https://github.com/chigwell/textblur-summary
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
https://github.com/chigwell/textblur-summary
automated-reporting benefit-highlighting digital-image-editing feature-extraction formatted-output free-tool image-blurring information-summarization instant-processing limitation-identification natural-language-processing nontechnical structured-summary text-analysis use-case-description user-input-parsing userfriendly watermarkfree
Last synced: about 1 month ago
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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
- Host: GitHub
- URL: https://github.com/chigwell/textblur-summary
- Owner: chigwell
- Created: 2025-12-21T13:07:41.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-12-21T13:07:52.000Z (about 2 months ago)
- Last Synced: 2025-12-23T04:42:33.430Z (about 2 months ago)
- Topics: automated-reporting, benefit-highlighting, digital-image-editing, feature-extraction, formatted-output, free-tool, image-blurring, information-summarization, instant-processing, limitation-identification, natural-language-processing, nontechnical, structured-summary, text-analysis, use-case-description, user-input-parsing, userfriendly, watermarkfree
- Language: Python
- Homepage: https://pypi.org/project/textblur-summary/
- Size: 2.93 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# textblur_summary
[](https://badge.fury.io/py/textblur-summary)
[](https://opensource.org/licenses/MIT)
[](https://pepy.tech/project/textblur-summary)
[](https://www.linkedin.com/in/eugene-evstafev-716669181/)
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.
---
## 📌 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.
---
## 🚀 Installation
Install via pip:
```bash
pip install textblur_summary
```
---
## 🔧 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)
```
---
## 🔑 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/).
---
## 📊 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`. |
---
## 📝 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`.
---
## 📢 Issues & Support
Report bugs or feature requests at:
[GitHub Issues](https://github.com/chigwell/textblur-summary/issues)
---
## 👤 Author
- **Name**: Eugene Evstafev
- **Email**: [hi@euegne.plus](mailto:hi@euegne.plus)
- **GitHub**: [@chigwell](https://github.com/chigwell)
---