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
License: MIT
Requires Python: >=3.9

# textblur_summary
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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:

```bash
pip install textblur_summary
```

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## 🔧 Usage

### Basic Usage (Default LLM: ChatLLM7)
```python
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:
```python
from langchain_openai import ChatOpenAI
from textblur_summary import textblur_summary

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

#### Using Anthropic:
```python
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:
```python
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:
  ```python
  response = textblur_summary(user_input, api_key="your_llm7_api_key")
  ```
- **Get a Free API Key**: Register at [LLM7 Token](https://token.llm7.io/).

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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](https://github.com/chigwell/textblur-summary/issues)

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## 👤 Author
- **Name**: Eugene Evstafev
- **Email**: [hi@euegne.plus](mailto:hi@euegne.plus)
- **GitHub**: [@chigwell](https://github.com/chigwell)

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