semvec/pypi-packages huggingface dataset:
streamlit-excel 0.0.2
Build Excel-style filter widgets for large pandas DataFrames in Streamlit
License: MIT

[![PyPI version](https://badge.fury.io/py/streamlit-excel.svg)](https://badge.fury.io/py/streamlit-excel)

Welcome to the `streamlit-excel` package!

This package provides a Streamlit component for filtering large pandas 
DataFrames (e.g., over 100,000 rows) through an intuitive, Excel-like 
interface. It enables interactive filtering directly within a Streamlit app, 
allowing users to explore and refine data visually and efficiently.

Designed for simplicity and seamless integration, the component is ideal for 
data scientists and developers building interactive data visualizations.

# Key Features

- **Easy Integration**: Initialize the component with a Pandas DataFrame using the `Table` class and store it in the session state.
- **Flexible Filtering**: Use `Table.show_filter_widget` to render a filter widget for specific columns. The widget can be placed anywhere in the app, including the sidebar.
- **Dynamic Views**: Access the filtered DataFrame via the `Table.view` property.
- **Multiple Tables**: Support for multiple tables by creating separate `Table` instances with unique `key` parameters.
- **Custom Column Names**: Customize display names in the filter widget by passing a dictionary to the `mapper` parameter when creating the `Table` instance.

# Installation

```sh
$ pip install streamlit-excel streamlit pandas
```

# Usage

Below is a simple example demonstrating how to use the `streamlit-excel` 
package to create a filterable table in a Streamlit app. In this example, we 
generate a random dataset with 100,000 rows and 5 columns to showcase the 
package's scalability.

Save the following code to `app.py`:

```python
import streamlit as st
import streamlit_excel
import pandas as pd
import numpy as np
import random
from datetime import datetime

if "example_table" not in st.session_state:
    n = 100000
    df = pd.DataFrame({
        "Name": np.random.choice(["Emily Johnson", "Michael Smith", "Lena Corwin", "Talia Vexley", "Kai Renford"], n),
        "City": np.random.choice(["Kyoto", "Cape Town", "Vancouver", "New York City", "Paris"], n),
        "Color": np.random.choice(["Red", "Green", "Yellow", "Blue"], n),
        "Start": [datetime(random.randint(1999, 2022), random.randint(1, 12), random.randint(1, 28)) for _ in range(n)],
        "End": [datetime(random.randint(2023, 2025), random.randint(1, 12), random.randint(1, 28)) for _ in range(n)],
    })
    st.session_state.example_table = streamlit_excel.Table(df, "example_table")

st.session_state.example_table.show_filter_widget("Example Table", ["Name", "City", "Color", "Start", "End"])
st.dataframe(st.session_state.example_table.view)
st.dataframe(st.session_state.example_table.view.describe(include="all"))
```

Run the app:

```sh
$ streamlit run app.py
```
django-smart-media 0.5.0
Django file fields with SVG support
License: MIT
Requires Python: >=3.10
Keywords: Python, Django, Image, Sorl

.. _Python: https://www.python.org/
.. _Django: https://www.djangoproject.com/
.. _sorl-thumbnail: https://github.com/jazzband/sorl-thumbnail

==================
Django smart media
==================

A suit of tools to use a FileField to upload image with light SVG support, include
thumbnail preview in field and a template tag around `sorl-thumbnail`_ library.


Dependencies
************

* `Python`_>=3.10;
* `Django`_>=4.2;
* `sorl-thumbnail`_>=12.9.0;


Overview
********

Concretely this contains:

* Form widgets to build HTML for a ``FileField`` either with or without clearable
  mode. Both mode have a version to include needed layout assets (CSS and Javascript)
  and another one without assets;
* Form fields which already set a "smart" widget;
* Templates to build the widgets HTML;
* CSS and Javascript for the widget layout;
* Field validator;
* Model signals to purge stale files;
* Model field with all "smart" features;
* A template tag to make thumbnail safely with SVG image (original SVG is just used
  without Sorl raising issue for a non bitmap image);
* Some helpers to ease some specific implementations;

Although this can work with ``ImageField``, SVG support will only work with
``FileField`` since ``ImageField`` rely on PIL that does not support SVG format.


Links
*****

* Read the documentation on `Read the docs <https://django-smart-media.readthedocs.io/>`_;
* Download its `PyPi package <https://pypi.python.org/pypi/django-smart-media>`_;
* Clone it on its `Github repository <https://github.com/sveetch/django-smart-media>`_;
zbyte 0.1.2
Python SDK for 0byte image generation and proof anchoring
License: MIT
Requires Python: >=3.8
Keywords: ai, proof, solana, image verification, zero byte

<p align="center">
  <img src="public/assets/0byte-logo-dark.png" alt="0byte logo" width="200"/>
</p>

<p align="center">
  <a href="https://www.0byte.tech/">
    <img src="https://img.shields.io/badge/Get%20Started-1abc9c?style=for-the-badge" alt="Get Started">
  </a>
  <a href="https://0byte.tech/verify">
    <img src="https://img.shields.io/badge/0byte%20Verifer-4C51BF?style=for-the-badge" alt="0byte Verifier">
  </a>
  <a href="https://pypi.org/project/zbyte">
    <img src="https://img.shields.io/badge/PyPI-zbyte-blue?style=for-the-badge" alt="Install from PyPI">
  </a>
</p>

# 0byte SDK

**0byte** is the official Python SDK for generating AI images and anchoring cryptographic proofs to the **Solana blockchain**. It supports integration with Hugging Face inference APIs and provides a seamless backend for verifiable media.

## What It Does

- Generates AI images via Hugging Face’s Stable Diffusion models  
- Anchors a **proof of generation** to Solana using the 0Byte protocol  
- Embeds the Solana transaction ID + platform into the image metadata  
- Enables downstream verification of the media origin

---

## Installation

```bash
pip install zbyte
```

## Quickstart
```python
from zbyte import Client, Config

config = Config(
    provider="stability",
    model="stable-diffusion-xl-base-1.0",
    api_key="your_huggingface_token",
    platform="0byte",
)

client = Client(config)

result = client.generate_image("A futuristic city at sunset")
with open("output.jpg", "wb") as f:
    f.write(result.image_bytes)

print("✅ Image verified at:", result.transaction_id)
```](url)