https://github.com/vivekcode101/mlimagepipeline
https://github.com/vivekcode101/mlimagepipeline
Last synced: 3 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/vivekcode101/mlimagepipeline
- Owner: vivekcode101
- License: apache-2.0
- Created: 2024-05-20T09:16:21.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2024-05-21T18:06:48.000Z (about 2 years ago)
- Last Synced: 2025-01-18T14:32:57.283Z (over 1 year ago)
- Language: JavaScript
- Size: 475 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AI Model Project
This project contains an AI model implemented in Python. It provides a simple API with two endpoints: one for returning a "Hello World" message and another for processing an image and returning information about it.
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [API Endpoints](#api-endpoints)
- [Docker](#docker)
- [Contributing](#contributing)
- [License](#license)
## Installation
To run this project, you need to have Docker installed on your machine. Follow these steps to set up and run the project:
For backend
1. Clone the repository:
```sh
git clone https://github.com/vivekcode101/mlimagepipeline
cd mlimagepipeline/backend
```
2. Building the Docker Image
```sh
docker build -t ai-model -f model.dockerfile .
```
3. Procfile
Procfile is also provided to assign web commands.
## Usage
Once the Docker container is running, the API will be available at `http://localhost:8000`.
## API Endpoints
### GET /
- **Description**: Returns a "Hello World" message.
- **URL**: `/`
- **Method**: `GET`
- **Response**:
```json
{
"message": "Hello World"
}
```
### POST /process
- **Description**: Processes an image and returns information about it.
- **URL**: `/process`
- **Method**: `POST`
- **Parameters**:
- `text` (query parameter): A string parameter required in the query.
- `image` (form-data multipart): An image file uploaded as form-data.
- **Response**: JSON object containing information about the image.
- **Example cURL request**:
```sh
curl -X POST http://localhost:8000/process \
-F "text=your_query_string" \
-F "image=@path_to_your_image_file"
```
## Docker Running the Docker Container
```sh
docker run -it -p 8000:8000 ai-model
```
For frontend
1.You should have npm installed in your local machine
2.Run npm install in the vqa-frontend folder to install all the dependencies
```sh
npm i
```
3. Run the frontend
```sh
npm start
```
Contributing
Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.
License
This project is licensed under the MIT License. See the LICENSE file for details.
This project includes a `Dockerfile` to simplify the setup and deployment process. Using Docker ensures a consistent environment for running the project.