# Memory Module Detection API Documentation

## Overview
Flask API for detecting memory modules on motherboard images using YOLOv8. Processes uploaded images and returns bounding box coordinates with confidence scores.

## Base URL
`http://localhost:5000`

## Endpoints

### 1. Root Endpoint
**GET** `/`  
- Returns the test interface HTML page  
- Response: `test.html`

### 2. Image Detection
**POST** `/detect`  
- Accepts image uploads for processing  
- **Request:**  
  ```bash
  curl -X POST -F "image=@motherboard.jpg" http://localhost:5000/detect
  ```  
- **Successful Response (200):**  
  ```json
  {
    "detections": [
      {
        "box": [x1,y1,x2,y2],
        "confidence": 0.95,
        "class": 0
      }
    ],
    "result_image": "/results/filename.jpg"
  }
  ```  
- **Error Responses:**  
  - `400 Bad Request`: Missing/invalid image file  
  - `500 Server Error`: Processing failure  

### 3. Result Retrieval
**GET** `/results/<filename>`  
- Returns annotated image with bounding boxes  
- Example: `http://localhost:5000/results/out1.jpg`  

## Request/Response Examples
**Sample Request:**  
```python
import requests
response = requests.post(
    'http://localhost:5000/detect',
    files={'image': open('motherboard.jpg', 'rb')}
)
```

**Sample Response:**  
```json
{
  "detections": [
    {
      "box": [541,567,661,265],
      "confidence": 0.98,
      "class": 0
    }
  ],
  "result_image": "/results/out1.jpg"
}
```

## Technical Specifications
| Parameter          | Value                     |
|--------------------|---------------------------|
| Model              | YOLOv8n (custom-trained)  |
| Input Formats      | JPG/PNG                   |
| Recommended Resolution | 416px                 |
| Processing Time (CPU) | 200-500ms per image    |