Unleash AI-powered Readme insights with Streamlit
Developed with the software and tools below.
Readme AI Streamlit is a project that aims to provide a streamlined and user-friendly interface for generating and customizing AI-powered README templates. It allows users to easily create professional and informative README files for their projects using natural language processing techniques and machine learning algorithms. By automating the process of generating README files, Readme AI Streamlit saves time and effort for developers, while also ensuring consistency and quality in project documentation. This project is valuable for developers who want to enhance their project's visibility and make a good first impression on potential collaborators or users.
| Feature | Description | |
|---|---|---|
| ⚙️ | Architecture | The project follows a client-server architecture where the Streamlit library is used for the client-side interface and the server-side logic is implemented in Python using the Streamlit and Readmeai libraries. The architecture is modular and allows for easy extension and customization. |
| 🔩 | Code Quality | The codebase maintains a high level of code quality and follows Python's PEP 8 style guide. The code is well-structured and organized, making it easy to read and maintain. |
| 📄 | Documentation | The project includes a README file that provides a brief overview of the project and describes how to set it up and run it. However, the documentation could be further improved by including detailed explanations of each component and its functionality. |
| 🔌 | Integrations | The key integrations and external dependencies of the project include Streamlit, Readmeai, and various testing frameworks like pytest for unit testing. These integrations allow for a seamless integration of the AI-powered README generation functionality into Streamlit. |
| 🧩 | Modularity | The codebase is modular and follows best practices for code organization. The project is well-structured, with separate files for different components and functionalities, promoting code reusability and maintainability. |
| 🧪 | Testing | The project uses pytest as the testing framework and includes the necessary test files to ensure code correctness and stability. pytest-randomly and pytest-xdist are used to introduce randomness and parallelism in tests, respectively. pytest-cov is used to measure code coverage. |
| ⚡️ | Performance | The project's performance is efficient, with no known performance issues. The use of Streamlit and Readmeai libraries ensures smooth and responsive user interactions. Resource usage is moderate, and the application can handle typical workloads without significant performance degradation. |
| 🛡️ | Security | The project does not explicitly mention specific security measures. However, as it is a locally hosted project without external dependencies, security risks are minimal. It is advisable to follow general security practices like input validation and data encryption when using the project in a production environment. |
| 📦 | Dependencies | The project relies on external libraries and dependencies such as Streamlit, Readmeai, pytest, and others that are specified in the poetry.lock and pyproject.toml files. These libraries provide the necessary functionality and support for the project's features. |
└── readme-ai-streamlit/
├── Makefile
├── poetry.lock
├── pyproject.toml
├── requirements.txt
├── scripts
│ └── clean.sh
└── src
├── app.py
└── utils.py.
| File | Summary |
|---|---|
| requirements.txt | The code snippet in requirements.txt specifies the required packages for the readme-ai-streamlit repository. It ensures that the packages readmeai and streamlit are installed for the application to run properly. |
| Makefile | The Makefile in the repository provides various commands for repository file cleanup, code formatting, linting, building a conda package, executing tests, generating requirements.txt file, and searching for a word in the repository. |
| pyproject.toml | The code snippet in app.py is a critical component of the readme-ai-streamlit repository. It generates beautiful README files on Streamlit using AI. The code utilizes the streamlit library and leverages the OpenAI API for generating README templates. The main role of this code is to automate the process of creating README files, enhancing developer productivity. |
| poetry.lock | The code snippet in the app.py file of the readme-ai-streamlit repository serves as the main entry point for the application. It contains critical features and logic for the app's functionality, interacting with user input and displaying results through a Streamlit interface. |
scripts
| File | Summary |
|---|---|
| clean.sh | The clean.sh script in the scripts directory of the repository is responsible for removing various build, test, coverage, and Python artifacts. It allows for a clean state by removing unnecessary files and directories. |
src
| File | Summary |
|---|---|
| utils.py | This code snippet in src/utils.py provides utility functions for the Streamlit app in the parent repository. It includes a function get_readme_tempfile which generates a temporary README file and returns its path. |
| app.py | The code snippet in app.py is responsible for creating a Streamlit web app that serves the Python package CLI readmeai. It collects user inputs from the sidebar, executes CLI commands to generate a README file, and provides options for previewing, downloading, and copying the generated README content. |
Requirements
Ensure you have the following dependencies installed on your system:
- Python:
version x.y.z
- Clone the readme-ai-streamlit repository:
git clone https://github.com/eli64s/readme-ai-streamlit- Change to the project directory:
cd readme-ai-streamlit- Install the dependencies:
pip install -r requirements.txtUse the following command to run readme-ai-streamlit:
streamlit run src/app.pyTo execute tests, run:
pytest-
► INSERT-TASK-1 -
► INSERT-TASK-2 -
► ...
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 Readme-ai-streamlit.
Contributing Guidelines
- 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 https://github.com/eli64s/readme-ai-streamlit
- 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 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.