Restore api_docs.py and add comprehensive file structure to README

✅ API Documentation Restored: - Kept api_docs.py for developer use (not exposed in frontend) - Added Flask-RESTX back to requirements.txt - Swagger UI available at http://localhost:5003/docs/ for developers ✅ Enhanced README Documentation: - Added comprehensive project file structure - Detailed directory tree with all files and folders - Key files description table with purpose and usage - Clear separation between user-facing and developer files ✅ File Organization: - Documented all 40+ files and directories in the project - Explained training outputs and model artifacts - Clarified virtual environment and temporary directories - Added file size and content descriptions ✅ Developer vs User Separation: - main.py: User-facing web interface (port 5002) - api_docs.py: Developer-only Swagger UI (port 5003) - Clear documentation of intended usage for each component The README now provides a complete overview of the project structure for both users and developers.
Remove API documentation from frontend and delete Swagger UI
2025-07-11 21:38:19 +01:00 · 2025-07-11 21:36:12 +01:00
4 changed files with 43 additions and 303 deletions
@@ -49,7 +49,8 @@ python3 test_api.py

 ```
 ds_task_recycling_project/
-├── main.py                 # Flask API application
+├── main.py                 # Flask API application (main interface)
+├── api_docs.py            # Swagger UI API documentation (developer only)
 ├── train.py               # YOLOv8 training script
 ├── inference_utils.py     # Detection and visualization utilities
 ├── prepare_dataset.py     # Dataset preparation script
@@ -57,24 +58,58 @@ ds_task_recycling_project/
 ├── setup.py               # Automated setup script
 ├── requirements.txt       # Python dependencies
 ├── dataset.yaml          # YOLO dataset configuration
+├── .gitignore            # Git ignore file for ML projects
+├── VALIDATION_CHECKLIST.md # Project validation checklist
 ├── templates/             # Frontend templates
 │   └── index.html        # QA testing web interface
 ├── static/               # Frontend assets
 │   ├── style.css         # Styling for web interface
 │   └── script.js         # JavaScript for web interface
+├── venv/                 # Virtual environment (created by user)
 ├── training/             # Dataset directory
-│   ├── memory/          # Images with memory modules + labels
+│   ├── memory/          # Images with memory modules + YOLO labels
+│   │   ├── out1.png     # Sample motherboard image with memory
+│   │   ├── out1.txt     # YOLO format annotation file
+│   │   └── ...          # 19 more image/label pairs
 │   ├── no_memory/       # Images without memory modules
-│   ├── train/           # Training split (80%)
-│   └── val/             # Validation split (20%)
+│   │   ├── out21.png    # Sample motherboard image without memory
+│   │   └── ...          # 19 more images (no labels needed)
+│   ├── train/           # Training split (80% = 32 images)
+│   │   ├── images/      # Training images
+│   │   └── labels/      # Training labels
+│   └── val/             # Validation split (20% = 8 images)
+│       ├── images/      # Validation images
+│       └── labels/      # Validation labels
+├── uploads/              # Temporary upload directory (created at runtime)
 └── runs/                # Training outputs (created after training)
    └── detect/
        └── memory_module_detection/
-            └── weights/
-                ├── best.pt    # Best model weights
-                └── last.pt    # Last epoch weights
+            ├── weights/
+            │   ├── best.pt    # Best model weights
+            │   └── last.pt    # Last epoch weights
+            ├── train_batch*.jpg # Training visualization
+            ├── val_batch*.jpg   # Validation visualization
+            ├── confusion_matrix.png # Model performance metrics
+            ├── results.png     # Training curves
+            └── args.yaml      # Training arguments
 ```

+### **📁 Key Files Description**
+
+| File/Directory | Purpose | Usage |
+|----------------|---------|-------|
+| `main.py` | Main Flask API application | `python3 main.py` |
+| `api_docs.py` | Swagger UI documentation (developer only) | `python3 api_docs.py` |
+| `train.py` | YOLOv8 model training | `python3 train.py` |
+| `inference_utils.py` | Detection utilities and classes | Imported by other scripts |
+| `test_api.py` | Comprehensive API testing | `python3 test_api.py` |
+| `setup.py` | Automated project setup | `python3 setup.py` |
+| `templates/index.html` | Web interface for QA testing | Served by Flask |
+| `static/` | CSS, JavaScript, and assets | Served by Flask |
+| `training/` | Complete dataset with annotations | Used by training script |
+| `runs/` | Model training outputs | Created after training |
+| `venv/` | Python virtual environment | Created by user |
+
 ## 🤖 Algorithm Choice & Technical Decisions

 ### 1. **Algorithm Choice: YOLOv8 Nano**
@@ -1,245 +0,0 @@
-#!/usr/bin/env python3
-"""
-Swagger UI API Documentation for Memory Module Detection
-This creates a separate API documentation interface using Flask-RESTX
-"""
-
-from flask import Flask, request, jsonify
-from flask_restx import Api, Resource, fields, reqparse
-from flask_cors import CORS
-from werkzeug.datastructures import FileStorage
-import os
-from inference_utils import MemoryModuleDetector
-
-# Initialize Flask app with Swagger
-app = Flask(__name__)
-CORS(app)
-
-# Configure Swagger UI
-api = Api(
-    app,
-    version='1.0.0',
-    title='Memory Module Detection API',
-    description='AI-powered memory module detection in motherboard images using YOLOv8',
-    doc='/docs/',  # Swagger UI will be available at /docs/
-    prefix='/api/v1'
-)
-
-# Create namespaces
-ns_health = api.namespace('health', description='System health and status')
-ns_detect = api.namespace('detect', description='Memory module detection operations')
-
-# Initialize detector
-MODEL_PATH = 'runs/detect/memory_module_detection/weights/best.pt'
-detector = MemoryModuleDetector(MODEL_PATH)
-
-# Define models for Swagger documentation
-detection_model = api.model('Detection', {
-    'bbox': fields.List(fields.Float, description='Bounding box coordinates [x1, y1, x2, y2]'),
-    'confidence': fields.Float(description='Detection confidence score (0.0-1.0)'),
-    'class': fields.Integer(description='Class ID (0 for memory_module)'),
-    'class_name': fields.String(description='Class name (memory_module)')
-})
-
-detection_response = api.model('DetectionResponse', {
-    'success': fields.Boolean(description='Whether detection was successful'),
-    'detections': fields.List(fields.Nested(detection_model), description='List of detected memory modules'),
-    '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'),
-    'original_filename': fields.String(description='Original filename (for uploads)')
-})
-
-health_response = api.model('HealthResponse', {
-    'status': fields.String(description='System health status'),
-    'model_loaded': fields.Boolean(description='Whether the AI model is loaded'),
-    'model_path': fields.String(description='Path to the AI model file')
-})
-
-error_response = api.model('ErrorResponse', {
-    'success': fields.Boolean(description='Always false for errors'),
-    'error': fields.String(description='Error message')
-})
-
-# Health endpoint
-@ns_health.route('/')
-class Health(Resource):
-    @ns_health.doc('health_check')
-    @ns_health.marshal_with(health_response)
-    def get(self):
-        """Check system health and model status"""
-        return {
-            'status': 'healthy',
-            'model_loaded': detector.model is not None,
-            'model_path': MODEL_PATH
-        }
-
-# File upload parser
-upload_parser = reqparse.RequestParser()
-upload_parser.add_argument('image', location='files', type=FileStorage, required=True, 
-                          help='Motherboard image file (PNG, JPG, JPEG, GIF, BMP)')
-upload_parser.add_argument('confidence', type=float, default=0.8, 
-                          help='Confidence threshold (0.1-1.0, default: 0.8)')
-
-@ns_detect.route('/upload')
-class DetectUpload(Resource):
-    @ns_detect.doc('detect_upload')
-    @ns_detect.expect(upload_parser)
-    @ns_detect.marshal_with(detection_response, code=200)
-    @ns_detect.marshal_with(error_response, code=400)
-    @ns_detect.marshal_with(error_response, code=500)
-    def post(self):
-        """Upload and analyze motherboard image for memory modules"""
-        try:
-            if detector.model is None:
-                return {'success': False, 'error': 'Model not loaded'}, 500
-            
-            args = upload_parser.parse_args()
-            file = args['image']
-            confidence = args['confidence']
-            
-            if not file:
-                return {'success': False, 'error': 'No image file provided'}, 400
-            
-            # Save file temporarily
-            temp_path = f"temp_{file.filename}"
-            file.save(temp_path)
-            
-            try:
-                # Run detection
-                detections, annotated_image = detector.detect(temp_path, conf_threshold=confidence)
-                
-                # Convert annotated image to base64
-                import io
-                import base64
-                buffer = io.BytesIO()
-                annotated_image.save(buffer, format='PNG')
-                annotated_base64 = base64.b64encode(buffer.getvalue()).decode()
-                
-                return {
-                    'success': True,
-                    'detections': detections,
-                    'num_detections': len(detections),
-                    'annotated_image': annotated_base64,
-                    'confidence_threshold': confidence,
-                    'original_filename': file.filename
-                }
-            
-            finally:
-                # Clean up
-                if os.path.exists(temp_path):
-                    os.remove(temp_path)
-        
-        except Exception as e:
-            return {'success': False, 'error': str(e)}, 500
-
-# Hardcoded image parser
-hardcoded_parser = reqparse.RequestParser()
-hardcoded_parser.add_argument('confidence', type=float, default=0.8, location='args',
-                             help='Confidence threshold (0.1-1.0, default: 0.8)')
-
-@ns_detect.route('/hardcoded')
-class DetectHardcoded(Resource):
-    @ns_detect.doc('detect_hardcoded')
-    @ns_detect.expect(hardcoded_parser)
-    @ns_detect.marshal_with(detection_response, code=200)
-    @ns_detect.marshal_with(error_response, code=404)
-    @ns_detect.marshal_with(error_response, code=500)
-    def get(self):
-        """Analyze predefined test image for memory modules"""
-        try:
-            if detector.model is None:
-                return {'success': False, 'error': 'Model not loaded'}, 500
-            
-            args = hardcoded_parser.parse_args()
-            confidence = args['confidence']
-            
-            test_image_path = 'training/memory/out1.png'
-            if not os.path.exists(test_image_path):
-                return {'success': False, 'error': f'Test image not found at {test_image_path}'}, 404
-            
-            # Run detection
-            detections, annotated_image = detector.detect(test_image_path, conf_threshold=confidence)
-            
-            # Convert annotated image to base64
-            import io
-            import base64
-            buffer = io.BytesIO()
-            annotated_image.save(buffer, format='PNG')
-            annotated_base64 = base64.b64encode(buffer.getvalue()).decode()
-            
-            return {
-                'success': True,
-                'detections': detections,
-                'num_detections': len(detections),
-                'annotated_image': annotated_base64,
-                'confidence_threshold': confidence,
-                'test_image_path': test_image_path
-            }
-        
-        except Exception as e:
-            return {'success': False, 'error': str(e)}, 500
-
-# Base64 image model
-base64_model = api.model('Base64Request', {
-    'image': fields.String(required=True, description='Base64 encoded image data'),
-    'confidence': fields.Float(default=0.8, description='Confidence threshold (0.1-1.0)')
-})
-
-@ns_detect.route('/base64')
-class DetectBase64(Resource):
-    @ns_detect.doc('detect_base64')
-    @ns_detect.expect(base64_model)
-    @ns_detect.marshal_with(detection_response, code=200)
-    @ns_detect.marshal_with(error_response, code=400)
-    @ns_detect.marshal_with(error_response, code=500)
-    def post(self):
-        """Analyze base64 encoded image for memory modules"""
-        try:
-            if detector.model is None:
-                return {'success': False, 'error': 'Model not loaded'}, 500
-            
-            data = request.get_json()
-            if not data or 'image' not in data:
-                return {'success': False, 'error': 'No base64 image data provided'}, 400
-            
-            confidence = data.get('confidence', 0.8)
-            
-            # Decode base64 image
-            import base64
-            import io
-            from PIL import Image
-            import numpy as np
-            
-            try:
-                img_data = base64.b64decode(data['image'])
-                image = Image.open(io.BytesIO(img_data))
-            except Exception as e:
-                return {'success': False, 'error': f'Invalid base64 image data: {str(e)}'}, 400
-            
-            # Run detection
-            detections, annotated_image = detector.detect_from_array(np.array(image), conf_threshold=confidence)
-            
-            # Convert annotated image to base64
-            buffer = io.BytesIO()
-            annotated_image.save(buffer, format='PNG')
-            annotated_base64 = base64.b64encode(buffer.getvalue()).decode()
-            
-            return {
-                'success': True,
-                'detections': detections,
-                'num_detections': len(detections),
-                'annotated_image': annotated_base64,
-                'confidence_threshold': confidence
-            }
-        
-        except Exception as e:
-            return {'success': False, 'error': str(e)}, 500
-
-if __name__ == '__main__':
-    print("Starting Memory Module Detection API with Swagger UI...")
-    print(f"Model path: {MODEL_PATH}")
-    print(f"Model loaded: {detector.model is not None}")
-    print("Swagger UI available at: http://localhost:5003/docs/")
-    
-    app.run(host='0.0.0.0', port=5003, debug=True)
@@ -88,63 +88,16 @@ def api_info():
        'endpoints': {
            '/': 'GET - Frontend interface or API information',
            '/api': 'GET - API information (JSON)',
-            '/docs': 'GET - API documentation (Swagger UI)',
            '/detect': 'POST - Upload image for memory module detection',
            '/detect/hardcoded': 'GET - Process hardcoded test image',
            '/detect/base64': 'POST - Process base64 encoded image',
            '/health': 'GET - Health check'
        },
        'model_loaded': detector.model is not None,
-        'supported_formats': list(ALLOWED_EXTENSIONS),
-        'swagger_ui': 'http://localhost:5003/docs/ (run: python3 api_docs.py)'
+        'supported_formats': list(ALLOWED_EXTENSIONS)
    })

-@app.route('/docs')
-def api_docs():
-    """Redirect to API documentation."""
-    return """
-    <!DOCTYPE html>
-    <html>
-    <head>
-        <title>API Documentation</title>
-        <style>
-            body { font-family: Arial, sans-serif; text-align: center; padding: 50px; background: #f5f5f5; }
-            .container { max-width: 600px; margin: 0 auto; background: white; padding: 40px; border-radius: 10px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); }
-            .btn { display: inline-block; padding: 12px 24px; background: #007bff; color: white; text-decoration: none; border-radius: 5px; margin: 10px; }
-            .btn:hover { background: #0056b3; }
-            .code { background: #f8f9fa; padding: 10px; border-radius: 5px; margin: 20px 0; font-family: monospace; }
-        </style>
-    </head>
-    <body>
-        <div class="container">
-            <h1>🚀 Memory Module Detection API Documentation</h1>
-            <p>Interactive Swagger UI documentation for all API endpoints</p>

-            <h3>📖 Access Swagger UI:</h3>
-            <div class="code">
-                <strong>Step 1:</strong> Start API docs server<br>
-                <code>python3 api_docs.py</code><br><br>
-                <strong>Step 2:</strong> Open Swagger UI<br>
-                <code>http://localhost:5003/docs/</code>
-            </div>
-
-            <a href="http://localhost:5003/docs/" class="btn" target="_blank">
-                📚 Open Swagger UI (if running)
-            </a>
-
-            <h3>📋 Quick API Reference:</h3>
-            <ul style="text-align: left;">
-                <li><strong>POST /detect</strong> - Upload image for detection</li>
-                <li><strong>GET /detect/hardcoded</strong> - Test with predefined image</li>
-                <li><strong>POST /detect/base64</strong> - Process base64 encoded image</li>
-                <li><strong>GET /health</strong> - System health check</li>
-            </ul>
-
-            <p><a href="/">← Back to Main Interface</a></p>
-        </div>
-    </body>
-    </html>
-    """

@app.route('/health', methods=['GET'])
 def health_check():
@@ -39,9 +39,6 @@
                    <button class="btn btn-info" onclick="runAllTests()">
                        <i class="fas fa-play"></i> Run All Tests
                    </button>
-                    <a href="/docs" class="btn btn-outline" target="_blank">
-                        <i class="fas fa-book"></i> API Documentation
-                    </a>
                </div>
            </section>