semvec/pypi-packages huggingface dataset:
plants-vs-zombies 0.0.5
a simple PlantsVsZombies game
License: MIT
Requires Python: >=3.7
Keywords: pygame

# PythonPlantsVsZombies
  A simple PlantsVsZombies game. <br>
  `It's only for personal learning and noncommercial use. If this game infringes the copyright, please let me know.`
* implement plants: sunflower, peashooter, wallnut, snowpeashooter, cherrybomb, threepeashooter, chomper, puffshroom, potatomine, spikeweed, scaredyshroom, squash, scaredyshroom, jalapeno, sunShroom, iceShroom, hypnoShroom.
* implement zombies: zombie, flagzombie, coneheadzombie, bucketheadzombie, newspaperzombie.
* use json file to store level data (e.g.position and time of zombies, background info)
* support to select plant cards at the beginning of the level
* support day level, night level, moving card select level and wallnut bowling level

# Requirement
* Python 3.7 
* Notice: python version 3.7 is advisable, but not required. For LINUX: if your Linux system has a preinstalled python 3+, it's ok to run this game. Updating to python 3.7 directly may break LINUX Mint.
* Python-Pygame 1.9

# How To Start Game
$ python -m plants_vs_zombies
or
$ pip install plants-vs-zombies
$ plants-vs-zombies

# How to Play
* use mouse to collect sun, select the plant cards and seed the plant 
* you can set the start level by changing START_LEVEL_NUM value in plants_vs_zombies/constants.py
  * level 1 and 2：day level
  * level 3: night level
  * level 4: moving card select level
  * level 5: wallnut bowling level

# Demo
![demo1](https://raw.githubusercontent.com/marblexu/PythonPlantsVsZombies/master/demo/demo1.jpg)
![demo2](https://raw.githubusercontent.com/marblexu/PythonPlantsVsZombies/master/demo/demo2.jpg)
nonebot-plugin-charpic-migang 0.0.1
Generate picture(or GIF) by characters
License: MIT
Requires Python: >=3.10

<div align="center">

# 生成字符画（包括GIF）

</div>

<p align="center">
  
  <a href="https://github.com/RafuiiChan/nonebot_plugin_charpic/blob/main/LICENSE">
    <img src="https://img.shields.io/badge/license-MIT-informational">
  </a>
  <a href="https://github.com/nonebot/nonebot2">
    <img src="https://img.shields.io/badge/nonebot2-2.0.1-green">
  </a>
  
</p>
</p>

## 安装

1. 通过`pip`或`nb`安装；

命令

1. 使用 nb-cli , 需要在 bot 根目录下执行
```
nb plugin install nonebot_plugin_charpic
```

2. 使用 pip ，安装完需要手动在 pyproject.toml 中的 `tool.nonebot.plugins` 中添加 `nonebot_plugin_charpic` 以加载插件
```
pip install nonebot_plugin_charpic
```

## 功能

合成字符画(或 gif )

## 使用方法

`字符画` + `图片`

⚠ 需要 nonebot2 配置的命令前缀，如果没配置默认 `/` 

## 问题


### 缺资源
（没有字体的话可以尝试以下解决方案）
1. 找到这个包(一般在`site-packages`目录下)，将`data_source.py`中的`Path(__file__).parent / "font" / "consola.ttf"`改为你自己想要的字体的路径；
2. 在`__init__.py`同级目录创建文件夹，名为 `font`, 再下载本仓库中的字体放进去。
3. 直接clone本仓库，安装好依赖，放在bot能导入到的文件夹下面 ~~（dddd）~~
conflare 0.1.3
conformal retreival augmented generation with LLMs
License: MIT
Requires Python: >=3.10

<img src="https://i.ibb.co/qjZPJc3/conflare.png" alt="drawing" width="200"/>

# CONFLARE: CONFormal LArge language model REtrieval

This is the repo for the [CONFLARE paper](https://arxiv.org/abs/2404.04287) and the related python package `conflare`.

## Installation

```bash
pip install conflare
```

Here are the 3 main tasks this package helps you with:

1. Loading the source documents (+ cleaning and chunking them)
2. Creating (or loading) a Calibration set
3. Retrieval Augmented Generation by applying conformal prediction


## Usage

Example:

```python
# 1
import os
os.environ['OPENAI_API_KEY'] = 'your openai secret key'
# to use HuggingFace models w/o needing an openai key, look below.

import conflare
from conflare import initialize_pipeline
from conflare.conformal.calibration import create_calibration_records
from conflare.augmented_retrieval.rag import ConformalRetrievalQA

document_dir = './data/documents'
docs, qa_pipeline, vector_db = initialize_pipeline(document_dir)

# 2
calibration_records = create_calibration_records(
    docs,
    qa_pipeline=qa_pipeline,
    vector_db=vector_db,
    size=100,
    topic_of_interest="Deep Learning"
)

# 3
conformal_rag = ConformalRetrievalQA(
    qa_pipeline=qa_pipeline,
    vector_db=vector_db,
    calibration_records=calibration_records,
    error_rate=0.10,
    verbose=True
)

response, retrieved_docs = conformal_rag(
    "How can a transformer model be used in detection of COVID?"
)
print(response)
```
```
>>>
Input Error Rate: 10.00%
Selected cosine distance thereshold: 0.456
Number of retrieved documents: 2

A transformer model can be used in the detection of COVID-19 by analyzing medical images ...
```

If you have run this script once before and saved the calibration records to disk, you can use the following to load the calibration records:

```python
from conflare.conformal.calibration import QuestionEvaluation

q_evaluation = QuestionEvaluation.from_pickle(path_to_pickle)
calibration_records = q_evaluation.get_calibration_records()
```

## Arguments

Here are some of the more important arguments that the functions and classes in this package use.
You can also take a look at the definition of `initialize_pipeline` function to see most of them.
Looking at the definition of `initialize_pipeline`, you can see the sequence of the functions called inside it and use them in your own custom way if neccessary.


`model`: the model name used for QA and retreivals. If set to `gpt-*` models, it will use the OpenAI models and an OpenAI API Key will be required. It can also be set to models names on HuggingFace like `mistralai/Mistral-7B-Instruct-v0.1` to use HF models w/o needing a key. 

<br>

`embedding_model`: the model from `sentence-transformers` library to be used to create embeddings for text chunks and user questions.


![figure1](https://i.ibb.co/GCwVSVg/RAG.jpg)
![figure2](https://i.ibb.co/vPyySmT/conflare-pipeline.png)