textblur-summary 2025.12.21130742

A new package that takes a text description of an image and returns a structured summary of the blurring tool's features and use cases. It processes user-provided text input about the tool, such as it

textblur_summary

A Python package that extracts and structures key features and use cases from text descriptions of blurring tools, providing a clean, formatted summary without sensitive or technical details.

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📌 Overview

textblur_summary processes user-provided text about a blurring tool (e.g., its purpose, benefits, and limitations) and returns a structured summary of its features. It highlights non-sensitive aspects like being free, instant, and watermark-free, while omitting technical or proprietary details.

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🚀 Installation

Install via pip:

pip install textblur_summary

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🔧 Usage

Basic Usage (Default LLM: ChatLLM7)

from textblur_summary import textblur_summary

# Example input: A user-provided description of a blurring tool
user_input = """
TextBlur is a free, instant image blurring tool. It allows users to blur faces or sensitive details in photos without watermarks.
"""

# Call the function (LLM7 API key is fetched from environment variable LLM7_API_KEY)
response = textblur_summary(user_input)
print(response)

Custom LLM Integration

You can pass your own LLM instance (e.g., OpenAI, Anthropic, or Google) for flexibility:

Using OpenAI:

from langchain_openai import ChatOpenAI
from textblur_summary import textblur_summary

llm = ChatOpenAI()
response = textblur_summary(user_input, llm=llm)
print(response)

Using Anthropic:

from langchain_anthropic import ChatAnthropic
from textblur_summary import textblur_summary

llm = ChatAnthropic()
response = textblur_summary(user_input, llm=llm)
print(response)

Using Google Generative AI:

from langchain_google_genai import ChatGoogleGenerativeAI
from textblur_summary import textblur_summary

llm = ChatGoogleGenerativeAI()
response = textblur_summary(user_input, llm=llm)
print(response)

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🔑 API Key Configuration

• Default LLM: Uses ChatLLM7 (from langchain_llm7) with the API key fetched from:
  • Environment variable: LLM7_API_KEY
  • Fallback: Hardcoded default (if no key is provided).
• Custom API Key: Pass it directly:

  response = textblur_summary(user_input, api_key="your_llm7_api_key")
  

• Get a Free API Key: Register at LLM7 Token.

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📊 Function Parameters

Parameter	Type	Description
user_input	str	The text description of the blurring tool to analyze.
api_key	Optional[str]	LLM7 API key (optional if using environment variable).
llm	Optional[BaseChatModel]	Custom LLM instance (e.g., ChatOpenAI, ChatAnthropic). Defaults to ChatLLM7.

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📝 Notes

• Rate Limits: The default LLM7 free tier is sufficient for most use cases.
• Output Format: Returns a list of structured key points (e.g., features, benefits).
• Safety: Avoid sharing sensitive or proprietary details in user_input.

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📢 Issues & Support

Report bugs or feature requests at: GitHub Issues

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👤 Author

• Name: Eugene Evstafev
• Email: [e-mail]
• GitHub: @chigwell

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