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CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

A Chinese AI-generated text (AIGC) detection web application built with Flask and BERT/RoBERTa model. Provides a reader-style interface for detecting whether text is AI-generated or human-written.

Running the Application

# Install dependencies
pip install -r requirements_web.txt

# Start the Flask server
python app.py

The server runs on http://localhost:5000

Key Files

  • app.py - Flask backend with REST API endpoints for text detection
  • templates/index.html - Frontend UI with theme system, chunk detection, and auto-save
  • requirements_web.txt - Python dependencies
  • Model files in root directory: pytorch_model.bin, config.json, tokenizer_config.json, vocab.txt, special_tokens_map.json

API Endpoints

Endpoint Method Description
/ GET Serve frontend
/api/detect POST Simple text detection
/api/detect-full POST Full detection with chunking
/api/detect-chunk POST Single chunk detection
/api/upload POST File upload (txt/docx/pdf)

Architecture

  • Backend: Flask + PyTorch + Transformers (BERT model)
  • Frontend: Vanilla HTML/CSS/JS with CSS custom properties for theming
  • Model: Chinese RoBERTa fine-tuned for binary classification (AI vs Human text)

Important Notes

  • Model is loaded globally at startup and uses MODEL_PATH = "." (current directory)
  • Detection returns probability that text is AI-generated (0 = human, 1 = AI)
  • Frontend uses localStorage for auto-save and theme persistence
  • Edit detection uses debounce (500ms) to prevent duplicate API calls

Gstack

Use the /browse skill from gstack for all web browsing. Never use mcp__claude-in-chrome__* tools.

Available skills: /office-hours, /plan-ceo-review, /plan-eng-review, /plan-design-review, /design-consultation, /review, /ship, /browse, /qa, /qa-only, /design-review, /setup-browser-cookies, /retro, /debug, /document-release