Pakistan Notice Helper：基于多模态模型的本地化诈骗识别助手

Core Overview

Pakistan Notice Helper is a localized AI safety assistant for Pakistani users, designed to identify suspicious notices, bills, SMS, and more via text or screenshots. It uses multimodal small models to provide risk ratings (4 levels: Looks normal/Verify first/Suspicious/Likely scam), detailed explanations, red flag alerts, safety suggestions, and polite reply drafts. Key principles: local-first (privacy protection), honest AI (no fallback to invented results), and focus on solving real-world scam identification issues.

Source: GitHub project by kingabzpro (https://github.com/kingabzpro/pakistan-notice-helper, released 2026-06-06).

Why This Project Exists

In Pakistan, users frequently receive confusing or suspicious messages (bank alerts, FBR notices, traffic fines,快递 info) mixed with scams. Traditional methods (rule-based/keyword filtering) fail to keep up with evolving scam tactics. This tool aims to fill the gap by using multimodal AI to help users analyze these messages, with a 'local-first' approach to protect privacy while providing reliable AI assistance.

Supported Inputs

• Text paste: Directly paste SMS, email, or notice content.
• Screenshot upload: Analyze images of suspicious info via multimodal models.

Key Outputs

1. Risk Rating: 4 levels (Looks normal/Verify first/Suspicious/Likely scam).
2. Explanations: English breakdown of why the risk rating was assigned.
3. Red Flags: Specific suspicious features (e.g., abnormal sender, urgent payment requests, suspicious links, grammar errors).
4. Safety Advice: Step-by-step guidance for handling the message.
5. Reply Drafts: Polite, safe templates to avoid sensitive info leaks.

System Architecture

• Frontend: Custom HTML/CSS/JS (mobile-first design, no default Gradio UI) for better mobile experience and brand control.
• Backend: Gradio Server (queue management, SSE support) using OpenAI SDK to communicate with model endpoints.
• Model: unsloth/Qwen3.6-27B-MTP-GGUF (multimodal, GGUF format for local deployment, MTP for efficient inference).

Configuration

Env vars like MODEL_BASE_URL (default: Modal-deployed Qwen3.6 endpoint), MODEL_NAME, MODEL_API_KEY control the model connection.

Core Design Decisions

• Pure Model-Driven: No rule-based fallbacks—if the model fails (e.g., missing credentials, invalid output), it shows clear errors instead of inventing results.
• Privacy Protection:
  • Local-first: Frontend resources are local (no CDN/analytics).
  • No persistent storage: Submitted data is only sent to the configured endpoint (no saving by the app).
  • Transparent data flow: Users are informed about data being sent to external endpoints.

Important Disclaimers

• No Official Verification: This tool only checks common scam signals—users must verify via official channels (e.g., PTA, FIA, banks) for final confirmation.
• Technical Limits: Depends on external model endpoints; local runs need extra config; image analysis requires visual-capable endpoints.

Official Report Channels

• PTA Complaint System: https://complaint.pta.gov.pk/
• FIA Complaint Portal: https://complaint.fia.gov.pk/
• SBP (State Bank of Pakistan): https://www.sbp.org.pk/
• FBR (Federal Board of Revenue): https://www.fbr.gov.pk/

Note: Never click links or call numbers directly from suspicious messages—always visit official sites independently.

Key Takeaways

• Vertical AI Application: Focuses on Pakistan-specific scam patterns and multilingual (Urdu/English) content, delivering more accurate results than general AI tools.
• Privacy-Convenience Balance: Local-first design and transparent data handling build user trust.
• Open-Source Reference: MIT-licensed, providing a template for similar regional AI safety tools.

This project prioritizes solving real user problems over cutting-edge tech, making it a practical example of ethical AI application in local contexts.