LuminaWeaver
Clean, Watermark-Free Descriptions from User Input
LuminaWeaver is a Python package that processes user-submitted text descriptions of images or videos containing watermarks and returns structured, watermark-free descriptions. It uses a language model to reinterpret the content while removing any watermark references, ensuring the output is clean and ready for further applications like content creation or archival.
📦 Installation
Install the package via pip:
pip install luminaweaver
🚀 Usage
Basic Usage (Default LLM7)
from luminaweaver import luminaweaver
user_input = "This image shows a sunset over the mountains with a watermark in the bottom right corner."
response = luminaweaver(user_input)
print(response)
Custom LLM Integration
You can replace the default LLM (ChatLLM7) with any LangChain-compatible model. Below are examples using popular LLMs:
OpenAI
from langchain_openai import ChatOpenAI
from luminaweaver import luminaweaver
llm = ChatOpenAI()
response = luminaweaver(user_input, llm=llm)
Anthropic
from langchain_anthropic import ChatAnthropic
from luminaweaver import luminaweaver
llm = ChatAnthropic()
response = luminaweaver(user_input, llm=llm)
Google Generative AI
from langchain_google_genai import ChatGoogleGenerativeAI
from luminaweaver import luminaweaver
llm = ChatGoogleGenerativeAI()
response = luminaweaver(user_input, llm=llm)
🔧 Parameters
| Parameter | Type | Description |
|---|---|---|
user_input |
str |
The text description containing watermarks to process. |
api_key |
Optional[str] |
Your LLM7 API key (if not using default). |
llm |
Optional[BaseChatModel] |
Custom LangChain LLM instance (optional). |
🔑 API Key & Rate Limits
- Default LLM: Uses
ChatLLM7fromlangchain_llm7(PyPI). - Free Tier: Sufficient for most use cases.
- Custom API Key: Pass via
api_keyparameter or environment variableLLM7_API_KEY. - Get a Free Key: Register at LLM7 Token.
📝 How It Works
- Takes a user-provided text description (e.g., "Image of a cat with a watermark").
- Uses an LLM to reinterpret the description while removing watermark references.
- Returns a clean, structured output (e.g., "Image of a cat").
📦 Dependencies
langchain-corelangchain-llm7(default)re(built-in)
🔧 Development
- GitHub Issues: Report here
- Author: Eugene Evstafev (@chigwell)
- Email: hi@euegne.plus
📜 License
MIT License (see LICENSE).
Metadata
Release files for luminaweaver 2025.12.21140209
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| luminaweaver-2025.12.21140209.tar.gz | 4.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| luminaweaver-2025.12.21140209-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 9.3 kB
Release files / luminaweaver-2025.12.21140209.tar.gz
| Download URL | luminaweaver-2025.12.21140209.tar.gz |
|---|---|
| Size | 4.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
114dbfe0230e68eeb3993ddef67b74329b3dabccec8492968eb68c83c2be79c6
|
|
BLAKE2b-256 checksum How to use checksums |
8afe7d7b6270aff04dd4aca40050acc02f69d2eea2c8cd4b2e4562b90fb47871
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.1
|
Release files / luminaweaver-2025.12.21140209-py3-none-any.whl
| Download URL | luminaweaver-2025.12.21140209-py3-none-any.whl |
|---|---|
| Size | 4.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
c5b3dda8d461623c64bbf000f8c7cd07af32c4d2ef535a8256fc3d2ec1ebebc7
|
|
BLAKE2b-256 checksum How to use checksums |
bee91322ba3af1680e21f157900bc78415be7eb515a81b32d3219cf8fc98628c
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.1
|