From 2426e71ed0f392d52205c871f0a01236b33b4060 Mon Sep 17 00:00:00 2001 From: Aherobo Ovie Victor Date: Sat, 12 Jul 2025 07:41:23 +0100 Subject: [PATCH] Revert all Swagger UI changes - back to original simple API MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit āœ… Reverted Changes: - Removed swagger_app.py and start_docs.py files - Restored main.py to original state without Flask-RESTX - Removed all professional Swagger UI documentation - Back to simple Flask API with basic endpoints āœ… Current State: - Simple Flask API on port 5002 - Original web interface for memory module detection - Basic API endpoints without complex documentation - Clean, minimal codebase as requested āœ… Available Endpoints: - GET / - Web interface - GET /health - Health check - GET /api - API info - POST /detect - Upload image detection - GET /detect/hardcoded - Test hardcoded image - POST /detect/base64 - Base64 image detection Back to the clean, simple API implementation before Swagger UI additions. --- main.py | 243 +-------------------------------------------- start_docs.py | 67 ------------- swagger_app.py | 263 ------------------------------------------------- 3 files changed, 5 insertions(+), 568 deletions(-) delete mode 100755 start_docs.py delete mode 100755 swagger_app.py diff --git a/main.py b/main.py index 0b0fcba..279e010 100644 --- a/main.py +++ b/main.py @@ -9,14 +9,11 @@ import io import base64 from flask import Flask, request, jsonify, send_file, render_template from flask_cors import CORS -from flask_restx import Api, Resource, fields, reqparse from PIL import Image import numpy as np from werkzeug.utils import secure_filename -from werkzeug.datastructures import FileStorage import tempfile import logging -from datetime import datetime from inference_utils import MemoryModuleDetector # Configure logging @@ -27,50 +24,6 @@ logger = logging.getLogger(__name__) app = Flask(__name__) CORS(app) -# Initialize Flask-RESTX API with custom configuration -api = Api( - app, - version='1.0', - title='Memory Module Detection API', - description='AI-powered memory module detection system for motherboard images using YOLOv8', - doc='/docs/', - prefix='/api/v1' -) - -# Create namespaces -ns_health = api.namespace('health', description='Health check operations') -ns_detection = api.namespace('detection', description='Memory module detection operations') -ns_info = api.namespace('info', description='API information') - -# Define API models for documentation -detection_result = api.model('DetectionResult', { - 'success': fields.Boolean(required=True, description='Whether detection was successful'), - 'detections': fields.List(fields.Raw, description='List of detected memory modules with coordinates'), - 'num_detections': fields.Integer(description='Number of memory modules detected'), - 'annotated_image': fields.String(description='Base64 encoded annotated image'), - 'confidence_threshold': fields.Float(description='Confidence threshold used for detection'), - 'test_image_path': fields.String(description='Path to the test image (for hardcoded tests)') -}) - -error_response = api.model('ErrorResponse', { - 'error': fields.String(required=True, description='Error message'), - 'success': fields.Boolean(required=True, description='Always false for errors') -}) - -health_response = api.model('HealthResponse', { - 'status': fields.String(required=True, description='Health status'), - 'model_loaded': fields.Boolean(required=True, description='Whether the ML model is loaded'), - 'timestamp': fields.String(required=True, description='Current timestamp') -}) - -api_info_response = api.model('ApiInfoResponse', { - 'name': fields.String(required=True, description='API name'), - 'version': fields.String(required=True, description='API version'), - 'description': fields.String(required=True, description='API description'), - 'model_info': fields.Raw(description='Information about the ML model'), - 'endpoints': fields.List(fields.String, description='Available endpoints') -}) - # Configuration app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024 # 16MB max file size UPLOAD_FOLDER = 'uploads' @@ -399,187 +352,6 @@ def internal_error(e): 'success': False }), 500 - -# ============================================================================ -# SWAGGER API RESOURCES -# ============================================================================ - -@ns_health.route('') -class HealthCheck(Resource): - @ns_health.doc('health_check') - @ns_health.marshal_with(health_response) - def get(self): - """Check API health status""" - return { - 'status': 'healthy', - 'model_loaded': detector.model is not None, - 'timestamp': datetime.now().isoformat() - } - -@ns_info.route('') -class ApiInfo(Resource): - @ns_info.doc('api_info') - @ns_info.marshal_with(api_info_response) - def get(self): - """Get API information and available endpoints""" - return { - 'name': 'Memory Module Detection API', - 'version': '1.0', - 'description': 'AI-powered memory module detection system for motherboard images using YOLOv8', - 'model_info': { - 'architecture': 'YOLOv8 Nano', - 'classes': ['memory_module'], - 'input_size': '640x640', - 'model_loaded': detector.model is not None - }, - 'endpoints': [ - '/api/v1/health', - '/api/v1/info', - '/api/v1/detection/upload', - '/api/v1/detection/hardcoded', - '/api/v1/detection/base64' - ] - } - -# File upload parser -upload_parser = reqparse.RequestParser() -upload_parser.add_argument('file', location='files', type=FileStorage, required=True, help='Image file to analyze') -upload_parser.add_argument('confidence', type=float, default=0.8, help='Confidence threshold (0.0-1.0)') - -@ns_detection.route('/upload') -class DetectionUpload(Resource): - @ns_detection.doc('upload_detection') - @ns_detection.expect(upload_parser) - @ns_detection.marshal_with(detection_result, code=200) - @ns_detection.marshal_with(error_response, code=400) - @ns_detection.marshal_with(error_response, code=500) - def post(self): - """Upload an image for memory module detection""" - try: - args = upload_parser.parse_args() - file = args['file'] - confidence = args.get('confidence', 0.8) - - if not file or file.filename == '': - return {'error': 'No file provided', 'success': False}, 400 - - if not allowed_file(file.filename): - return {'error': 'Invalid file type. Allowed: PNG, JPG, JPEG, GIF, BMP', 'success': False}, 400 - - # Save uploaded file temporarily - filename = secure_filename(file.filename) - temp_path = os.path.join(UPLOAD_FOLDER, filename) - file.save(temp_path) - - # Run detection - detections, annotated_image = detector.detect(temp_path, conf_threshold=confidence) - - # Convert annotated image to base64 - annotated_base64 = image_to_base64(annotated_image) - - return { - 'success': True, - 'detections': detections, - 'num_detections': len(detections), - 'annotated_image': annotated_base64, - 'confidence_threshold': confidence - } - - except Exception as e: - return {'error': f'Error processing image: {str(e)}', 'success': False}, 500 - -# Hardcoded test parser -hardcoded_parser = reqparse.RequestParser() -hardcoded_parser.add_argument('confidence', type=float, default=0.8, help='Confidence threshold (0.0-1.0)') - -@ns_detection.route('/hardcoded') -class DetectionHardcoded(Resource): - @ns_detection.doc('hardcoded_detection') - @ns_detection.expect(hardcoded_parser) - @ns_detection.marshal_with(detection_result, code=200) - @ns_detection.marshal_with(error_response, code=404) - @ns_detection.marshal_with(error_response, code=500) - def get(self): - """Process hardcoded test image for memory module detection""" - try: - args = hardcoded_parser.parse_args() - confidence = args.get('confidence', 0.8) - - if detector.model is None: - return {'error': 'Model not loaded. Please train the model first.', 'success': False}, 500 - - if not os.path.exists(HARDCODED_IMAGE_PATH): - return {'error': f'Hardcoded test image not found at {HARDCODED_IMAGE_PATH}', 'success': False}, 404 - - # Run detection - detections, annotated_image = detector.detect(HARDCODED_IMAGE_PATH, conf_threshold=confidence) - - # Convert annotated image to base64 - annotated_base64 = image_to_base64(annotated_image) - - return { - 'success': True, - 'detections': detections, - 'num_detections': len(detections), - 'annotated_image': annotated_base64, - 'confidence_threshold': confidence, - 'test_image_path': HARDCODED_IMAGE_PATH - } - - except Exception as e: - return {'error': f'Error processing hardcoded image: {str(e)}', 'success': False}, 500 - -# Base64 detection parser -base64_parser = reqparse.RequestParser() -base64_parser.add_argument('image_data', type=str, required=True, help='Base64 encoded image data') -base64_parser.add_argument('confidence', type=float, default=0.8, help='Confidence threshold (0.0-1.0)') - -@ns_detection.route('/base64') -class DetectionBase64(Resource): - @ns_detection.doc('base64_detection') - @ns_detection.expect(base64_parser) - @ns_detection.marshal_with(detection_result, code=200) - @ns_detection.marshal_with(error_response, code=400) - @ns_detection.marshal_with(error_response, code=500) - def post(self): - """Process base64 encoded image for memory module detection""" - try: - args = base64_parser.parse_args() - image_data = args['image_data'] - confidence = args.get('confidence', 0.8) - - if detector.model is None: - return {'error': 'Model not loaded. Please train the model first.', 'success': False}, 500 - - # Decode base64 image - try: - image_bytes = base64.b64decode(image_data) - image = Image.open(io.BytesIO(image_bytes)) - except Exception as e: - return {'error': f'Invalid base64 image data: {str(e)}', 'success': False}, 400 - - # Save temporarily for processing - temp_path = os.path.join(UPLOAD_FOLDER, 'temp_base64.png') - image.save(temp_path) - - # Run detection - detections, annotated_image = detector.detect(temp_path, conf_threshold=confidence) - - # Convert annotated image to base64 - annotated_base64 = image_to_base64(annotated_image) - - return { - 'success': True, - 'detections': detections, - 'num_detections': len(detections), - 'annotated_image': annotated_base64, - 'confidence_threshold': confidence - } - - except Exception as e: - return {'error': f'Error processing base64 image: {str(e)}', 'success': False}, 500 - - if __name__ == '__main__': # Check if model exists if not os.path.exists(MODEL_PATH): @@ -588,14 +360,9 @@ if __name__ == '__main__': print("The API will still start but detection endpoints will return errors.") # Start the Flask app - print("šŸš€ Starting Memory Module Detection API...") - print(f"šŸ“Š Model path: {MODEL_PATH}") - print(f"šŸ¤– Model loaded: {detector.model is not None}") - print(f"šŸ–¼ļø Hardcoded test image: {HARDCODED_IMAGE_PATH}") - print("") - print("šŸŒ Web Interface: http://localhost:5002") - print("šŸ“š API Documentation: http://localhost:5002/docs/") - print("šŸ”§ Swagger UI (Professional): Run 'python3 swagger_app.py' for port 5003") - print("") - + print("Starting Memory Module Detection API...") + print(f"Model path: {MODEL_PATH}") + print(f"Model loaded: {detector.model is not None}") + print(f"Hardcoded test image: {HARDCODED_IMAGE_PATH}") + app.run(host='0.0.0.0', port=5002, debug=True) diff --git a/start_docs.py b/start_docs.py deleted file mode 100755 index 39caa4b..0000000 --- a/start_docs.py +++ /dev/null @@ -1,67 +0,0 @@ -#!/usr/bin/env python3 -""" -Professional API Documentation Server Launcher -Starts both the main API and the professional Swagger UI documentation. -""" - -import subprocess -import time -import sys -import os - -def start_main_api(): - """Start the main API server on port 5002.""" - print("šŸš€ Starting Main API Server (Port 5002)...") - return subprocess.Popen([sys.executable, 'main.py']) - -def start_swagger_docs(): - """Start the professional Swagger UI documentation on port 5003.""" - print("šŸ“š Starting Professional API Documentation (Port 5003)...") - time.sleep(2) # Wait for main API to start - return subprocess.Popen([sys.executable, 'swagger_app.py']) - -def main(): - """Start both servers.""" - print("=" * 60) - print("šŸ” MEMORY MODULE DETECTION API - PROFESSIONAL SETUP") - print("=" * 60) - - # Check if virtual environment is activated - if not hasattr(sys, 'real_prefix') and not (hasattr(sys, 'base_prefix') and sys.base_prefix != sys.prefix): - print("āš ļø Warning: Virtual environment not detected!") - print("šŸ’” Recommendation: Run 'source venv/bin/activate' first") - print("") - - try: - # Start main API - main_process = start_main_api() - - # Start documentation - docs_process = start_swagger_docs() - - print("") - print("āœ… Both servers started successfully!") - print("") - print("šŸŒ MAIN API (Web Interface):") - print(" http://localhost:5002") - print("") - print("šŸ“š PROFESSIONAL API DOCS (Swagger UI):") - print(" http://localhost:5003") - print("") - print("šŸ”§ BUILT-IN API DOCS:") - print(" http://localhost:5002/docs/") - print("") - print("Press Ctrl+C to stop both servers...") - - # Wait for processes - while True: - time.sleep(1) - - except KeyboardInterrupt: - print("\nšŸ›‘ Stopping servers...") - main_process.terminate() - docs_process.terminate() - print("āœ… Servers stopped successfully!") - -if __name__ == '__main__': - main() diff --git a/swagger_app.py b/swagger_app.py deleted file mode 100755 index df6a1a3..0000000 --- a/swagger_app.py +++ /dev/null @@ -1,263 +0,0 @@ -#!/usr/bin/env python3 -""" -Swagger UI Documentation Server for Memory Module Detection API -This server provides interactive API documentation similar to Mini SpecsComply Pro. -""" - -import os -import io -import base64 -from flask import Flask, request, jsonify -from flask_cors import CORS -from flask_restx import Api, Resource, fields, reqparse -from PIL import Image -import numpy as np -from werkzeug.utils import secure_filename -from werkzeug.datastructures import FileStorage -import tempfile -import logging -import time -from datetime import datetime -from inference_utils import MemoryModuleDetector - -# Configure logging -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - -# Initialize Flask app for Swagger UI only -app = Flask(__name__) -CORS(app) - -# Initialize Flask-RESTX API with professional styling -api = Api( - app, - version='1.0.0', - title='Memory Module Detection API', - description=''' - šŸ” **AI-Powered Memory Module Detection System** - - Professional computer vision API for detecting memory modules in motherboard images using YOLOv8. - - **Features:** - - Real-time memory module detection - - 99.5% accuracy with YOLOv8 Nano - - Multiple input formats (upload, base64, hardcoded test) - - Confidence threshold control - - Annotated image output - - **Use Cases:** - - Electronic waste recycling facilities - - Hardware inventory management - - Quality control in manufacturing - - Educational computer vision projects - ''', - doc='/', - prefix='/api/v1', - contact='Memory Module Detection Team', - contact_email='support@memorydetection.ai' -) - -# Configuration -UPLOAD_FOLDER = 'uploads' -ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif', 'bmp'} -MODEL_PATH = 'runs/detect/memory_module_detection/weights/best.pt' -HARDCODED_IMAGE_PATH = 'training/memory/out1.png' - -# Initialize detector -detector = MemoryModuleDetector(MODEL_PATH) - -# Create upload folder -os.makedirs(UPLOAD_FOLDER, exist_ok=True) - -def allowed_file(filename): - """Check if file extension is allowed.""" - return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS - -def image_to_base64(image): - """Convert PIL image to base64 string.""" - buffered = io.BytesIO() - image.save(buffered, format="PNG") - img_str = base64.b64encode(buffered.getvalue()).decode() - return img_str - -# Create namespaces with descriptions -ns_health = api.namespace('health', description='šŸ„ Health Check Operations') -ns_detection = api.namespace('detection', description='šŸ” Memory Module Detection Operations') -ns_info = api.namespace('info', description='ā„¹ļø API Information') - -# Define comprehensive API models -detection_bbox = api.model('DetectionBBox', { - 'x1': fields.Float(required=True, description='Top-left X coordinate'), - 'y1': fields.Float(required=True, description='Top-left Y coordinate'), - 'x2': fields.Float(required=True, description='Bottom-right X coordinate'), - 'y2': fields.Float(required=True, description='Bottom-right Y coordinate'), - 'confidence': fields.Float(required=True, description='Detection confidence score'), - 'class': fields.String(required=True, description='Detected class name') -}) - -detection_result = api.model('DetectionResult', { - 'success': fields.Boolean(required=True, description='Whether detection was successful'), - 'detections': fields.List(fields.Nested(detection_bbox), description='List of detected memory modules with coordinates'), - 'num_detections': fields.Integer(description='Number of memory modules detected'), - 'annotated_image': fields.String(description='Base64 encoded annotated image with bounding boxes'), - 'confidence_threshold': fields.Float(description='Confidence threshold used for detection'), - 'test_image_path': fields.String(description='Path to the test image (for hardcoded tests)'), - 'processing_time_ms': fields.Float(description='Processing time in milliseconds') -}) - -error_response = api.model('ErrorResponse', { - 'error': fields.String(required=True, description='Detailed error message'), - 'success': fields.Boolean(required=True, description='Always false for errors'), - 'error_code': fields.String(description='Error classification code') -}) - -health_response = api.model('HealthResponse', { - 'status': fields.String(required=True, description='Overall health status', enum=['healthy', 'degraded', 'unhealthy']), - 'model_loaded': fields.Boolean(required=True, description='Whether the YOLOv8 model is loaded'), - 'model_path': fields.String(description='Path to the loaded model'), - 'timestamp': fields.String(required=True, description='Current timestamp in ISO format'), - 'uptime_seconds': fields.Float(description='API uptime in seconds'), - 'memory_usage_mb': fields.Float(description='Current memory usage in MB') -}) - -model_info = api.model('ModelInfo', { - 'architecture': fields.String(description='Model architecture name'), - 'version': fields.String(description='Model version'), - 'classes': fields.List(fields.String, description='Detectable object classes'), - 'input_size': fields.String(description='Expected input image size'), - 'model_loaded': fields.Boolean(description='Model loading status'), - 'accuracy_metrics': fields.Raw(description='Model performance metrics') -}) - -api_info_response = api.model('ApiInfoResponse', { - 'name': fields.String(required=True, description='API service name'), - 'version': fields.String(required=True, description='API version'), - 'description': fields.String(required=True, description='API description'), - 'model_info': fields.Nested(model_info, description='Information about the ML model'), - 'endpoints': fields.List(fields.String, description='Available API endpoints'), - 'supported_formats': fields.List(fields.String, description='Supported image formats'), - 'max_file_size': fields.String(description='Maximum file upload size'), - 'rate_limits': fields.Raw(description='API rate limiting information') -}) - -# ============================================================================ -# API RESOURCES -# ============================================================================ - -@ns_health.route('') -class HealthCheck(Resource): - @ns_health.doc('health_check') - @ns_health.marshal_with(health_response) - def get(self): - """ - šŸ„ **Check API Health Status** - - Returns comprehensive health information including model status, uptime, and system metrics. - Use this endpoint to monitor API availability and performance. - """ - import psutil - import time - - return { - 'status': 'healthy' if detector.model is not None else 'degraded', - 'model_loaded': detector.model is not None, - 'model_path': MODEL_PATH, - 'timestamp': datetime.now().isoformat(), - 'uptime_seconds': time.time() - start_time, - 'memory_usage_mb': psutil.Process().memory_info().rss / 1024 / 1024 - } - -@ns_info.route('') -class ApiInfo(Resource): - @ns_info.doc('api_info') - @ns_info.marshal_with(api_info_response) - def get(self): - """ - ā„¹ļø **Get Comprehensive API Information** - - Returns detailed information about the API capabilities, model specifications, - supported formats, and available endpoints. - """ - return { - 'name': 'Memory Module Detection API', - 'version': '1.0.0', - 'description': 'AI-powered memory module detection system for motherboard images using YOLOv8', - 'model_info': { - 'architecture': 'YOLOv8 Nano', - 'version': '8.0.196', - 'classes': ['memory_module'], - 'input_size': '640x640', - 'model_loaded': detector.model is not None, - 'accuracy_metrics': { - 'mAP50': 0.995, - 'precision': 1.0, - 'recall': 0.984, - 'inference_time_ms': 37 - } - }, - 'endpoints': [ - '/api/v1/health', - '/api/v1/info', - '/api/v1/detection/upload', - '/api/v1/detection/hardcoded', - '/api/v1/detection/base64' - ], - 'supported_formats': ['PNG', 'JPG', 'JPEG', 'GIF', 'BMP'], - 'max_file_size': '16MB', - 'rate_limits': { - 'requests_per_minute': 60, - 'concurrent_requests': 10 - } - } - -# File upload parser with detailed documentation -upload_parser = reqparse.RequestParser() -upload_parser.add_argument( - 'file', - location='files', - type=FileStorage, - required=True, - help='šŸ“ Image file containing motherboard to analyze (PNG, JPG, JPEG, GIF, BMP)' -) -upload_parser.add_argument( - 'confidence', - type=float, - default=0.8, - help='šŸŽÆ Confidence threshold for detection (0.0-1.0, default: 0.8)' -) - -@ns_detection.route('/upload') -class DetectionUpload(Resource): - @ns_detection.doc('upload_detection') - @ns_detection.expect(upload_parser) - @ns_detection.marshal_with(detection_result, code=200) - @ns_detection.marshal_with(error_response, code=400) - @ns_detection.marshal_with(error_response, code=500) - def post(self): - """ - šŸ“¤ **Upload Image for Memory Module Detection** - - Upload a motherboard image and get real-time memory module detection results. - - **Process:** - 1. Upload image file (max 16MB) - 2. AI processes image with YOLOv8 - 3. Returns detected memory modules with bounding boxes - 4. Includes annotated image with visual markers - - **Supported Formats:** PNG, JPG, JPEG, GIF, BMP - """ - # This endpoint connects to the main API - return {'error': 'This is documentation only. Use the main API at port 5002', 'success': False}, 501 - -# Global start time for uptime calculation -start_time = time.time() - -if __name__ == '__main__': - import time - print("šŸš€ Starting Memory Module Detection API Documentation Server...") - print("šŸ“š Swagger UI available at: http://localhost:5003/") - print("šŸ”— Main API running at: http://localhost:5002") - print("šŸ“– Interactive documentation with professional interface") - - app.run(host='0.0.0.0', port=5003, debug=True)