semvec/pypi-packages huggingface dataset:
whatsapp-parse 0.2.2
A parsing library for WhatsApp chat logs
License: MIT

# WhatsApp Chat Parser

## Introduction
WhatsApp Parser is a tool designed to simplify the process of parsing text data extracted from WhatsApp conversations. It aims to provide users with an easy-to-use interface for extracting meaningful information from chat logs, such as timestamps, sender names, and messages.

```
pip install whatsapp-parse
```

## Features
- Extract conversation data including date, time, sender, and message content.  
- Parse chat logs into a Pandas DataFrame
- Directly output a CSV file from the command line.

## Future Updates
- Add more output formats.
- Add media attachments handling.

## Contributing
We welcome contributions to the WhatsApp Chat Parser project. If you have suggestions or improvements, please open an issue or submit a pull request.

## Contact
For any questions or feedback, please contact me **Hassn Hamada** at hassneltokhy4@gmail.com.
superstream-beta 1.0.4
Default template for PDM package
License: MIT
Requires Python: >=3.8

# Superstream

## Installation

```sh
 pip install superstream
```

## Importing

```python
import superstream
from superstream.types import Option
```

## Producer

To use `superstream` with kafka producer, first define the kafka and superstream configurations:
  
```python
token = "<superstream-token>"
superstream_host = "<superstream-host>"
brokers = "<kafka-broker>"
topic = "<kafka-topic>"
config = {"bootstrap.servers": brokers}
options = Option(learning_factor=10, servers=brokers)
```

To initialize superstream, use `init` function and pass the producer instance as an argument:

```python
producer = Producer(config)
producer = superstream.init(token, superstream_host, config, options, producer=producer)
```

Finally, to produce messages to kafka, use `produce` function:

```python
person = {"name": "John Doe", "message": f"Hello, World!"}
producer.produce(
    topic,
    person,
    on_delivery=delivery_callback,
    headers={"key": "value"},
)
```

## Consumer

To use `superstream` with kafka consumer, first define the consumer configurations:

```python
token = "<superstream-token>"
superstream_host = "<superstream-host>"
group = "<kafka-consumer-group>"
topics = ["<kafka-topic>"]
brokers = "<kafka-broker>"
config = {
    "bootstrap.servers": brokers,
    "group.id": group
}
options = Option(learning_factor=10, servers=brokers)
```

To initialize superstream, use `init` function and pass the consumer instance as an argument:

```python
consumer = Consumer(config)
consumer = superstream.init(token, superstream_host, config, options, consumer=consumer)
```
gamba-torch 0.0.1
gamba - Pytorch
License: MIT
Requires Python: <4.0,>=3.6
Keywords: artificial intelligence, deep learning, optimizers, Prompt Engineering

[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)

# Gamba
Implementation of "GAMBA: MARRY GAUSSIAN SPLATTING WITH MAMBA FOR SINGLE-VIEW 3D RECONSTRUCTION" in PyToch

## install
`$ pip intall gamba`

## usage
```python
import torch 
from gamba.main import Gamba


# Forward pass of the GambaDecoder module.
x = torch.randn(1, 1000, 512)

# Model
model = Gamba(
    dim=512,
    d_state=512,
    d_conv=512,
    n=16384,
    depth=3
)

# Out
out = model(x)
print(out)
```


# License
MIT