Summary

This project offers a high-performance, asynchronous Python module for querying ICP (Internet Content Provider) filing information for domains, apps, mini-programs, quick apps, and enterprises. It directly scrapes real-time data from China's MIIT government platform, providing a fully localized API and Web UI. This enables efficient, automated retrieval of regulatory data for digital services operating in China, targeting developers and businesses needing to verify or obtain ICP compliance details.

How It Works

ICP_Query utilizes an asynchronous Python architecture to scrape data directly from China's MIIT government service platform. It supports diverse queries (name, filing number, enterprise) and identifies non-compliant services. Advanced captcha recognition, including slider challenges, and model acceleration enhance processing speed. The design prioritizes performance and integration via a localized API.

Quick Start & Requirements

Deployment options include Docker, pre-compiled executables, or source (Python/Rust). Python source requires Python 3.11+ and the uv package manager. Rust source requires its toolchain. The Web UI is accessible at http://127.0.0.1:16181 by default. Links to Releases and documentation are implied.

Highlighted Details

• Real-time MIIT scraping for domains, apps, mini-programs, quick apps, and enterprises.
• Specific query types for "illegal/non-compliant" services (bweb, bapp, etc.).
• Performance claims cite an AMD 9600X averaging 0.09s per recognition with model acceleration support.
• Features a built-in Web UI, Python SDK, and MCP integration for tools like Claude Desktop.

Maintenance & Community

Community support is available via QQ group 1029212047. The README provides no details on core maintainers, sponsorships, or a roadmap.

Licensing & Compatibility

The README states the open-source purpose is "only for learning and exchange of reverse engineering and captcha recognition technology use." No formal open-source license (e.g., MIT, Apache) is specified, creating ambiguity for commercial use and redistribution.

Limitations & Caveats

The stated "open source purpose" may restrict usage to non-commercial, learning scenarios. The lack of a formal license hinders adoption, particularly in commercial contexts. Reliance on web scraping makes the project vulnerable to changes or blocking by the MIIT.