semvec/pypi-packages huggingface dataset:
llama-index-readers-imdb-review 0.4.2
llama-index readers imdb_review integration
Requires Python: <4.0,>=3.9
Keywords: IMDB, movies, reviews

## IMDB MOVIE REVIEWS LOADER

```bash
pip install llama-index-readers-imdb-review
```

This loader fetches all the reviews of a movie or a TV-series from IMDB official site. This loader is working on Windows machine and it requires further debug on Linux. Fixes are on the way

Install the required dependencies

```
pip install -r requirements.txt
```

The IMDB downloader takes in two attributes

- movie_name_year: The name of the movie or series and year
- webdriver_engine: To use edge, google or gecko (mozilla) webdriver
- generate_csv: Whether to generate csv file
- multithreading: whether to use multithreading or not

## Usage

```python
from llama_index.readers.imdb_review import IMDBReviews

loader = IMDBReviews(
    movie_name_year="The Social Network 2010", webdriver_engine="edge"
)
docs = loader.load_data()
```

The metadata has the following information

- date of the review (date)
- title of the review (title)
- rating of the review (rating)
- link of the review (link)
- whether the review is spoiler or not (spoiler)
- number of people found the review helpful (found_helpful)
- total number of votes (total)

It will download the files inside the folder `movie_reviews` with the filename as the movie name

## EXAMPLES

This loader can be used with both Langchain and LlamaIndex.

### LlamaIndex

```python
from llama_index.core import VectorStoreIndex, download_loader
from llama_index.core import VectorStoreIndex

from llama_index.readers.imdb_review import IMDBReviews

loader = IMDBReviewsloader(
    movie_name_year="The Social Network 2010",
    webdriver_engine="edge",
    generate_csv=False,
    multithreading=False,
)
docs = loader.load_data()

index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()

response = query_engine.query(
    "What did the movie say about Mark Zuckerberg?",
)
print(response)
```

### Langchain

```python
from langchain.llms import OpenAI
from langchain.agents.agent_toolkits.pandas import (
    create_pandas_dataframe_agent,
)
from langchain.agents import Tool
from langchain.agents import initialize_agent
from langchain.chat_models import ChatOpenAI

from llama_index.readers.imdb_review import IMDBReviews

loader = IMDBReviewsloader(
    movie_name_year="The Social Network 2010",
    webdriver_engine="edge",
    generate_csv=False,
    multithreading=False,
)
docs = loader.load_data()
tools = [
    Tool(
        name="LlamaIndex",
        func=lambda q: str(index.as_query_engine().query(q)),
        description="useful for when you want to answer questions about the movies and their reviews. The input to this tool should be a complete english sentence.",
        return_direct=True,
    ),
]
llm = ChatOpenAI(temperature=0)
agent = initialize_agent(tools, llm, agent="conversational-react-description")
agent.run("What did the movie say about Mark Zuckerberg?")
```
tivar 0.1.3
Translation Initiation Variation
Requires Python: <4,>=3.4
Keywords: translation, initiation, RNA, variation

# TIVar
Translation Initiation Variation

Predict translation initiation (TI) efficiency for potential start codons, based on the context sequence near the start codon. Given SNP/Indel variation, this tools can predict changes of TI efficiencies between ref and alt alleles.

# INSTALL

Python version >= 3.4.

**Requirements**

[NumPy](https://numpy.org/)

[PyTorch](https://pytorch.org/)

**Install from source**

`git clone https://github.com/zhpn1024/TIVar`

`python setup.py install`

or

`python setup.py install --user`


**Install from PyPI**

`pip install tivar`


# Usage

**predict**

This module can calculate TI efficiency scores from given sequences.

Fasta sequence file as input:

`tivar predict -S test1.fa -o out1.txt`

Provide sequence in the parameter:

`tivar predict -s aaaaaacaaaaaaaTGTACAATGGATGCATTGAAATTATATGTAATTGTATAAATGGTGCAACA -o out1.txt`

Provide transcript annotation and genome sequence:

`tivar predict -g hg38_gc31.gtf.gz -f hg38.fa -o out1.txt`

The output is like:

|SeqID|Pos|StartSeq|EffScore|
|-----|-----|-----|-----|
|Seq|13|aacaaaaaa-aTG-TACA|0.30354|
|Seq|20|aaaTGTACA-ATG-GATG|0.37131|



**diff**

This module predict TI changes caused by sequence variation.

`tivar diff -i test.vcf -g hg38_gc31.gtf.gz -f hg38.fa -o out2.txt`

The output is like:

|Gid|Tid|Var|GenoPos|Strand|Pos|RefSeq|AltSeq|EffeRef|EffeAlt|Diff|FC|Type|
|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|
|ENSG00000134262.13|ENST00000369569.6|chr1:113895309:A>AC|113895310|-|2056|ACCCTCCAG-ATG-GCTC|ACCCTCCAG-AGT-GGCT|0.32097|0.0|-0.321|0.0|TI_decreased|
|ENSG00000134262.13|ENST00000369569.6|chr1:113895309:A>AC|113895310|-|2056|ACCCTCCAG-ATG-GCTC|CCCTCCAGA-GTG-GCTC|0.32097|0.04335|-0.2776|0.1351|TI_decreased|
gzspidertools 0.0.30
Scrapy Tools
Requires Python: >=3.11
Keywords: crawler, scraping, scrapy-extension

概述
=======
   一句话介绍：用于扩展 Scrapy 功能来解放双手。


安装
=======

   可以直接输入以下命令：

.. code:: bash

   pip install gzspidertools

-------------------------------

   可选安装1，安装数据库相关的所有依赖：

.. code:: bash

   pip install gzspidertools[database]

-----------------------------------------

   可选安装2，通过以下命令安装所有依赖：

.. code:: bash

   pip install gzspidertools[all]

注：详细的安装介绍请查看 `安装指南`_。

用法
=======
   开发人员只需根据命令生成示例模板，再配置相关设置即可。

   # 查看库版本
   gzcmd version

   # 创建项目
   gzcmd startproject <project_name>

   # 进入项目根目录
   cd <project_name>

   # 替换(覆盖)为真实的配置 .conf 文件：
   # 这里是为了演示方便，正常情况是直接在 VIT 中的 .conf 文件填上你需要的配置即可
   cp /root/mytemp/.conf DemoSpider/VIT/.conf

   # 生成爬虫脚本
   gzcmd genspider <spider_name> <example.com>

   # 运行脚本
   scrapy crawl <spider_name>
   # 注：也可以使用 gzcmd crawl <spider_name>
=======

## uv

uv 安装更新
> pip install --upgrade uv

uv sync


## build

> uv build 

> uv publish
add-url 0.2.0
Assist adding links to your pyproject.toml
Requires Python: >=3.10

# Assist adding links to your pyproject.toml

```
❯ uvx add-url https://example.com
  documentation
  releasenotes
  changelog
  download
  source
> homepage
  8/8
```

# References

- <https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#writing-pyproject-toml-urls>
- <https://packaging.python.org/en/latest/specifications/well-known-project-urls/#well-known-labels>
minid-py 0.1.0
Jira-like IDs for links and short content.
Requires Python: >=3.13

# minid

[![tests](https://github.com/rfunduk/minid/actions/workflows/test.yml/badge.svg?branch=main)](https://github.com/rfunduk/minid/actions/workflows/test.yml)
[![codecov](https://codecov.io/github/rfunduk/minid/graph/badge.svg?token=7U8QOQBEXF)](https://codecov.io/github/rfunduk/minid)


Generate and store minimal, Jira-like IDs for content. Kind of like a URL shortener.

## Installation

```bash
uv tool install minid-py
pip install minid-py
```

## Usage

### Register a prefix
```bash
minid prefix SLACK
```

### Create a new entry
```bash
$ minid new slack "https://myorg.slack.com/archives/ABC123/pXYZ456"
Stored as SLACK-100

# Or use multiline input
$ minid new slack
Content (Ctrl+D or 2 blank lines to finish):
https://myorg.slack.com/archives/ABC123/pXYZ456

A very important thing involving Bob


Stored as SLACK-101
```

### Find entries
```bash
# list *all* entries
$ minid find

# all for prefix
$ minid find -p slack

# search all entries for content
$ minid find "Bob"

# search for content within a specific prefix
$ minid find "important" --prefix slack
```

### Get a specific entry
```bash
$ minid get slack 100

# fuzzy prefix matching
$ minid g sl 100
```

## Development

```bash
# install dependencies
uv sync

# run tests
./bin/test

# run tests in watch mode
./bin/test --watch

# install pre-commit hooks
uv run pre-commit install
```

## Features

- **Fuzzy command matching** - Type `minid f` instead of `minid find`
- **Fuzzy prefix match** - Likewise, `minid f -p s` will attempt to figure out what you meant by matching
- **Clickable links** - URLs and GitHub references (owner/repo#123) become clickable in terminal
- **Rich terminal output** - Tables, colors, and formatting
- **Multiline input** - Support for long descriptions
- **Fast search** - LMDB-based storage for quick queries
- **Comprehensive testing** - 100% test coverage
rstms-cloudflare 1.2.10
Top-level package for rstms-cloudflare.
Keywords: rstms_cloudflare

rstms-cloudflare
================


![Image](https://img.shields.io/github/license/rstms/rstms_cloudflare)

![Image](https://img.shields.io/pypi/v/rstms_cloudflare.svg)





Cloudflare DNS management CLI


* Free software: MIT license
* Documentation: https://rstms-cloudflare.readthedocs.io.



Credits
-------

This package was created with Cookiecutter and `rstms/cookiecutter-python-cli`, a fork of the `audreyr/cookiecutter-pypackage` project template.

[audreyr/cookiecutter](https://github.com/audreyr/cookiecutter)
[audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage)
[rstms/cookiecutter-python-cli](https://github.com/rstms/cookiecutter-python-cli)