cloud-computing

CrashSnap API

CrashSnap API is a service designed to predict vehicle damage repair costs from uploaded images. This API is built using Node.js and Express, and uses Multer to handle image uploads. The API integrates with a machine learning model to provide accurate repair cost predictions based on the images of the damage.

Features

• Upload vehicle damage images and get repair cost predictions.
• View prediction history.
• View prediction details by ID.
• Delete predictions by ID.
• Integrated with a machine learning model for accurate predictions.

Installation

1. Clone this repository

   git clone https://github.com/username/crashsnap-api.git
   cd crashsnap-api

2. Install dependencies

   Make sure you have Node.js installed on your machine. Then run the following command:

   npm install

3. Configuration

   Create a .env file in the root directory of your project and add the necessary configuration (example):

   PORT=3000

4. Run the application

   Start the application with:

   npm start

   For development, you can use nodemon:

   npm run dev

Usage

This API provides several endpoints for various operations. Here is a list of available endpoints:

1. Predict Damage Repair Cost

• Endpoint: /cost

• Method: POST

• Description: Upload vehicle damage images and get repair cost predictions. This endpoint uses a machine learning model to analyze the images and provide an estimated repair cost.

• Parameters:

  • image: Array of images (form-data)

• Request Example:

  POST /cost
  Content-Type: multipart/form-data
  
  image: [file1.jpg, file2.jpg]

• Response Example:

  {
    "id": "prediction_id",
    "cost": 5000,
    "message": "Prediction successful"
  }

2. View Prediction History

• Endpoint: /

• Method: GET

• Description: Get all prediction history.

• Request Example:

  GET /

• Response Example:

  [
    {
      "id": "prediction_id_1",
      "cost": 5000,
      "date": "2023-06-21"
    },
    {
      "id": "prediction_id_2",
      "cost": 3000,
      "date": "2023-06-22"
    }
  ]

3. View Prediction Details

• Endpoint: /:id/detail

• Method: GET

• Description: Get prediction details by ID.

• Parameters:

  • id: Prediction ID

• Request Example:

  GET /12345/detail

• Response Example:

  {
    "id": "12345",
    "cost": 5000,
    "date": "2023-06-21",
    "details": "Detailed information about the prediction"
  }

4. Delete Prediction

• Endpoint: /:id/delete

• Method: DELETE

• Description: Delete prediction by ID.

• Parameters:

  • id: Prediction ID

• Request Example:

  DELETE /12345/delete

• Response Example:

  {
    "message": "Prediction deleted successfully"
  }

Contribution

If you want to contribute to this project, please create a pull request or open a new issue in this repository. Your contributions are greatly appreciated!

Authors

• Ihsansyafiul
• Faturihsan

License

This project is licensed under the MIT License.