SFOSR / README.md
DanielSwift's picture
Initial SFOSR system with Gradio interface
2249e80

A newer version of the Gradio SDK is available: 5.33.0

Upgrade
metadata
title: SFOSR
emoji: 🏃
colorFrom: yellow
colorTo: gray
sdk: gradio
sdk_version: 5.24.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: SFOSR System

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

SFOSR: Система Формальной Оценки Смысла и Верификации

This project implements core components of the SFOSR theory, including semantic analysis, contract verification, and proof construction using both input data and a knowledge base.

Project Structure

  • sfosr_core/: Contains the main system logic (integrated_sfosr.py, sfosr_database.py).
  • tests/: Contains unit tests (test_*.py).
  • docs/: Contains documentation and theoretical papers related to SFOSR.
  • archive/: Contains archived materials (e.g., old databases).
  • sfosr.db: The main SQLite database containing concepts, vectors, rules, etc.
  • requirements.txt: Project dependencies.
  • README.md: This file.

Installation

(Currently, no external dependencies are required beyond standard Python libraries.)

# It's recommended to use a virtual environment
python -m venv venv
source venv/bin/activate # On Windows use `venv\\Scripts\\activate`

# Install dependencies (if any added later)
pip install -r requirements.txt

Running Tests

To run all tests, execute the following command from the project root directory:

python -m unittest discover tests -v

Current Capabilities

  • Analyzes SFOSR structures for syntactic validity.
  • Verifies vectors against database concepts and predefined contracts.
  • Constructs proofs based on input vectors, prioritizing them first.
  • Integrates knowledge from the sfosr.db database into the proof process if input vectors are insufficient.
  • Supports inference rules: chain_rule, causality_transfer, implication_causality_chain, part_of_transitivity.
  • Correctly handles cyclic dependencies in proof paths.

Known Limitations / Future Work

Запуск python integrated_sfosr.py демонстрирует обработку примера с построением доказательства и выводом оценок достоверности.

Вклад в проект

Приглашаем заинтересованных исследователей и разработчиков присоединиться к развитию SFOSR.

Лицензия

Проект SFOSR распространяется под лицензией MIT.