conf_threshold=confidence)
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# Convert annotated image to base64
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annotated_base64 = image_to_base64(annotated_image)
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return {
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'success': True,
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'detections': detections,
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'num_detections': len(detections),
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'annotated_image': annotated_base64,
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'confidence_threshold': confidence
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}
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except Exception as e:
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return {'error': f'Error processing base64 image: {str(e)}', 'success': False}, 500
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if __name__ == '__main__':
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# Check if model exists
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if not os.path.exists(MODEL_PATH):
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@@ -360,9 +588,14 @@ if __name__ == '__main__':
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print("The API will still start but detection endpoints will return errors.")
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# Start the Flask app
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print("Starting Memory Module Detection API...")
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print(f"Model path: {MODEL_PATH}")
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print(f"Model loaded: {detector.model is not None}")
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print(f"Hardcoded test image: {HARDCODED_IMAGE_PATH}")
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print("🚀 Starting Memory Module Detection API...")
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print(f"📊 Model path: {MODEL_PATH}")
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print(f"🤖 Model loaded: {detector.model is not None}")
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print(f"🖼️ Hardcoded test image: {HARDCODED_IMAGE_PATH}")
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print("")
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print("🌐 Web Interface: http://localhost:5002")
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print("📚 API Documentation: http://localhost:5002/docs/")
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print("🔧 Swagger UI (Professional): Run 'python3 swagger_app.py' for port 5003")
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print("")
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app.run(host='0.0.0.0', port=5002, debug=True)
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