semvec/pypi-packages huggingface dataset:
fraudet 0.0.4
Fraud probability prediction using CatBoost with isotonic calibration
License: MIT
Requires Python: >=3.9

# Fraud Scoring (CatBoost + Isotonic Calibration)

A tiny Python package that predicts a calibrated fraud probability for a single car claim.

## Install
```bash
!pip install fraudet

from fraudet import predict_fraud

pred = predict_fraud(
    AccidentArea="Urban",
    DayOfWeekClaimed="Monday",
    MonthClaimed="Jan",
    Sex="Female",
    MaritalStatus="Single",
    Fault="Third Party",
    VehicleCategory="Sedan",
    VehiclePrice="20000 to 29000",
    DaysPolicyAccident="more than 30",
    DaysPolicyClaim="more than 30",
    PastNumberOfClaims=0,
    AgeOfVehicle="3 years",
    AgeOfPolicyHolder="26 to 30",
    PoliceReportFiled="No",
    WitnessPresent="No",
    AgentType="External",
    NumberOfSuppliments="1 to 2",
    AddressChangeClaim="no change",
    NumberOfCars="1 vehicle",
    BasePolicy="Liability",
)


print(pred.proba_fraud)
textforgepy 2025.12.21090031
A new package designed to transform unstructured text inputs into standardized, machine-readable outputs. This tool focuses on domains where consistency and format are critical, such as technical docu
License: MIT
Requires Python: >=3.9

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


Transform unstructured text inputs into standardized, machine-readable outputs using natural language processing (NLP) and reinforcement learning.

## Overview

TextForgePy is a Python package designed to convert free-form text inputs into structured data, perfect for domains where consistency and formatting are crucial. By leveraging LLM7, it reduces ambiguity and enhances reliability.

## Installation

```bash
pip install textforgepy
```

## Example Usage

```python
from textforgepy import textforgepy

response = textforgepy(user_input="Your user input text here")
print(response)  # response is a list of processed strings
```

## Input Parameters

* `user_input`: The user input text to process (string)
* `llm`: The langchain LLM instance to use; defaults to `ChatLLM7` from `langchain_llm7` (optional)
* `api_key`: The API key for LLM7; if not provided, uses the `LLM7_API_KEY` environment variable or defaults to "None" (optional)

Note: You can safely pass your own LLM instance by using a different langchain library, e.g.:

```python
from langchain_openai import ChatOpenAI
from textforgepy import textforgepy

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

or:

```python
from langchain_anthropic import ChatAnthropic
from textforgepy import textforgepy

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

or even:

```python
from langchain_google_genai import ChatGoogleGenerativeAI
from textforgepy import textforgepy

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

## Rate Limits

The default rate limits for LLM7's free tier are sufficient for most use cases of TextForgePy. If you need higher rate limits, you can pass your own API key via environment variable `LLM7_API_KEY` or directly like `textforgepy(user_input, api_key="your_api_key")`.

Get a free API key at https://token.llm7.io/

## Contributing

Please report issues at https://github.com/chigwell/textforgepy

Author: Eugene Evstafev
Email: hi@euegne.plus
GitHub: https://github.com/chigwell

## License

...
promptxform 2025.12.21085416
A new package is designed to simplify user interactions by accepting free-text prompts and providing structured, reliable responses. It leverages an underlying pattern-matching system to interpret use
License: MIT
Requires Python: >=3.9

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

A simple and efficient way to process user input text with ChatLLM7

**Overview**

promptxform is a Python package designed to simplify user interactions by accepting free-text prompts and providing structured, reliable responses. It leverages an underlying pattern-matching system to interpret user inputs and generate consistent outputs, enabling seamless information extraction or task execution without complex processing of media types.

**Installation**
```bash
pip install promptxform
```
**Example usage**
```python
from promptxform import promptxform

response = promptxform(user_input="my text here")
```
**Input parameters**

* `user_input`: str - the user input text to process
* `llm`: Optional[BaseChatModel] - the langchain llm instance to use, if not provided the default ChatLLM7 will be used.
* `api_key`: Optional[str] - the api key for llm7, if not provided it will use the default ChatLLM7 from langchain_llm7.

**Using your own LLM instance**

You can safely pass your own llm instance (based on [langchain documentation](https://docs.langchain.readthedocs.io/en/latest/)) if you want to use another LLM, via passing it like `promptxform(user_input, llm=your_llm_instance)`.

Here are some examples:

```python
from langchain_openai import ChatOpenAI
from promptxform import promptxform
llm = ChatOpenAI()
response = promptxform(user_input, llm=llm)

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

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

**Rate limits**

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 api key via environment variable LLM7_API_KEY or via passing it directly like `promptxform(user_input, api_key="your_api_key")`. You can get a free api key by registering at https://token.llm7.io/

**Support and issues**

Please report any issues you encounter at [github issues page](https://github.com/chigwell/promptxform/issues)

**Author**

Eugene Evstafev (eugene@eugene.plus)
Alenaova Systems