semvec/pypi-packages huggingface dataset:
bioscreen 0.1.0
A biosecurity screening library for AI protein design tools
License: MIT
Requires Python: >=3.9
Keywords: biosecurity, protein, screening, ai-safety

# bioscreen

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A biosecurity screening library for AI protein design tools. Provides simple APIs to flag potentially hazardous protein design requests before processing.

## ⚠️ Version 0.1.0 - Infrastructure Release

This is an infrastructure setup release with minimal functionality. The core screening logic will be implemented in future versions. This release establishes:

- ✅ Package structure and build system
- ✅ Type-safe API design
- ✅ Comprehensive test suite
- ✅ CI/CD pipeline
- ✅ Code quality tooling (Black, Ruff, mypy)

**Current behavior:** All sequences are flagged as `RiskLevel.GREEN` with no actual screening performed.

## Installation

```bash
pip install bioscreen
```

## Quick Start

```python
from bioscreen import ProteinScreener, RiskLevel

# Initialize the screener
screener = ProteinScreener()

# Screen a protein sequence
result = screener.screen_protein("MKTAYIAKQRQISFVKSHFSRQ")

print(f"Flagged: {result.flagged}")
print(f"Risk Level: {result.risk_level}")
print(f"Reason: {result.reason}")
# Output:
# Flagged: False
# Risk Level: RiskLevel.GREEN
# Reason: v0.1.0: No screening rules implemented yet
```
docsumm-ai 0.1.0
Audience-aware document summarizer for PDF/DOCX/TXT — optimized for context retention, not token count.
License: MIT
Requires Python: >=3.9
Keywords: AI, LLM, documents, docx, pdf, summarization

# docsumm-ai  
**One-line, opinionated document summarizer for PDFs, Word, or text — optimized for context retention, not token count.**

![CI](https://github.com/RohitRajdev/docsumm-ai/actions/workflows/ci.yml/badge.svg)
![License](https://img.shields.io/badge/License-MIT-blue.svg)
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![Version](https://img.shields.io/badge/version-0.1.0-orange)

---

## Why docsumm-ai?

Summarizing long documents shouldn’t mean losing meaning.  
Most tools today **truncate context** just to fit into token limits — resulting in shallow, inaccurate summaries.

`docsumm-ai` was built to fix that.

We designed it for **researchers, analysts, and AI developers** who care about both **fidelity and efficiency**.  
It automatically adapts to document structure, ensuring retention of key insights from text, Word, or PDFs — in a single line.

---

## What Makes It Different

✅ **One-line summarize()** — clean summaries with context retention  
✅ **Handles PDFs, DOCX, TXT** — no format left behind  
✅ **Context-aware chunking** — semantic segmentation, not blind splitting  
✅ **Adaptive compression** — keeps the right level of detail per section  
✅ **CLI + Python API** — works both in scripts and terminal  
✅ **Transparent JSON + Markdown output** — reproducible and human-readable  

---

## Installation

```bash
pip install docsumm-ai

## Quickstart
1. Summarize a text file
from docsumm_ai import summarize

summary = summarize("annual_report.txt", mode="concise")
print(summary)

2. Summarize a PDF (CLI)
docsumm summarize my_report.pdf --mode detailed --out summary.md

## Output Example

Input:

“The study explores the correlation between urban growth and environmental impact across 32 global cities…”

Output:

“Analyzes 32 cities showing urban expansion drives higher emissions; highlights need for adaptive policies.”

---

## License

MIT License © 2025 Rohit Rajdev
Open for community collaboration and research integration.

🌐 Links

🔗 GitHub: https://github.com/RohitRajdev/docsumm-ai

✉️ Contact: rohitrajdev.com

🧠 Related project: dataprep-ai