semvec/pypi-packages huggingface dataset:
react-native-training 2024.3.3.0
React Native Training
Requires Python: >=3.6

# React Native Training

## 下载

### Docker

```
docker pull apachecn0/react-native-training
docker run -tid -p <port>:80 apachecn0/react-native-training
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install react-native-training
react-native-training <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g react-native-training
react-native-training <port>
# 访问 http://localhost:{port} 查看文档
```
random-forests-for-beginners 2024.3.3.0
RANDOM FORESTS FOR BEGINNERS
Requires Python: >=3.6

# RANDOM FORESTS FOR BEGINNERS

## 下载

### Docker

```
docker pull apachecn0/random-forests-for-beginners
docker run -tid -p <port>:80 apachecn0/random-forests-for-beginners
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install random-forests-for-beginners
random-forests-for-beginners <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g random-forests-for-beginners
random-forests-for-beginners <port>
# 访问 http://localhost:{port} 查看文档
```
sws-api-client 0.0.7.dev0
A Python client to easily interact with the FAO SWS REST APIs
Keywords: python,fao,sws,rest,api,client


# SWS API Client

This library provides the user with a set of useful tools to easily interact with the FAO SWS (Statistical Working System) REST APIs.

## Installation

The module is available on Pypi:

```bash
python -m pip install sws_api_client
```

The library requires Python 3.11+.

## Usage

To use the package the user needs to create an instance of the SwsApiClient class, provide it with the necessary parameters and execute the methods to query the specific endpoints.  

### Instantiate the client locally

There are three methods to instantiate the client:

#### 1. Pass the `sws_endpoint` and the `access_token` to the constructor

```python
from sws_api_client import SwsApiClient

sws_client = SwsApiClient(sws_endpoint="<sws_endpoint>", access_token="<access_token>")
```

#### 2. Pass to `sws_endpoint` and the `access_token` as named arguments

We need to execute the script from command line passing `--sws_endpoint` and `--access_token` as arguments:

```bash
python script.py --sws_endpoint <endpoint> --access_token <test_access_token>
```

And instantiate the client in our script with the class method `from_args`:

```python
from sws_api_client import SwsApiClient

sws_client = SwsApiClient.from_args()
```

#### 3. Create a conf file where to store the arguments

We need to create a conf file (default name: `"conf_sws_api_client.json"`) with the following structure:

```json
{
    "sws_endpoint": "<sws_endpoint>",
    "access_token": "<access_token>"
}
```

And instantiate the client in our script with the class method `from_conf`:

```python
from sws_api_client import SwsApiClient

sws_client = SwsApiClient.from_conf(conf_file="<conf_file_path>")

# Or, if the conf_file is named "conf_sws_api_client.json" and is in the current working directory

sws_client = SwsApiClient.from_conf()
```

### Instantiate the client in a SWS plugin

When working withing a SWS plugin instantiate the client as:

```python
from sws_api_client import SwsApiClient

sws_client = SwsApiClient.from_env()
```

### Perform requests

To perform requests you just need to call the available methods using the SwsApiClient object, as an example:

```python
dataset_details = sws_client.get_dataset_export_details(dataset_id="<dataset_id>")
```
grepper-python 0.0.1a0
An API wrapper for the Grepper API.
Requires Python: >=3.7

# Grepper Python Client
The Grepper Python library provides convenient access to the Grepper API from applications written in the Python language.

## Requirements
Python 3.7 and later.

## PIP
```
pip install grepper-python
```

## Manual Installation
```bash
git clone https://github.com/CantCode023/grepper-python
cd grepper-python
python setup.py install
```

## Getting Started
Simple usage:
```py
grepper = Grepper("your-grepper-api-key")
answers = grepper.search("query")
print(answers)
```
atb-lib 0.6.16
A Python package for Advanced Trading Bot
Keywords: crypto,exchange,trading,bot,package

1) **Title**: ATB Library
2) **Description**: This package serves as an auxiliary library for solving Advanced Trading Bot (ATB) challenges.
3) **Installation**: 
   ```bash
   pip install atb_lib
   ```
4) **Usage Examples**: This package is intended for internal use.
5) **Functionality**: It includes features for working with crypto exchange websockets, RabbitMQ, and more.
6) **Requirements**:
   - `colorlog>=6.8.2`: For colored logging output.
   - `aio_pika>=9.4.0`: For RabbitMQ support.
   - `asyncio>=3.4.3`: For async/await syntax support.
   - `importlib-metadata>=7.0.1`: For metadata handling, like versions.
   - `uvicorn[standard]>=0.27.1`: ASGI server for FastAPI.
   - `fastapi>=0.109.2`: Web framework for building APIs.
   - `websockets>=12.0`: For websocket support.
   - `pip>=24.0.0`: Package installer for Python.
   - `setuptools>=69.1.1`: Python setup tool.
   - `arrow>=1.3.0`: For date time functions.
   - `msgpack>=1.0.7`: For efficient data serialization.
   - `wheel>=0.42.0`: For building the wheel package.
   - `starlette>=0.36.3`: For FastAPI web framework.
7) **License**: MIT License.
8) **Contact Information**: [GitHub - yuriytigiev](https://github.com/yuriytigiev)
9) **Acknowledgments and References**: Special thanks to the open-source community and contributors who made this project possible.
culingam 0.0.8
CULiNGAM accelerates LiNGAM analysis on GPUs.
Requires Python: >=3.8

# CULiNGAM - CUDA Accelerated LiNGAM Analysis

## Introduction

CULiNGAM is a high-performance library that accelerates Linear Non-Gaussian Acyclic Model (LiNGAM) analysis on GPUs. It leverages the computing power of NVIDIA GPUs to provide fast and efficient computations for LiNGAM applications, making it ideal for large-scale data analysis in fields such as bioinformatics, economics, and machine learning.

## Installation

To install CULiNGAM, you need a system with NVIDIA CUDA installed and a compatible GPU. This library is tested with CUDA 12.2 and designed for GPUs with the architecture `sm_86`, although it may work with other versions and architectures with appropriate adjustments.

### Prerequisites

- NVIDIA GPU with CUDA Compute Capability 8.6 or higher
- CUDA Toolkit 12.2 or compatible version
- Python 3.6 or newer
- Numpy
- A C++17 compatible compiler

### Installing from PyPI

CULiNGAM is available on PyPI and can be easily installed with pip:

```bash
pip install culingam
```

### Installing CULiNGAM manually

1. Clone the repository to your local machine:
```bash
git clone https://github.com/Viktour19/culingam
```

2. Ensure that CUDA_HOME environment variable is set to your CUDA Toolkit installation path. If not, you can set it as follows (example for default CUDA installation path):
```bash
export CUDA_HOME=/usr/local/cuda-12.2
```

3. Optionally, set the GPU_ARCH environment variable to match your GPU architecture if it differs from the default sm_86:
```bash
export GPU_ARCH=sm_xx  # Replace xx with your GPU's compute capability
```

4. Install the library using pip:
```bash
pip install .
```

### Usage
After installation, you can use CULiNGAM in your Python projects to accelerate LiNGAM analysis. Here's a simple example to get started:

https://github.com/Viktour19/culingam/blob/main/examples/basic.py

### Support
For bugs, issues, and feature requests, please submit a report to the repository's issue tracker. Contributions are also welcome.

### Author
Victor Akinwande

### License and Acknowledgment
This project is licensed under the MIT License - see the LICENSE file for details.
A good amount of the code is adapted from sequential implementations of the LiNGAM algorithms present in CULiNGAM: https://github.com/cdt15/lingam.
machine-learning-super-vip-cheatsheet 2024.3.3.0
Machine Learning Super VIP Cheatsheet
Requires Python: >=3.6

# Machine Learning Super VIP Cheatsheet

## 下载

### Docker

```
docker pull apachecn0/machine-learning-super-vip-cheatsheet
docker run -tid -p <port>:80 apachecn0/machine-learning-super-vip-cheatsheet
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install machine-learning-super-vip-cheatsheet
machine-learning-super-vip-cheatsheet <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g machine-learning-super-vip-cheatsheet
machine-learning-super-vip-cheatsheet <port>
# 访问 http://localhost:{port} 查看文档
```
little-asp-net-core-book 2024.3.3.0
Little ASP.NET Core Book
Requires Python: >=3.6

# Little ASP.NET Core Book

## 下载

### Docker

```
docker pull apachecn0/little-asp-net-core-book
docker run -tid -p <port>:80 apachecn0/little-asp-net-core-book
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install little-asp-net-core-book
little-asp-net-core-book <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g little-asp-net-core-book
little-asp-net-core-book <port>
# 访问 http://localhost:{port} 查看文档
```
linux-neihe-yuandaima-qingjing-fenxi 2024.3.3.0
Linux 内核源代码情景分析
Requires Python: >=3.6

# Linux 内核源代码情景分析

## 下载

### Docker

```
docker pull apachecn0/linux-neihe-yuandaima-qingjing-fenxi
docker run -tid -p <port>:80 apachecn0/linux-neihe-yuandaima-qingjing-fenxi
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install linux-neihe-yuandaima-qingjing-fenxi
linux-neihe-yuandaima-qingjing-fenxi <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g linux-neihe-yuandaima-qingjing-fenxi
linux-neihe-yuandaima-qingjing-fenxi <port>
# 访问 http://localhost:{port} 查看文档
```
linux-mingling-daquan-linuxtoday 2024.3.3.0
Linux 命令大全（LinuxToday）
Requires Python: >=3.6

# Linux 命令大全（LinuxToday）

## 下载

### Docker

```
docker pull apachecn0/linux-mingling-daquan-linuxtoday
docker run -tid -p <port>:80 apachecn0/linux-mingling-daquan-linuxtoday
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install linux-mingling-daquan-linuxtoday
linux-mingling-daquan-linuxtoday <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g linux-mingling-daquan-linuxtoday
linux-mingling-daquan-linuxtoday <port>
# 访问 http://localhost:{port} 查看文档
```
less-jianming-jiaocheng-wai-bowang 2024.3.3.0
Less 简明教程（歪脖网）
Requires Python: >=3.6

# Less 简明教程（歪脖网）

## 下载

### Docker

```
docker pull apachecn0/less-jianming-jiaocheng-wai-bowang
docker run -tid -p <port>:80 apachecn0/less-jianming-jiaocheng-wai-bowang
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install less-jianming-jiaocheng-wai-bowang
less-jianming-jiaocheng-wai-bowang <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g less-jianming-jiaocheng-wai-bowang
less-jianming-jiaocheng-wai-bowang <port>
# 访问 http://localhost:{port} 查看文档
```
leetbook-leetcode-xiangjie 2024.3.3.0
LeetBook（LeetCode详解）
Requires Python: >=3.6

# LeetBook（LeetCode详解）

## 下载

### Docker

```
docker pull apachecn0/leetbook-leetcode-xiangjie
docker run -tid -p <port>:80 apachecn0/leetbook-leetcode-xiangjie
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install leetbook-leetcode-xiangjie
leetbook-leetcode-xiangjie <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g leetbook-leetcode-xiangjie
leetbook-leetcode-xiangjie <port>
# 访问 http://localhost:{port} 查看文档
```
learning-scrapy-zhongwenban 2024.3.3.0
Learning Scrapy 中文版
Requires Python: >=3.6

# Learning Scrapy 中文版

## 下载

### Docker

```
docker pull apachecn0/learning-scrapy-zhongwenban
docker run -tid -p <port>:80 apachecn0/learning-scrapy-zhongwenban
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install learning-scrapy-zhongwenban
learning-scrapy-zhongwenban <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g learning-scrapy-zhongwenban
learning-scrapy-zhongwenban <port>
# 访问 http://localhost:{port} 查看文档
```
learning-rust 2024.3.3.0
Learning Rust
Requires Python: >=3.6

# Learning Rust

## 下载

### Docker

```
docker pull apachecn0/learning-rust
docker run -tid -p <port>:80 apachecn0/learning-rust
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install learning-rust
learning-rust <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g learning-rust
learning-rust <port>
# 访问 http://localhost:{port} 查看文档
```
learning-deep-architectures-for-ai-zhongwenban-chuyi 2024.3.3.0
Learning Deep Architectures for AI 中文版（初译）
Requires Python: >=3.6

# Learning Deep Architectures for AI 中文版（初译）

## 下载

### Docker

```
docker pull apachecn0/learning-deep-architectures-for-ai-zhongwenban-chuyi
docker run -tid -p <port>:80 apachecn0/learning-deep-architectures-for-ai-zhongwenban-chuyi
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install learning-deep-architectures-for-ai-zhongwenban-chuyi
learning-deep-architectures-for-ai-zhongwenban-chuyi <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g learning-deep-architectures-for-ai-zhongwenban-chuyi
learning-deep-architectures-for-ai-zhongwenban-chuyi <port>
# 访问 http://localhost:{port} 查看文档
```
laravel-yuanma-jiexi 2024.3.3.0
laravel 源码解析
Requires Python: >=3.6

# laravel 源码解析

## 下载

### Docker

```
docker pull apachecn0/laravel-yuanma-jiexi
docker run -tid -p <port>:80 apachecn0/laravel-yuanma-jiexi
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install laravel-yuanma-jiexi
laravel-yuanma-jiexi <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g laravel-yuanma-jiexi
laravel-yuanma-jiexi <port>
# 访问 http://localhost:{port} 查看文档
```
kungeekui 2024.3.3.0
kungeekUI
Requires Python: >=3.6

# kungeekUI

## 下载

### Docker

```
docker pull apachecn0/kungeekui
docker run -tid -p <port>:80 apachecn0/kungeekui
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install kungeekui
kungeekui <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g kungeekui
kungeekui <port>
# 访问 http://localhost:{port} 查看文档
```
kubernetes-zhongwen-wendang 2024.3.3.0
Kubernetes 中文文档
Requires Python: >=3.6

# Kubernetes 中文文档

## 下载

### Docker

```
docker pull apachecn0/kubernetes-zhongwen-wendang
docker run -tid -p <port>:80 apachecn0/kubernetes-zhongwen-wendang
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install kubernetes-zhongwen-wendang
kubernetes-zhongwen-wendang <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g kubernetes-zhongwen-wendang
kubernetes-zhongwen-wendang <port>
# 访问 http://localhost:{port} 查看文档
```
kubernetes-zhinan 2024.3.3.0
Kubernetes 指南
Requires Python: >=3.6

# Kubernetes 指南

## 下载

### Docker

```
docker pull apachecn0/kubernetes-zhinan
docker run -tid -p <port>:80 apachecn0/kubernetes-zhinan
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install kubernetes-zhinan
kubernetes-zhinan <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g kubernetes-zhinan
kubernetes-zhinan <port>
# 访问 http://localhost:{port} 查看文档
```
kivy-zhongwen-biancheng-zhinan 2024.3.3.0
Kivy中文编程指南
Requires Python: >=3.6

# Kivy中文编程指南

## 下载

### Docker

```
docker pull apachecn0/kivy-zhongwen-biancheng-zhinan
docker run -tid -p <port>:80 apachecn0/kivy-zhongwen-biancheng-zhinan
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install kivy-zhongwen-biancheng-zhinan
kivy-zhongwen-biancheng-zhinan <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g kivy-zhongwen-biancheng-zhinan
kivy-zhongwen-biancheng-zhinan <port>
# 访问 http://localhost:{port} 查看文档
```
kira-xiandaixiao-juhua-wan-2017-quan 2024.3.3.0
Kira 线代小菊花丸 2017（全）
Requires Python: >=3.6

# Kira 线代小菊花丸 2017（全）

## 下载

### Docker

```
docker pull apachecn0/kira-xiandaixiao-juhua-wan-2017-quan
docker run -tid -p <port>:80 apachecn0/kira-xiandaixiao-juhua-wan-2017-quan
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install kira-xiandaixiao-juhua-wan-2017-quan
kira-xiandaixiao-juhua-wan-2017-quan <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g kira-xiandaixiao-juhua-wan-erlingyiqi-quan
kira-xiandaixiao-juhua-wan-erlingyiqi-quan <port>
# 访问 http://localhost:{port} 查看文档
```
kira-gaoshu-kuihuabaodian-2017-xiudingban 2024.3.3.0
Kira 高数葵花宝典 2017（修订版）
Requires Python: >=3.6

# Kira 高数葵花宝典 2017（修订版）

## 下载

### Docker

```
docker pull apachecn0/kira-gaoshu-kuihuabaodian-2017-xiudingban
docker run -tid -p <port>:80 apachecn0/kira-gaoshu-kuihuabaodian-2017-xiudingban
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install kira-gaoshu-kuihuabaodian-2017-xiudingban
kira-gaoshu-kuihuabaodian-2017-xiudingban <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g kira-gaoshu-kuihuabaodian-erlingyiqi-xiudingban
kira-gaoshu-kuihuabaodian-erlingyiqi-xiudingban <port>
# 访问 http://localhost:{port} 查看文档
```
kira-gaoshu-kuihuabaodian-2017 2024.3.3.0
Kira 高数葵花宝典 2017
Requires Python: >=3.6

# Kira 高数葵花宝典 2017

## 下载

### Docker

```
docker pull apachecn0/kira-gaoshu-kuihuabaodian-2017
docker run -tid -p <port>:80 apachecn0/kira-gaoshu-kuihuabaodian-2017
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install kira-gaoshu-kuihuabaodian-2017
kira-gaoshu-kuihuabaodian-2017 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g kira-gaoshu-kuihuabaodian-erlingyiqi
kira-gaoshu-kuihuabaodian-erlingyiqi <port>
# 访问 http://localhost:{port} 查看文档
```
kira-gaitong-shipian-2018 2024.3.3.0
Kira 概统十篇 2018
Requires Python: >=3.6

# Kira 概统十篇 2018

## 下载

### Docker

```
docker pull apachecn0/kira-gaitong-shipian-2018
docker run -tid -p <port>:80 apachecn0/kira-gaitong-shipian-2018
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install kira-gaitong-shipian-2018
kira-gaitong-shipian-2018 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g kira-gaitong-shipian-erlingyiba
kira-gaitong-shipian-erlingyiba <port>
# 访问 http://localhost:{port} 查看文档
```
json-jiaocheng-wai-bowang 2024.3.3.0
JSON 教程（歪脖网）
Requires Python: >=3.6

# JSON 教程（歪脖网）

## 下载

### Docker

```
docker pull apachecn0/json-jiaocheng-wai-bowang
docker run -tid -p <port>:80 apachecn0/json-jiaocheng-wai-bowang
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install json-jiaocheng-wai-bowang
json-jiaocheng-wai-bowang <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g json-jiaocheng-wai-bowang
json-jiaocheng-wai-bowang <port>
# 访问 http://localhost:{port} 查看文档
```
jiqi-xuexi-xunlian-miji-machine-learning-yearning-zhongwenban 2024.3.3.0
机器学习训练秘籍（Machine Learning Yearning 中文版）
Requires Python: >=3.6

# 机器学习训练秘籍（Machine Learning Yearning 中文版）

## 下载

### Docker

```
docker pull apachecn0/jiqi-xuexi-xunlian-miji-machine-learning-yearning-zhongwenban
docker run -tid -p <port>:80 apachecn0/jiqi-xuexi-xunlian-miji-machine-learning-yearning-zhongwenban
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install jiqi-xuexi-xunlian-miji-machine-learning-yearning-zhongwenban
jiqi-xuexi-xunlian-miji-machine-learning-yearning-zhongwenban <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g jiqi-xuexi-xunlian-miji-machine-learning-yearning-zhongwenban
jiqi-xuexi-xunlian-miji-machine-learning-yearning-zhongwenban <port>
# 访问 http://localhost:{port} 查看文档
```
gongye-kongzhi-wangluoanquan-jichu-v2 2024.3.3.0
工业控制网络安全基础 v2
Requires Python: >=3.6

# 工业控制网络安全基础 v2

## 下载

### Docker

```
docker pull apachecn0/gongye-kongzhi-wangluoanquan-jichu-v2
docker run -tid -p <port>:80 apachecn0/gongye-kongzhi-wangluoanquan-jichu-v2
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install gongye-kongzhi-wangluoanquan-jichu-v2
gongye-kongzhi-wangluoanquan-jichu-v2 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g gongye-kongzhi-wangluoanquan-jichu-v2
gongye-kongzhi-wangluoanquan-jichu-v2 <port>
# 访问 http://localhost:{port} 查看文档
```
getting-started-in-computer-vision-research 2024.3.3.0
Getting Started in Computer Vision Research
Requires Python: >=3.6

# Getting Started in Computer Vision Research

## 下载

### Docker

```
docker pull apachecn0/getting-started-in-computer-vision-research
docker run -tid -p <port>:80 apachecn0/getting-started-in-computer-vision-research
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install getting-started-in-computer-vision-research
getting-started-in-computer-vision-research <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g getting-started-in-computer-vision-research
getting-started-in-computer-vision-research <port>
# 访问 http://localhost:{port} 查看文档
```
gei-ios-kaifazhe-de-sketch-rumenjiaocheng 2024.3.3.0
给iOS开发者的Sketch入门教程
Requires Python: >=3.6

# 给iOS开发者的Sketch入门教程

## 下载

### Docker

```
docker pull apachecn0/gei-ios-kaifazhe-de-sketch-rumenjiaocheng
docker run -tid -p <port>:80 apachecn0/gei-ios-kaifazhe-de-sketch-rumenjiaocheng
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install gei-ios-kaifazhe-de-sketch-rumenjiaocheng
gei-ios-kaifazhe-de-sketch-rumenjiaocheng <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g gei-ios-kaifazhe-de-sketch-rumenjiaocheng
gei-ios-kaifazhe-de-sketch-rumenjiaocheng <port>
# 访问 http://localhost:{port} 查看文档
```
askdoc 0.1.1
Ask a personal doctor for your medical queries

# AskDoc

This bot understands your medical concerns and helps diagnose them. Here is what you can do:

1. Ask for your health-related questions.
2. Send a medical report and ask questions based on that

Components

1. **ChatGPT (Vision)**: This is given a prompt to act like a doctor and do diagnostics
2. **Tesseract**: To extract information from PDF and image reports
3. **Validation agent** (Coming soon): This validates input/output
4. **Memory** stored in NumPy array. This memory is queried for information when the user asks for a query
5. **Summarizer** that summarizes after 100 turns and stores that summary in the memory

## Setup

### Clone the repo

```bash
git clone <repo>
cd askdoc
touch .env
open .env # Add OPENAI_API_KEY to this
```

### Create a new virtualenv

```bash
python3 -m venv myenv
source myenv/bin/activate
```

### Install the package

```bash
pip install .
```

### Run the command

```bash
# This will open a local server
askdoc
```
fuliye-bianhuan-jiqi-yingyong-sitanfudaxue 2024.3.3.0
傅里叶变换及其应用（斯坦福大学）
Requires Python: >=3.6

# 傅里叶变换及其应用（斯坦福大学）

## 下载

### Docker

```
docker pull apachecn0/fuliye-bianhuan-jiqi-yingyong-sitanfudaxue
docker run -tid -p <port>:80 apachecn0/fuliye-bianhuan-jiqi-yingyong-sitanfudaxue
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install fuliye-bianhuan-jiqi-yingyong-sitanfudaxue
fuliye-bianhuan-jiqi-yingyong-sitanfudaxue <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g fuliye-bianhuan-jiqi-yingyong-sitanfudaxue
fuliye-bianhuan-jiqi-yingyong-sitanfudaxue <port>
# 访问 http://localhost:{port} 查看文档
```
frida-zhongwen-wendang 2024.3.3.0
Frida 中文文档
Requires Python: >=3.6

# Frida 中文文档

## 下载

### Docker

```
docker pull apachecn0/frida-zhongwen-wendang
docker run -tid -p <port>:80 apachecn0/frida-zhongwen-wendang
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install frida-zhongwen-wendang
frida-zhongwen-wendang <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g frida-zhongwen-wendang
frida-zhongwen-wendang <port>
# 访问 http://localhost:{port} 查看文档
```
freebsd-101-hacks 2024.3.3.0
FreeBSD 101 Hacks
Requires Python: >=3.6

# FreeBSD 101 Hacks

## 下载

### Docker

```
docker pull apachecn0/freebsd-101-hacks
docker run -tid -p <port>:80 apachecn0/freebsd-101-hacks
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install freebsd-101-hacks
freebsd-101-hacks <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g freebsd-yilingyi-hacks
freebsd-yilingyi-hacks <port>
# 访问 http://localhost:{port} 查看文档
```
forecasting-principles-and-practice-2e 2024.3.3.0
Forecasting Principles and Practice  2e
Requires Python: >=3.6

# Forecasting Principles and Practice  2e

## 下载

### Docker

```
docker pull apachecn0/forecasting-principles-and-practice-2e
docker run -tid -p <port>:80 apachecn0/forecasting-principles-and-practice-2e
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install forecasting-principles-and-practice-2e
forecasting-principles-and-practice-2e <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g forecasting-principles-and-practice-2e
forecasting-principles-and-practice-2e <port>
# 访问 http://localhost:{port} 查看文档
```