annotron

v0.1.1 safe
4.0
Medium Risk

Annotron — French document parsing (Factur-X, KBis, RIB, paie, CERFA). Official Python SDK.

🤖 AI Analysis

Final verdict: SAFE

The package is considered safe with low risks across most categories. While there is a moderate concern about metadata suggesting inactivity or lack of maintenance, this alone does not indicate malicious intent.

  • Network risk is moderate due to HTTP request usage.
  • Metadata suggests potential inactivity or lack of maintainer information.
Per-check LLM notes
  • Network: The network call pattern indicates the package uses HTTP requests for its functionality, which is common but should be reviewed for data sent over the network.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of potential inactivity and lack of maintainer information, raising some concerns.

📦 Package Quality Overall: Low (3.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://annotron.com/docs
  • Detailed PyPI description (1587 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 7 type-annotated function signatures (partial)
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • self._client = client or httpx.Client(timeout=timeout) self._own_client = client is None
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: annotron.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with annotron 
Create a mini-application called 'DocParser' using the Python package 'annotron'. This application will serve as a versatile tool for parsing various types of French documents such as Factur-X, KBis, RIB, payroll documents, and CERFA forms. The goal is to streamline the process of extracting meaningful information from these documents into structured data formats that can be easily processed or stored.

Step-by-Step Instructions:
1. **Setup Environment**: Start by setting up a virtual environment and installing the 'annotron' package alongside other necessary Python libraries like pandas for data manipulation and flask for creating a web interface.
2. **Document Upload Interface**: Develop a simple web interface using Flask where users can upload their French documents. Ensure the interface supports multiple file formats commonly used in France (e.g., PDF, XML).
3. **Parsing Engine**: Integrate 'annotron' to parse the uploaded documents. For each document type, utilize the corresponding functions provided by 'annotron' to extract relevant information. Store this extracted data in a structured format such as JSON or a database.
4. **Data Visualization**: Implement basic data visualization features within the web app to display key extracted information in a user-friendly manner. Use libraries such as matplotlib or seaborn for visualizations.
5. **Export Options**: Allow users to export the parsed data into different formats such as CSV or Excel for further analysis or record-keeping.
6. **Error Handling & Feedback**: Implement robust error handling mechanisms to manage cases where the document might not be correctly formatted or if there are issues during the parsing process. Provide clear feedback messages to the user.
7. **Security Measures**: Since the application will handle sensitive financial and personal data, ensure all data transmission and storage processes are secure. Consider using HTTPS for web traffic and encrypting stored data.

Suggested Features:
- Support for batch processing of documents.
- Ability to save parsing templates for frequent use.
- Integration with cloud storage services for document management.
- Detailed logs of parsing activities for auditing purposes.
- User authentication and role-based access control to restrict access based on user roles.

How 'annotron' is Utilized:
- Utilize 'annotron' to leverage its specialized functions for parsing different types of French documents. Each document type has specific parsing requirements which 'annotron' simplifies through its pre-built functionalities. For instance, use 'annotron' to extract payroll details, company registration information from KBis documents, banking details from RIB, invoice details from Factur-X, etc. This package significantly reduces the complexity involved in manually writing parsers for each document type.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!