semvec/pypi-packages huggingface dataset:
impact-parser 2025.12.21202313
impact-parser processes text to extract structured data on service disruptions, including affected areas, impact scope, causes, and restoration times.
License: MIT
Requires Python: >=3.9

# impact_parser
[![PyPI version](https://badge.fury.io/py/impact-parser.svg)](https://badge.fury.io/py/impact-parser)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/impact-parser)](https://pepy.tech/project/impact-parser)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)

A Python package for parsing text input to extract and structure information about service disruptions and restorations.

## Overview
This package takes text input describing incidents like power outages, internet disruptions, or transportation delays, and returns a structured output with key details such as the affected area, number of impacted users, cause of the disruption, and estimated restoration time.

## Installation
```bash
pip install impact_parser
```

## Usage
```python
from impact_parser import impact_parser

response = impact_parser(
    user_input="The power outage occurred in the downtown area due to a storm, affecting 1000 homes and businesses. Estimated restoration time is 2 hours.",
    api_key=None,
    llm=None,
)
print(response)
```

## Parameters
* `user_input`: The text input to process
* `llm`: The Langchain LLM instance to use (optional, defaults to `ChatLLM7` with no API key)
* `api_key`: The API key for LLM7 (optional, defaults to `None`)

## LLM Options
You can safely pass your own LLM instance (based on https://docs.langchain.io/) if you want to use another LLM.

Examples:
```python
from langchain_openai import ChatOpenAI
from impact_parser import impact_parser
llm = ChatOpenAI()
response = impact_parser(user_input, llm=llm)

from langchain_anthropic import ChatAnthropic
from impact_parser import impact_parser
llm = ChatAnthropic()
response = impact_parser(user_input, llm=llm)

from langchain_google_genai import ChatGoogleGenerativeAI
from impact_parser import impact_parser
llm = ChatGoogleGenerativeAI()
response = impact_parser(user_input, llm=llm)
```

## LLM7 Rate Limits
The default rate limits for LLM7 free tier are sufficient for most use cases of this package. If you need higher rate limits for LLM7, you can pass your own API key via environment variable `LLM7_API_KEY` or via passing it directly like `impact_parser(user_input, api_key="your_api_key")`.

To get a free API key, register at https://token.llm7.io/

## Issues
Refer to the GitHub issues page: https://github.com/chigwell/impact-parser/issues

## Author
Eugene Evstafev
<eugene@eugene.plus>
heritage-insight 2025.12.21201637
Generates structured summaries, timelines, and thematic insights from historical or cultural texts using pattern matching and language models.
License: MIT
Requires Python: >=3.9

# Heritage Insight Package
[![PyPI version](https://badge.fury.io/py/heritage-insight.svg)](https://badge.fury.io/py/heritage-insight)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/heritage-insight)](https://pepy.tech/project/heritage-insight)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)

leveraging pattern matching with language models to generate structured summaries and insights from user-provided historical or cultural texts.

## Overview
A new package that accepts textual inputs related to specific topics or events and returns organized, key information such as summaries, timelines, or thematic breakdowns.

## Installation
```bash
pip install heritage_insight
```

## Usage
```python
from heritage_insight import heritage_insight

user_input = "Input text here"
response = heritage_insight(user_input, verbose=False)
```

## Example usage with specific LLMs
```python
from heritage_insight import heritage_insight
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_google_genai import ChatGoogleGenerativeAI

openai_response = heritage_insight(user_input, llm=ChatOpenAI())
anthropic_response = heritage_insight(user_input, llm=ChatAnthropic())
google_response = heritage_insight(user_input, llm=ChatGoogleGenerativeAI())
```

## Configuration
- `user_input`: str: the user input text to process
- `llm`: Optional[BaseChatModel]: the langchain llm instance to use, defaults to ChatLLM7
- `api_key`: Optional[str]: the api key for llm7, defaults to LLM7_API_KEY environment variable or LLM7 free tier limits

## Using custom LLMs
You can safely pass your own llm instance (based on https://docs.langchain.dev/) if you want to use another LLM, via passing it like `heritage_insight(user_input, llm=your_llm_instance)`.

## LLM7 API Key
The default rate limits for LLM7 free tier are sufficient for most use cases of this package. If you want higher rate limits for LLM7, you can pass your own `api_key` via environment variable `LLM7_API_KEY` or via passing it directly like `heritage_insight(user_input, api_key="your_api_key")`. You can get a free api key by registering at https://token.llm7.io/

## Support and Issues
Report issues and provide feedback at https://github.com/chigwell/heritage-insight

## Author
Eugene Evstafev (hi@eugene.plus)
zx2xberry 2025.12.21201323
zx2xberry translates ZX Spectrum+ keyboard keypresses into commands for XBerry Pi devices.
License: MIT
Requires Python: >=3.9

# zx2xberry
[![PyPI version](https://badge.fury.io/py/zx2xberry.svg)](https://badge.fury.io/py/zx2xberry)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/zx2xberry)](https://pepy.tech/project/zx2xberry)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)


A Python package that enables users to control their XBerry Pi devices using a ZX Spectrum+ keyboard by translating keypresses into structured commands.

## Features

- Translates ZX Spectrum+ keyboard keypresses into structured commands for XBerry Pi devices
- Uses LLM7 by default (via `langchain_llm7`)
- Supports custom LLM instances from LangChain
- Free tier of LLM7 has sufficient rate limits for most use cases

## Installation

```bash
pip install zx2xberry
```

## Usage

### Basic Usage

```python
from zx2xberry import zx2xberry

response = zx2xberry("user input describing keypresses")
```

### Using a Custom LLM

You can use any LLM compatible with LangChain. Here are examples with different LLMs:

#### OpenAI

```python
from langchain_openai import ChatOpenAI
from zx2xberry import zx2xberry

llm = ChatOpenAI()
response = zx2xberry("user input", llm=llm)
```

#### Anthropic

```python
from langchain_anthropic import ChatAnthropic
from zx2xberry import zx2xberry

llm = ChatAnthropic()
response = zx2xberry("user input", llm=llm)
```

#### Google

```python
from langchain_google_genai import ChatGoogleGenerativeAI
from zx2xberry import zx2xberry

llm = ChatGoogleGenerativeAI()
response = zx2xberry("user input", llm=llm)
```

### Using a Custom API Key

You can provide your own API key for LLM7 either via environment variable or directly:

```python
from zx2xberry import zx2xberry

# Via environment variable
import os
os.environ["LLM7_API_KEY"] = "your_api_key"
response = zx2xberry("user input")

# Directly
response = zx2xberry("user input", api_key="your_api_key")
```

## Parameters

- `user_input` (str): The user input text to process
- `llm` (Optional[BaseChatModel]): The LangChain LLM instance to use. Defaults to `ChatLLM7`.
- `api_key` (Optional[str]): The API key for LLM7. If not provided, it will use the environment variable `LLM7_API_KEY` or the default LLM7 key.

## Returns

A list of structured commands that the XBerry Pi can interpret and execute.

## Getting an LLM7 API Key

You can get a free API key by registering at [LLM7](https://token.llm7.io/).

## Issues

If you encounter any issues, please report them on the [GitHub issues page](https://github.com/chigwell/zx2xberry/issues).

## Author

**Eugene Evstafev**

- Email: hi@eugene.plus
- GitHub: [chigwell](https://github.com/chigwell)
csrf-sentry 2025.12.21200926
Helps developers implement tailored CSRF protection strategies without tokens, ensuring robust security and easy integration.
License: MIT
Requires Python: >=3.9

# csrf-sentry
[![PyPI version](https://badge.fury.io/py/csrf-sentry.svg)](https://badge.fury.io/py/csrf-sentry)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/csrf-sentry)](https://pepy.tech/project/csrf-sentry)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)


A Python package that helps developers implement CSRF (Cross-Site Request Forgery) protection without relying on traditional tokens or hidden form fields. The package takes a user's input describing their web application's structure and security requirements, and returns a structured response with a tailored CSRF protection strategy.

## Installation

```bash
pip install csrf_sentry
```

## Usage

### Basic Usage

```python
from csrf_sentry import csrf_sentry

# Basic usage with default LLM
response = csrf_sentry("Describe your web application structure and security requirements")
print(response)
```

### Using Custom LLM

You can use any Langchain-compliant LLM with csrf_sentry:

#### OpenAI

```python
from langchain_openai import ChatOpenAI
from csrf_sentry import csrf_sentry

llm = ChatOpenAI()
response = csrf_sentry(user_input, llm=llm)
```

#### Anthropic

```python
from langchain_anthropic import ChatAnthropic
from csrf_sentry import csrf_sentry

llm = ChatAnthropic()
response = csrf_sentry(user_input, llm=llm)
```

#### Google

```python
from langchain_google_genai import ChatGoogleGenerativeAI
from csrf_sentry import csrf_sentry

llm = ChatGoogleGenerativeAI()
response = csrf_sentry(user_input, llm=llm)
```

### Using Custom API Key

If you need to use your own LLM7 API key:

```python
from csrf_sentry import csrf_sentry

# Pass API key directly
response = csrf_sentry(user_input, api_key="your_api_key_here")

# Or set environment variable
import os
os.environ["LLM7_API_KEY"] = "your_api_key_here"
response = csrf_sentry(user_input)
```

## Parameters

- `user_input` (str): The user input text describing your web application structure and security requirements
- `llm` (Optional[BaseChatModel]): A Langchain LLM instance. If not provided, defaults to ChatLLM7
- `api_key` (Optional[str]): API key for LLM7. If not provided, uses the environment variable LLM7_API_KEY or the default free tier

## Getting an API Key

You can get a free API key for LLM7 by registering at https://token.llm7.io/

## Default LLM

The package uses ChatLLM7 from langchain_llm7 by default. The free tier rate limits are sufficient for most use cases of this package. If you need higher rate limits, you can provide your own API key.

## Support

For issues and questions, please visit our [GitHub issues page](https://github...)

## Author

- **Eugene Evstafev** - hi@euegne.plus
- GitHub: [chigwell](https://github.com/chigwell)