semvec/pypi-packages huggingface dataset:
feature-implementer 0.1.8
A tool to help implement features and create specific prompts.
Requires Python: >=3.8
Keywords: llm, feature, prompt, implementation, ai, context, cli, web, feature-implement, template

# Feature Implementer

**Generate high-quality feature prompts for LLMs from your codebase.**  
A pip-installable tool that helps create well-structured prompts for LLMs by gathering context from relevant code files within your project.

## ⚡ Quickstart

```bash
# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install the package
pip install feature-implementer

# Run in your project directory
cd /your/project
feature-implementer
```

Open your browser at http://127.0.0.1:4605 and start generating prompts!

## ✨ Features

- 🌐 **Interactive Web UI** and powerful CLI
- 📁 Smart context gathering from your codebase
- 🎯 Custom prompt templates with local storage
- 🔄 Jira integration for ticket descriptions
- 📝 Export prompts as Markdown files
- 🌙 Light/dark mode support

## 📦 Installation

Install from PyPI:

```bash
pip install feature-implementer
```

Or get the latest development version:

```bash
git clone https://github.com/paulwenner/feature-implementer.git
cd feature-implementer
pip install -e .
```

## 🚀 Basic Usage

### Web UI

1. Start the server: `feature-implementer`
2. Open http://127.0.0.1:4605 in your browser
3. Select relevant code files from your project
4. Add Jira ticket description and instructions
5. Generate and export your prompt!

### CLI

Generate a prompt with context files:

```bash
feature-implementer-cli --context-files src/app.py src/models.py \
                       --jira "FEAT-123: New feature" \
                       --output prompt.md
```

## 📚 Documentation

For detailed information, check out:

- [Complete Usage Guide](docs/usage.md)
- [Template Management](docs/templates.md)
- [CLI Reference](docs/cli.md)
- [Development Guide](docs/development.md)

## 🛠️ Configuration Options

Quick reference for common settings:

```bash
# Custom port
feature-implementer --port 5001

# Custom directories
feature-implementer --working-dir /path/to/project --prompts-dir /path/to/prompts

# Production mode
feature-implementer --prod --workers 4
```

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
Flask-Threads 0.2.0
A helper library to work with threads within Flask applications.

## Flask-Threads
[![Actions Status](https://github.com/sintezcs/flask-threads/workflows/GitHub%20Build/badge.svg)](https://github.com/sintezcs/flask-threads/actions)

A helper library to work with threads within Flask applications.

The main problem that you face trying to spin a background thread or running a 
future in Flask app - is loosing the application context. The most common 
scenario is to try to access `flask.g` object. Application context 
is a thread local so you can not access it from another thread and Flask will 
raise an exception if you would try to. 

This library provides helper classes that allows you accessing the current 
application context from another thread.

### Installation
```bash
$ pip install Flask-Threads
```

### Examples

#### Threads

```python
from flask import g
from flask import request
from flask import Flask
from flaskthreads import AppContextThread

app = Flask('my_app')


@app.route('/user')
def get_user():
    g.user_id = request.headers.get('user-id')
    t = AppContextThread(target=do_some_user_work_in_another_thread)
    t.start()
    t.join()
    return 'ok'


def do_some_user_work_in_another_thread():
    id = g.user_id
    print(id)

```

#### Concurrent futures

```python
from flask import g
from flask import request
from flask import Flask
from flaskthreads import ThreadPoolWithAppContextExecutor

app = Flask('my_app')


@app.route('/user')
def get_user():
    g.user_id = request.headers.get('user-id')
    with ThreadPoolWithAppContextExecutor(max_workers=2) as pool:
        future = pool.submit(do_some_user_work_in_another_thread)
        future.result()
    return 'ok'


def do_some_user_work_in_another_thread():
    id = g.user_id
    print(id)
```
ppxf-sew 0.5.5
A method of stellar population synthesis based on ''Equivalent Widths spectrum'', utilizing pPXF for full-spectrum fitting
Requires Python: >=3.11


**pPXF-SEW: A full-spectrum fitting method of stellar population Synthesis based on ''Equivalent Widths spectrum'' without attenuation curve prior based on pPXF**

Description
===========

This ``pPXF-SEW`` package contains a Python implementation of the stellar 
population Synthesis based on ``Equivalent Widths spectrum`` (``SEW``) 
method and the Penalized PiXel-Fitting (``pPXF``) method. It uses 
full-spectrum fitting with SED to extract stellar and gas kinematics, 
the stellar population, as well as the dust attenuation curve of stars
and galaxies, without a prior attenuation curve. The kinematics are derived
from the ``pPXF`` method, while the stellar population and attenuation curve
are derived from the ``SEW`` method.

The ``SEW`` method was described in Lu (2025) (in prep.).

The ``pPXF`` method was originally described in `Cappellari & Emsellem (2004)
<https://ui.adsabs.harvard.edu/abs/2004PASP..116..138C>`_
and was substantially upgraded in subsequent years, particularly in
`Cappellari (2017) <https://ui.adsabs.harvard.edu/abs/2017MNRAS.466..798C>`_.

Installation
------------

Install with::

    pip install ppxf-sew

Without write access to the global ``site-packages`` directory, use::

    pip install --user ppxf-sew

To upgrade to the latest version, use::

    pip install --upgrade ppxf-sew

Usage Examples
--------------

To learn how to use the ``pPXF-SEW`` package, run the examples as 
Jupyter Notebooks in the ``ppxf-sew/example`` directory. 
It can be found within the main ``pPXF-SEW`` package installation folder 
inside `site-packages <https://stackoverflow.com/a/46071447>`_.

###########################################################################

License
-------

Other/Proprietary License

Copyright (c) 2024 Jiafeng Lu

This software is provided as is with no warranty. You may use it for
non-commercial purposes and modify it for personal or internal use, as long
as you include this copyright and disclaimer in all copies. You may not
redistribute the code.