semvec/pypi-packages huggingface dataset:
quick-flask 1.1.2
CLI tool to quickly generate Flask apps
Requires Python: >=3.7
Keywords: flask, web development, cli tool

# QuickFlask

**QuickFlask** is a CLI tool designed to help you create modular, functional Flask applications in seconds. I love Flask, but I often found myself writing the same boilerplate code repeatedly. To save time, I created QuickFlask. This tool will save you time and effort, allowing you to focus on building your application rather than setting up the initial structure. It adheres to the DRY (Don’t Repeat Yourself) principle.

## Installation

To install QuickFlask, run:

```sh
pip3 install quick-flask
```

```sh
pip3 install quick-flask --upgrade
```

## Usage

Once installed, you can create a new Flask project by running:

```sh
quickflask
```

![Usage](ss.jpeg)
![Usage](ss2.jpeg)

### Options:

- `--name` (Required): The name of your Flask application.

Example:

```sh
quickflask --name my_flask_app
```

## Running Your Flask App

After creating your project, navigate into the directory:

```sh
cd my_flask_app
```

Set up a virtual environment (recommended):

```sh
python3 -m venv venv
source venv/bin/activate  # On Windows: .\venv\Scripts\activate
```

Install dependencies:

```sh
pip install -r requirements.txt
```

Run the Flask app:

```sh
python app.py
```

Your application will be accessible at `http://127.0.0.1:5000/`.

## Features

- Generates a structured Flask project with blueprints and templates.
- Automatically sets up templates and API routes.
- Saves time by eliminating repetitive boilerplate code.

## Contributing

Feel free to open issues or submit pull requests to improve QuickFlask. Contributions are always welcome!

## License

This project is licensed under the MIT License. See `LICENSE` for details.

## Change Log

For a detailed list of changes and version history, please see the [CHANGELOG.md](CHANGELOG.md) file.
gcloud-cache 0.1.7
A Python package for caching data and files in Google Cloud Storage.
Requires Python: >=3.6

# Cloud Cache

Cloud Cache is a Python package for caching data and files in Google Cloud Storage.

## Features

- Store and retrieve cached results from Google Cloud Storage
- Supports JSON and file-based caching
- Easy-to-use decorator for function caching
- Ensures the Cloud Storage bucket exists before usage

## Installation

```sh
pip install gcloud_cache
```

Installing locally:
```sh
python setup.py clean --all
python setup.py sdist bdist_wheel
twine upload dist/*
```
## Usage

### Basic Caching Example

```python
from cloud_cache import cache_result

@cache_result
def expensive_function(param):
    return {"result": f"Processed {param}"}

print(expensive_function("test"))  # First call - runs the function
print(expensive_function("test"))  # Second call - retrieves from cache
```

## Configuration

Cloud Cache requires a configuration file `cloud_storage.yaml`, which should be placed in the `local/` directory.

### Example `local/cloud_storage.yaml`:

```yaml
bucket_name: your-cache-bucket
credentials_path: local/pdf-converter-449809-b8ce9144eafa.json
```

- `bucket_name` - The name of the Google Cloud Storage bucket.
- `credentials_path` - The path to the service account JSON file for authentication.

Ensure that `local/cloud_storage.yaml` is **not** committed to your repository by adding `local/cloud_storage.yaml` to your `.gitignore` file.

## License

MIT License
canvasrobot 0.9.1
Library which uses Canvasapi (see https://canvasapi.readthedocs.io) to provide a CanvasRobot class.
Requires Python: <4.0.0,>=3.9

# CanvasRobot
Library which uses
[Canvasapi](https://canvasapi.readthedocs.io/en/stable/getting-started.html)
(minimally patched to allow more flexible user search)
to provide a CanvasRobot class for GUI and commandline use.
You can install canvasrobot and urltransform (special edition 
to translate video urls in Canvas pages) 
using [UV](https://docs.astral.sh/uv/getting-started/installation/)
Tip: use `brew` to install on macOS and `winget` on Windows. Check if the 
path is correct, after reopening terminal/Powershell

``` commandline
uv tool install canvasrobot

canvasrobot --help
urltransform --help
```

## Uses 
- [CanvasAPI](https://canvasapi.readthedocs.io/en/stable/getting-started.html) 
- rich
- rich_click
- keyring (safe store for API key)
- pydal (local database)
- pywebview (HTML reporting)
- [opt] pymemcache (install to add caching canvas interaction)

## Used 
In word2quiz library.
(Not yet ready for general use...)

## Examples
```Python
import rich
import canvasrobot as cr

if __name__ == '__main__':

    console = rich.console.Console(width=160, force_terminal=True)

    robot = cr.CanvasRobot(reset_api_keys=False,
                           console=console)

    # robot.update_database_from_canvas()
    robot.create_folder_in_all_courses('Tentamens')



    # result = robot.enroll_in_course("", 4472, 'u752058',
    # 'StudentEnrollment') #  (enrollment={'type': 'StudentEnrollment'}
    
    # user = robot.search_user('u752058')
    # print(user)
    # if not user:
    #   print(robot.errors)

    #COURSE_ID = 9999  # test course
    #result = robot.create_folder_in_course_files(COURSE_ID, 'Tentamens')

    # print(robot.course_metada(COURSE_ID))
    # print(robot.unpublish_folderitems_in_course(COURSE_ID,
    #                                            foldername,
    #                                            files_too=True))

    #course = robot.get_course(COURSE_ID)
    # tab = robot.get_course_tab_by_label(COURSE_ID, "Files")
    # print(tab.visibility)

    # for course_id in (COURSE_ID, COURSE_ID2):
    #     result = robot.create_folder_in_course_files(course_id, 'Tentamens')

    # result = robot.unpublish_subfolder_in_all_courses(foldername,
    #                                                  files_too=True,
    #                                                  check_only=True)
    # if course_ids_missing_folder:
    #    logging.info(f"Courses with missing folder {foldername}: {course_ids_missing_folder}")


    # QUIZZES -----------------------------

    # filename = 'Q_A.docx'
    # robot.create_quizzes_from_document(filename=filename,
    #                                    course_id=COURSE_ID,
    #                                    question_format='Vraag {}. Vertaal:',
    #                                    adjust_fontsize=True,
    #                                    testrun=False
    #                                    )
```


[![PyPI version](https://badge.fury.io/py/canvasrobot.svg)]
SimpleKeyMappingMacro 1.0.1
Simple key mapping macro with GUI
Requires Python: >=3.7

# Simple Key Mapping Macro

## 📌 소개
Simple Key Mapping Macro는 사용자가 키 매핑을 직접 설정하고 특정 키 입력 시 미리 정의된 키 시퀀스를 자동 실행할 수 있도록 도와주는 Python 기반 매크로 프로그램입니다.

---

## 🛠 주요 기능
- **GUI 기반 매크로 관리**: 사용자가 쉽게 키 매핑을 추가 및 삭제 가능
- **트리거 키 설정**: F1~F12 및 숫자키 등 특정 키 입력을 트리거로 사용
- **키 시퀀스 실행**: 특정 키를 누르면 미리 설정한 키 입력 자동 실행
- **입력 간격 설정**: 기본 및 개별 키 입력 간격 조절 가능
- **리스트 관리 기능**: 설정한 매핑을 GUI에서 확인 가능

---

## 📥 설치 방법
Python 3.7 이상이 필요합니다.

### **1️⃣ 패키지 설치**
PyPI에서 패키지를 다운로드하여 설치할 수 있습니다:
```sh
pip install SimpleKeyMappingMacro
```

### **2️⃣ 실행 방법**
설치 후, 다음 명령어를 입력하면 GUI가 실행됩니다:
```sh
simplekeymacro
```

---

## 🚀 사용 방법
1. 프로그램 실행 후 **"키 매핑 추가"** 버튼을 클릭합니다.
2. 트리거 키(F1~F12 또는 숫자)를 입력합니다.
3. 실행할 키 시퀀스를 쉼표(,)로 구분하여 입력합니다.
4. 매핑된 키를 누르면 입력된 시퀀스가 자동으로 실행됩니다.
5. **"입력 간격 설정"** 버튼을 클릭하면 전체 키 입력 간격을 설정할 수 있습니다.
6. **"선택한 매핑 삭제"** 버튼을 눌러 특정 매핑을 제거할 수 있습니다.

---

## 🔄 업데이트 예정 기능
- **다중 트리거 지원**: 여러 개의 키를 동시에 트리거로 설정
- **설정 저장 기능**: 매핑을 저장하고 불러올 수 있도록 개선 예정
- **키보드 조합 핫키 지원**: `Ctrl + A` 같은 조합키 매핑 기능 추가 예정

---

## 📜 라이선스
MIT License 하에 배포되며 자유롭게 사용 및 수정이 가능합니다.
django-admin-ai 0.1.6
An AI-powered assistant for Django Admin, allowing data import using OpenAI.
Requires Python: >=3.7

# Django Admin AI

An **AI-powered enhancement** for Django Admin that integrates artificial intelligence directly into the Django admin panel. This package provides utilities for **AI-assisted data processing, automatic form population, intelligent suggestions, and more**.

## 🚀 Key Features

✅ **AI-driven form population**: Automatically fill forms by extracting data from various document types (PDF, images, text files).  
✅ **Seamless Django Admin integration**: Enable AI-enhanced forms with a single line of code.  
✅ **Easy setup**: Minimal configuration required to get started.  

### 🔮 Future Enhancements (Planned Features)

🚀 **Voice input support**: Record audio directly in Django Admin and let AI extract and fill form data.  
🚀 **Foreign Key handling**: AI-driven form population for ForeignKey fields.  
🚀 **Embedded AI chatbot**: A chatbot trained with your database schema to assist users and execute database queries on demand.  

---

## 🛠 Installation

### Prerequisites
- Python **3.8+**  
- Django **3.0+**  

### Install via pip

```bash
pip install django-admin-ai
```

---

## ⚙️ Setup

### 1️⃣ Add `django_admin_ai` to your Django settings

In your project's `settings.py`, update the following:

```python
import django_admin_ai
import os

INSTALLED_APPS = [
    ...,
    'django_admin_ai',  # Add this line
]

TEMPLATES = [
    {
        'DIRS': [
            os.path.join(BASE_DIR, 'templates/'),
            os.path.join(os.path.dirname(django_admin_ai.__file__), 'templates'),
        ],
    },
]
```

Define your OpenAI API key in `settings.py`:

```python
DJANGO_ADMIN_AI_CONFIG = {
    "openai_api_key": "<YOUR_OPENAI_API_KEY>"
}
```

---

### 2️⃣ Configure URL routing

In your project's `urls.py`, include Django Admin AI:

```python
from django.urls import path, include

urlpatterns = [
    path('admin-ai/', include('django_admin_ai.urls')),
]
```

---

### 3️⃣ Enable AI-powered forms in your models

In your Django **app’s `admin.py`**, simply add the `ai_import` flag to your admin models:

```python
from django.contrib import admin
from .models import YourModel

class YourModelAdmin(admin.ModelAdmin):
    ai_import = True  # Enable AI-driven form filling

admin.site.register(YourModel, YourModelAdmin)
```

Now, when you upload a **PDF, text file, or image**, the AI will extract relevant information and populate the fields automatically! 🎉

---

## 📜 License

This project is licensed under the **MIT License**. Feel free to contribute and improve the project!

---

## 💡 Contributing

We welcome contributions! If you’d like to add a feature or fix a bug, feel free to fork the repository and submit a pull request.

### 📬 Stay Updated
Follow the repository and stay tuned for updates!

---

🔥 Start enhancing your Django Admin with AI today! 🔥