BASKARYA FLASK ML MODEL

◦ Developed with the software and tools below.

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📖 Table of Contents

• 📖 Table of Contents
• 📍 Overview
• 📦 Features
• 📂 repository Structure
• ⚙️ Modules
• 🚀 Getting Started
  • 🔧 Installation
  • 🤖 Running flask-ml-model
  • 🧪 Tests
• 🛣 Roadmap
• 🤝 Contributing
• 📄 License
• 👏 Acknowledgments

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📍 Overview

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.

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📦 Features

• Machine Learning Feature: Provide an endpoint for machine learning analysis of batik images, enhancing the platform's capabilities.

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Documentation

For more detail API Documentation, Documentation.

https://documenter.getpostman.com/view/25932120/2s9YkkfNgo#928f8e75-8575-4d6a-b4d7-9809e002a748

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API Service Endpoint

https://ml-baskarya-veuznuhx2a-et.a.run.app/ Endpoint.

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📂 Repository Structure

└── flask-ml-model/
    ├── Dockerfile
    ├── app.py
    ├── credentials/
    │   └── serviceAccountKey.json
    ├── image_features.joblib
    ├── image_paths.joblib
    ├── requirements.txt

Machine Learning Feature

Endpoint

POST http://{{endpoint}}/api/ml

Request

• Method: POST
• URL: http://{{endpoint}}/api/ml
• Body:
  • file (Form Data): Image file for machine learning analysis.

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⚙️ Modules

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

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🚀 Getting Started

Dependencies

Please ensure you have the following dependencies installed on your system:

Dependencies

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.

🔧 Installation

1. Clone the flask-ml-model repository:

git clone https://github.com/Baskarya/flask-ml-model

2. Change to the project directory:

cd flask-ml-model

3. Install the dependencies:

pip install -r requirements.txt

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🛣 Project Roadmap

• ℹ️ Task 1: Processing Model from Uploaded File

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🤝 Contributing

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.

Contributing Guidelines

Click to expand

1. Fork the Repository: Start by forking the project repository to your GitHub account.
2. Clone Locally: Clone the forked repository to your local machine using a Git client.

   git clone <your-forked-repo-url>

3. Create a New Branch: Always work on a new branch, giving it a descriptive name.

   git checkout -b new-feature-x

4. Make Your Changes: Develop and test your changes locally.
5. Commit Your Changes: Commit with a clear and concise message describing your updates.

   git commit -m 'Implemented new feature x.'

6. Push to GitHub: Push the changes to your forked repository.

   git push origin new-feature-x

7. 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.

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📄 License

This project is protected under the SELECT-A-LICENSE License. For more details, refer to the LICENSE file.

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👏 Acknowledgments

• List any resources, contributors, inspiration, etc. here.

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