- 📖 Table of Contents
- 📍 Overview
- 📦 Features
- 📂 repository Structure
- ⚙️ Modules
- 🚀 Getting Started
- 🛣 Roadmap
- 🤝 Contributing
- 📄 License
- 👏 Acknowledgments
Leveraging machine learning capabilities. The file images to be used for the machine learning operation. Input a batik images, then get recommendation for similar batik.
- Machine Learning Feature: Provide an endpoint for machine learning analysis of batik images, enhancing the platform's capabilities.
For more detail API Documentation, Documentation.
https://documenter.getpostman.com/view/25932120/2s9YkkfNgo#928f8e75-8575-4d6a-b4d7-9809e002a748
https://ml-baskarya-veuznuhx2a-et.a.run.app/
Endpoint.
└── flask-ml-model/
├── Dockerfile
├── app.py
├── credentials/
│ └── serviceAccountKey.json
├── image_features.joblib
├── image_paths.joblib
├── requirements.txt
POST http://{{endpoint}}/api/ml
- Method: POST
- URL:
http://{{endpoint}}/api/ml - Body:
file(Form Data): Image file for machine learning analysis.
Root
| File | Summary |
|---|---|
| Dockerfile | HTTPStatus Exception: 404 |
| requirements.txt | HTTPStatus Exception: 404 |
| app.py | HTTPStatus Exception: 404 |
Credentials
| File | Summary |
|---|---|
| serviceAccountKey.json | HTTPStatus Exception: 404 |
Dependencies
Please ensure you have the following dependencies installed on your system:
Below are the dependencies used in this project, each serving a specific purpose:
-
firebase-admin (
==6.3.0): Firebase Admin SDK for Python, providing the necessary tools for managing Firebase services. -
Flask (
==3.0.0): Lightweight web application framework for Python. -
joblib (
==1.3.2): Library for parallelizing tasks in Python, often used for parallel processing and caching. -
keras (
==2.15.0): High-level neural networks API in Python, working on top of TensorFlow and Theano. -
numpy (
==1.26.2): Fundamental package for scientific computing with Python. -
scikit-learn (
==1.3.2): Machine learning library for Python, providing simple and efficient tools for data analysis and modeling. -
tensorflow (
==2.15.0): Open-source machine learning framework developed by the TensorFlow team. -
asyncio (
==3.4.3): Library for writing single-threaded concurrent code using coroutines, multiplexing I/O access over sockets and other resources. -
gevent (
==23.9.1): Python coroutine-based concurrency library, useful for handling many concurrent connections.
Please refer to requirements.txt for more details on versioning and additional information about the project's dependencies.
- Clone the flask-ml-model repository:
git clone https://github.com/Baskarya/flask-ml-model- Change to the project directory:
cd flask-ml-model- Install the dependencies:
pip install -r requirements.txt
ℹ️ Task 1: Processing Model from Uploaded File
Contributions are welcome! Here are several ways you can contribute:
- Submit Pull Requests: Review open PRs, and submit your own PRs.
- Join the Discussions: Share your insights, provide feedback, or ask questions.
- Report Issues: Submit bugs found or log feature requests for ROUTINEE66.
Click to expand
- Fork the Repository: Start by forking the project repository to your GitHub account.
- Clone Locally: Clone the forked repository to your local machine using a Git client.
git clone <your-forked-repo-url>
- Create a New Branch: Always work on a new branch, giving it a descriptive name.
git checkout -b new-feature-x
- Make Your Changes: Develop and test your changes locally.
- Commit Your Changes: Commit with a clear and concise message describing your updates.
git commit -m 'Implemented new feature x.' - Push to GitHub: Push the changes to your forked repository.
git push origin new-feature-x
- Submit a Pull Request: Create a PR against the original project repository. Clearly describe the changes and their motivations.
Once your PR is reviewed and approved, it will be merged into the main branch.
This project is protected under the SELECT-A-LICENSE License. For more details, refer to the LICENSE file.
- List any resources, contributors, inspiration, etc. here.