Fix file upload issues and add Swagger UI API documentation
✅ Frontend File Upload Fixes: - Fixed file upload reset issue - can now upload multiple files without page reload - Added 'Change File' and 'Upload Another Image' buttons for better UX - Fixed double-click file selection issue with proper event handling - Improved drag & drop functionality with proper event propagation - Added visual feedback for file selection and processing states ✅ Swagger UI API Documentation: - Created api_docs.py with comprehensive Swagger UI documentation - Added Flask-RESTX for professional API documentation interface - Documented all 3 detection endpoints with request/response models - Added health check endpoint documentation - Included detailed parameter descriptions and example responses - Available at http://localhost:5003/docs/ for interactive API testing ✅ Enhanced User Experience: - Seamless file upload workflow without page reloads - Clear visual indicators for file selection and processing - Professional API documentation for developers and QA testing - Consistent 80% confidence threshold across all interfaces ✅ Technical Improvements: - Better event handling for file inputs and drag & drop - Proper cleanup of uploaded files and UI state - Comprehensive error handling and user feedback - Interactive API documentation with live testing capabilities
This commit is contained in:
Aherobo Ovie Victor
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#!/usr/bin/env python3
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"""
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Swagger UI API Documentation for Memory Module Detection
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This creates a separate API documentation interface using Flask-RESTX
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"""
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from flask import Flask, request, jsonify
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from flask_restx import Api, Resource, fields, reqparse
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from flask_cors import CORS
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from werkzeug.datastructures import FileStorage
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import os
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from inference_utils import MemoryModuleDetector
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# Initialize Flask app with Swagger
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app = Flask(__name__)
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CORS(app)
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# Configure Swagger UI
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api = Api(
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app,
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version='1.0.0',
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title='Memory Module Detection API',
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description='AI-powered memory module detection in motherboard images using YOLOv8',
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doc='/docs/', # Swagger UI will be available at /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='System health and status')
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ns_detect = api.namespace('detect', description='Memory module detection operations')
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# Initialize detector
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MODEL_PATH = 'runs/detect/memory_module_detection/weights/best.pt'
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detector = MemoryModuleDetector(MODEL_PATH)
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# Define models for Swagger documentation
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detection_model = api.model('Detection', {
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'bbox': fields.List(fields.Float, description='Bounding box coordinates [x1, y1, x2, y2]'),
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'confidence': fields.Float(description='Detection confidence score (0.0-1.0)'),
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'class': fields.Integer(description='Class ID (0 for memory_module)'),
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'class_name': fields.String(description='Class name (memory_module)')
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})
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detection_response = api.model('DetectionResponse', {
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'success': fields.Boolean(description='Whether detection was successful'),
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'detections': fields.List(fields.Nested(detection_model), description='List of detected memory modules'),
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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'),
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'original_filename': fields.String(description='Original filename (for uploads)')
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})
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health_response = api.model('HealthResponse', {
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'status': fields.String(description='System health status'),
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'model_loaded': fields.Boolean(description='Whether the AI model is loaded'),
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'model_path': fields.String(description='Path to the AI model file')
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})
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error_response = api.model('ErrorResponse', {
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'success': fields.Boolean(description='Always false for errors'),
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'error': fields.String(description='Error message')
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})
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# Health endpoint
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@ns_health.route('/')
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class Health(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 system health and model 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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'model_path': MODEL_PATH
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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('image', location='files', type=FileStorage, required=True,
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help='Motherboard image file (PNG, JPG, JPEG, GIF, BMP)')
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upload_parser.add_argument('confidence', type=float, default=0.8,
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help='Confidence threshold (0.1-1.0, default: 0.8)')
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@ns_detect.route('/upload')
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class DetectUpload(Resource):
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@ns_detect.doc('detect_upload')
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@ns_detect.expect(upload_parser)
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@ns_detect.marshal_with(detection_response, code=200)
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@ns_detect.marshal_with(error_response, code=400)
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@ns_detect.marshal_with(error_response, code=500)
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def post(self):
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"""Upload and analyze motherboard image for memory modules"""
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try:
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if detector.model is None:
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return {'success': False, 'error': 'Model not loaded'}, 500
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args = upload_parser.parse_args()
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file = args['image']
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confidence = args['confidence']
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if not file:
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return {'success': False, 'error': 'No image file provided'}, 400
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# Save file temporarily
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temp_path = f"temp_{file.filename}"
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file.save(temp_path)
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try:
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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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import io
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import base64
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buffer = io.BytesIO()
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annotated_image.save(buffer, format='PNG')
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annotated_base64 = base64.b64encode(buffer.getvalue()).decode()
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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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'original_filename': file.filename
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}
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finally:
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# Clean up
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if os.path.exists(temp_path):
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os.remove(temp_path)
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except Exception as e:
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return {'success': False, 'error': str(e)}, 500
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# Hardcoded image parser
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hardcoded_parser = reqparse.RequestParser()
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hardcoded_parser.add_argument('confidence', type=float, default=0.8, location='args',
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help='Confidence threshold (0.1-1.0, default: 0.8)')
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@ns_detect.route('/hardcoded')
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class DetectHardcoded(Resource):
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@ns_detect.doc('detect_hardcoded')
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@ns_detect.expect(hardcoded_parser)
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@ns_detect.marshal_with(detection_response, code=200)
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@ns_detect.marshal_with(error_response, code=404)
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@ns_detect.marshal_with(error_response, code=500)
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def get(self):
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"""Analyze predefined test image for memory modules"""
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try:
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if detector.model is None:
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return {'success': False, 'error': 'Model not loaded'}, 500
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args = hardcoded_parser.parse_args()
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confidence = args['confidence']
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test_image_path = 'training/memory/out1.png'
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if not os.path.exists(test_image_path):
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return {'success': False, 'error': f'Test image not found at {test_image_path}'}, 404
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# Run detection
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detections, annotated_image = detector.detect(test_image_path, conf_threshold=confidence)
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# Convert annotated image to base64
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import io
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import base64
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buffer = io.BytesIO()
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annotated_image.save(buffer, format='PNG')
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annotated_base64 = base64.b64encode(buffer.getvalue()).decode()
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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': test_image_path
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}
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except Exception as e:
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return {'success': False, 'error': str(e)}, 500
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# Base64 image model
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base64_model = api.model('Base64Request', {
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'image': fields.String(required=True, description='Base64 encoded image data'),
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'confidence': fields.Float(default=0.8, description='Confidence threshold (0.1-1.0)')
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})
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@ns_detect.route('/base64')
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class DetectBase64(Resource):
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@ns_detect.doc('detect_base64')
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@ns_detect.expect(base64_model)
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@ns_detect.marshal_with(detection_response, code=200)
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@ns_detect.marshal_with(error_response, code=400)
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@ns_detect.marshal_with(error_response, code=500)
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def post(self):
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"""Analyze base64 encoded image for memory modules"""
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try:
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if detector.model is None:
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return {'success': False, 'error': 'Model not loaded'}, 500
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data = request.get_json()
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if not data or 'image' not in data:
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return {'success': False, 'error': 'No base64 image data provided'}, 400
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confidence = data.get('confidence', 0.8)
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# Decode base64 image
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import base64
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import io
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from PIL import Image
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import numpy as np
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try:
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img_data = base64.b64decode(data['image'])
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image = Image.open(io.BytesIO(img_data))
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except Exception as e:
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return {'success': False, 'error': f'Invalid base64 image data: {str(e)}'}, 400
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# Run detection
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detections, annotated_image = detector.detect_from_array(np.array(image), conf_threshold=confidence)
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# Convert annotated image to base64
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buffer = io.BytesIO()
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annotated_image.save(buffer, format='PNG')
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annotated_base64 = base64.b64encode(buffer.getvalue()).decode()
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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 {'success': False, 'error': str(e)}, 500
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if __name__ == '__main__':
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print("Starting Memory Module Detection API with Swagger UI...")
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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("Swagger UI available at: http://localhost:5003/docs/")
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app.run(host='0.0.0.0', port=5003, debug=True)
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