semvec/pypi-packages huggingface dataset:
python-bingsearch 1.1.0
bing Search unofficial API for Python with no external dependencies
Keywords: search-api bing python

Python Bing Search API
======================

Unofficial bing Search API for Python.

It uses web scraping in the background and is compatible with both
**Python 2 and 3**.

Source
------

https://github.com/samsha1971/python-bingsearch

Why this project?
-----------------

for everyone who want to use bing search.

Installation
------------

.. code:: bash

   pip install python-bingsearch

Using
-----

.. code:: bash

   from bingsearch.bingsearch import BingSearch

   bs = BingSearch()
   data = bs.search("python")
   print(data)

.. code:: python

   # reuse in a program

   ## first times
   with BingSearch() as bs:
       results = bs.search(
           args.keyword, num_results=args.num_results, debug=args.debug)
       
   ## second times
   bs = BingSearch()
   results = bs.search(
       args.keyword, num_results=args.num_results, debug=args.debug)
   bs.release_resource() # Need active resource release

   ## third times
   with BingSearch() as bs:
       results = bs.search(
           args.keyword, num_results=args.num_results, debug=args.debug)
JTRFaker 1.1.0
A library to generate fake data for various use cases
Requires Python: >=3.9

# JTRFaker

The `JTRFaker` package is a utility that is focused on generating fake data for testing and
development purposes. It leverages the power of the Faker library to create realistic yet
random data that can be used to populate your database during `development` or for
`testing` your application's functionality.

## Installation

```bash
pip install JTRFaker
```

## Usage

The usage varies depending on the type of data you want to generate. The following are the
different types of data that can be generated using the `JTRFaker` module:

1. [`ModelFaker`](src/ModelFaker/README.md) - Database entries based on sqlalchemy models with lots of different options.
siliconperception 0.0.13
Single chip image encoder and pose decoder with pretrained weights from HuggingFace
Requires Python: >=3.8

# IE120R image encoder
- PyTorch model for training
- classification accuracy script using frozen pretrained weights
- pretraining script using knowedge distillation from resnet18
- Verilog code for functional simulation, uses FP32 weights
- Verilog testbench to verify that the Verilog matches PyTorch
- Verilog code for Quartus compilation, uses bfloat18 weights
- Quartus .ip files, .sdc

# DX120P pose decoder
- PyTorch model for training
- Verilog code for Quartus compilation, uses bfloat18 weights
- Quartus .ip files, dummy top.v, top.sdc
- functional verification TBD
- pretraining TBD
- evaluation TBD
dudl 0.6.0
The DUDL Game
Requires Python: >=3.12

# DÜDL
Welcome to DÜDL! Here's how to get started:

## Option 1:

1. Install using `pip install dudl`.
2. Run with the `start-dudl` command.

## Option 2:

1. Download the files in this repository.
2. If you do not already have `pip` installed, download it [here](https://pip.pypa.io/en/stable/installation/) first, then run `pip install -e .` to install all dependencies. Alternatively, you may install all dependencies manually. 
3. Run with the `start-dudl` command, or just `python3 dudl/dudl_app.py`.

Once the server is running, note the IP address that is printed out (http://192.168.1.15:8001) - that's what needs to be entered on your phone. Once all the server info has been entered, the `Connect and Continue` button on the app should light up green, and you should be ready to go! All your friends playing locally can join your server by entering the same server information.