README.md

metadata

title: ThtratLandscapeChat
emoji: 💬
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 6.0.0
app_file: app.py
pinned: false
hf_oauth: true
hf_oauth_scopes:
  - inference-api
license: mit
datasets:
  - mrmoor/cyber-threat-intelligence
  - romaingrx/red-teamer-mistral-
  - SummerSigh/Muti-Class-Redteaming

APJ Threat Intelligence System

Mobile-First, Multilingual Cybercrime Intelligence Platform

Overview

This project delivers a full-stack threat-intelligence console focused on Asia-Pacific & Japan (APJ) cybercrime ecosystems. The system ingests Mandarin/Cantonese underground-market chatter, interprets idioms and cultural nuances, classifies threats using transformer models, and presents insights to analysts through a mobile-first Gradio interface.

The design is modular, allowing you to use:

• Local Transformers models (HuggingFace)
• External LLM APIs
• Custom datasets
• A growing slang / idiom lexicon
• Marketplace monitoring pipelines

This repository is optimized for GitHub and HuggingFace Spaces.

---

✨ Features

🔍 Intelligence Layer

• Threat classification (Transformers)
• Vendor graph modeling
• Marketplace and trend analysis
• Slang & idiom identification

🌏 Multilingual Processing

• Mandarin + Cantonese dialect detection
• Literal + functional translation
• Cultural interpretation for cybercrime slang

📱 Mobile-First UI

Built with Gradio 4.x:

• Single-column layout
• Mode switcher (Threat Intel / Translation / Marketplace Watch / Analyst Tools)
• File upload for logs, screenshots, raw text
• Downloadable chat transcripts
• Clean UX optimized for mobile operators

🔧 Built With

• transformers
• datasets
• gradio
• Python 3.10+

---

🧩 Repository Structure

. ├── app.py # Main Gradio app ├── prompt_engine.py # Centralized prompt construction ├── model_inference.py # Transformers-based inference wrapper ├── datasets_loader.py # HuggingFace datasets loader utilities ├── slang_lexicon.json # Evolving idiom/slang dictionary ├── PROJECT_SPEC.md # Architectural overview ├── HUGGINGFACE.md # HF Spaces deployment instructions ├── GITHUB_SETUP.md # Repo usage & development guide ├── requirements.txt └── README.md

---

🚀 Running Locally

1. Clone the repo

git clone https://github.com/<yourname>/apj-threat-intel
cd apj-threat-intel

2. Install requirements

pip install -r requirements.txt

3. Launch the app

python app.py

The interface opens automatically in your browser.

⸻

🧠 Model Integration

You can plug your own HuggingFace model into model_inference.py:

ThreatModel(model_path="your-model-name")

Or use an API-driven LLM via prompt_engine.py.

⸻

🌐 Deploy on HuggingFace Spaces

See HUGGINGFACE.md in this repo for step-by-step instructions.

⸻

📄 Docs
    •	Project Spec
    •	HF Spaces Setup
    •	GitHub Setup Guide

⸻

📜 License

MIT (You may swap this with your preferred license.)

⸻

🤝 Contributing

Pull requests and issue reports are welcome.

⸻

🧭 Roadmap
    •	Vendor identity resolution model
    •	Marketplace scraping connectors
    •	Cantonese pretrained language model fine-tuning
    •	In-browser graph explorer
    •	Real-time alerting engine

⸻

✉️ Contact

For questions, enhancements, or collaboration, open an issue.

---