semvec/pypi-packages huggingface dataset:
django-floc-disable 1.2.0
Django middleware to disable Google FLoC tracking
Requires Python: >=3.8

# django-floc-disable

[![unit test](https://github.com/nhymxu/django-floc-disable/actions/workflows/test-python.yml/badge.svg?branch=main)](https://github.com/nhymxu/django-floc-disable/actions/workflows/test-python.yml)

Django middleware to disable Google's Federated Learning of Cohorts (`FLoC`) tracking

New version is Topics API

## Python / Django Support

- Python 3.8+
- Django versions 3+

## Usage

Install django-floc-disable:

```shell
pip install django-floc-disable
```

Add it to your `MIDDLEWARE` tuple in `settings.py`:

```python
MIDDLEWARE = (
    # ...
    'django_floc_disable.middleware.FLoCDisableMiddleware',
    # ...
)
```

This will set the `Permissions-Policy` header to a value of `browsing-topics=()` for every request served by Django.

Reference:
- https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Permissions-Policy/browsing-topics
- https://developers.google.com/privacy-sandbox/private-advertising/topics/web/controls#opt_out_as_a_developer

## Support

* Issues: <https://github.com/nhymxu/django-floc-disable/issues>
* Here you can [donate](https://dungnt.net/donate.html) for this project.

## License

The MIT License (MIT). Please see [License File](LICENSE.txt) for more information.
speaking-character-ai 0.1.0
A Flask-based web application for generating random responses with a beautiful interface
Requires Python: >=3.8

AI Voice Cloning & Storytelling Application

This application leverages advanced AI technology to generate personalized, unique voices for a variety of use cases, including storytelling, emotional connection, and educational purposes. The possibilities are endless with voice cloning, offering both entertainment and practical applications.

Key Features & Use Cases:

Personalized Storytelling – Create custom characters with unique voices to enhance storytelling and entertainment experiences.
Voice Cloning for Emotional Connection – Reconnect with lost loved ones by recreating their voices, preserving memories and fostering emotional connections.
Recreating Historical Figures – Bring historical figures to life with accurate voice simulations for educational and historical learning.
Unlimited Voice Cloning – Offer users unlimited voice cloning capabilities for various projects and applications.
Entertainment and Virtual Companionship – Provide AI-generated characters for engaging conversations, entertainment, and emotional support.
Language Learning – Enhance language skills by using AI voices for interactive practice and pronunciation.
This AI-powered tool opens doors to a world of creative and emotional possibilities, making voice technology more accessible and impactful.

Explore more at https://speakingcharacter.ai/⁠
curious-me 0.1.1
Small project for aiding in research and development
Requires Python: <4.0,>=3.11

# curious-me
Small library to ask questions, get reviews and citations of papers.
Currently papers are fetched from arxiv but it can be possible to use other sources as well.


# Installation

1. Create virtual environment and activate it
```bash
python -m venv venv
```
In Windows
```bash
venv\Scripts\activate
```
In GNU/Linux
```bash
source venv/bin/activate
```

2. Install package
If using pip
```bash
pip install curious-me
```

If cloned the repo
```bash
cd curious-me/curious_me
pip install .
```
4. Run the application, first open python then run the following commands
```python
from curious_me import Curious
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
            model_name="grok-2-1212",
            temperature=0.1,
            base_url="https://api.x.ai/v1",
            api_key=<your api key>,
        )

topics = ['GPT', 'LLM', 'Decoder', 'Artificial generative Intelligence']
curious = Curious(topics=topics, llm=llm) # or Curious(topics=topics, llm=llm, skip_search=True) if you # want to search papers and build vector store again
curious.ask("What are recent advances in GPT?")
curious.get_review("RAG")
curious.get_citation(claim="Leaky ReLU is better than ReLU")
```

![Demo](https://github.com/itsankitkp/gifstore/blob/main/curious_demo.gif)

# Note:
Steps for using own pdfs
1. Clone the repo.
2. Add your pdfs in the folder `curious-me/curious_me/research_papers`
3. Run the following commands
```python
from curious_me import Curious
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
            model_name="grok-2-1212",
            temperature=0.1,
            base_url="https://api.x.ai/v1",
            api_key=<your api key>,
        )
topics = [] # your topics
curious = Curious(topics=topics, llm=llm, skip_search=True, rebuild_vec_store=True)
curious.ask("Questions?")
```