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# Python Code Analyzer | |
## Motivation | |
The main purpose of the app is to allow Python developers navigate Python code base much easier by showing dependencies | |
among files included in the directory with better visualization. | |
## Setup | |
- Python version: 3.8 | |
- `pipenv install` | |
- `cp .env.example .env`: fill in your GitHub username and personal access token if you need to increase [GitHub API requests rate limiting](https://docs.github.com/en/rest/overview/resources-in-the-rest-api#rate-limiting) | |
- `make run` | |
- Also refer to References to understand settings behind the project | |
## Thoughts on solving the problem | |
1. Build a prototype without UI interface to show dependencies among files included in the directory(output) given user's | |
input to one GitHub public repo's URL(input) | |
2. Build a `streamlit` app to show results using some network visualization tools | |
## Used technologies | |
- `networkx`: network analysis | |
- `pyvis`: network visualization | |
- `streamlit`: web app | |
## TODOs | |
- [x] Build a prototype | |
- [x] Finish `generate_imports_graph` implementation | |
- [x] Fetch python files given public GitHub repo url(owner, repo, path, ref) | |
- [x] Use `ast` to parse imports among given python files | |
- [x] Generate a basic `networkx` graph given python imports | |
- [x] Visualize a basic `networkx` graph using `pyvis` | |
- [x] Build a `streamlit` app | |
## References | |
- [How to set up a perfect Python project](https://sourcery.ai/blog/python-best-practices/) | |