NoticeCheck

This repository is the local version of the Pakistan Notice Helper Hugging Face Space. It keeps the same notice-checking purpose with a redesigned English interface. The hosted demo uses Hugging Face ZeroGPU, while Docker Compose runs the models on a local NVIDIA GPU.

Try the live demo

Project Links

• Live Hugging Face Space
• GitHub repository
• LinkedIn project post
• Demo GIF
• Field notes: making NoticeCheck fully local
• Privacy-safe trace dataset

NoticeCheck is a safety assistant for suspicious Pakistani messages, bills, bank alerts, challans, courier notices, and screenshots. It returns:

• a risk label
• a short explanation based on visible evidence
• warning signs and safer next actions
• a brief reply draft when replying is appropriate

NoticeCheck does not verify the sender and does not provide legal or financial advice. Find official contact details independently before paying, clicking, replying, or sharing personal information.

Runtime

Text or screenshot
        |
        v
Custom Gradio Server frontend
        |
        +--> Nemotron-Parse v1.2 for screenshot text
        |
        v
MiniCPM5-1B through Transformers on ZeroGPU
        |
        v
Structured risk assessment

• Reasoning: openbmb/MiniCPM5-1B through Transformers
• OCR: nvidia/NVIDIA-Nemotron-Parse-v1.2 through Transformers
• Compute: Hugging Face Spaces ZeroGPU
• Interface: redesigned custom HTML, CSS, and JavaScript
• Language: English only

The application does not use a remote model API and has no heuristic assessment fallback. Model and OCR failures are returned explicitly.

Both models run through Transformers on the Hugging Face ZeroGPU deployment.

Run Locally With Docker and CUDA

The included Compose setup runs the same Transformers pipeline entirely on a local NVIDIA GPU. It does not use ZeroGPU or a remote inference API.

Prerequisites:

• Docker Engine with Docker Compose 2.30 or newer
• a supported NVIDIA GPU and current NVIDIA driver
• NVIDIA Container Toolkit configured for Docker Engine on Linux
• Git LFS for downloading the interface images
• enough GPU memory for MiniCPM5-1B and Nemotron-Parse v1.2

Clone the GitHub repository and download its Git LFS assets:

git clone https://github.com/kingabzpro/local-notice-check.git
cd local-notice-check

git lfs install
git lfs pull

Before building, confirm that NVIDIA Container Toolkit exposes CUDA and the GPU inside Docker:

docker run --rm --gpus all \
  pytorch/pytorch:2.9.1-cuda12.8-cudnn9-runtime \
  python -c "import torch; print(torch.cuda.is_available(), torch.cuda.get_device_name(0))"

The command must print True and the NVIDIA GPU name. If it fails, configure NVIDIA Container Toolkit before starting the application.

Build and start NoticeCheck:

docker compose up --build

Open http://localhost:7860. The first startup takes longer because both model repositories are downloaded. Downloads are retained in the huggingface-cache Docker volume.

Optional environment overrides can be placed in a local .env file:

NOTICECHECK_PORT=7860
TRANSFORMERS_MODEL_REPO=openbmb/MiniCPM5-1B
MODEL_ENABLE_THINKING=0
HF_TOKEN=

Stop the application without deleting downloaded models:

docker compose down

To also remove the model cache and trace volumes:

docker compose down --volumes

Repository Layout

app.py                 Thin Space launcher
Dockerfile             Local CUDA image
compose.yaml           Local NVIDIA GPU deployment
app/
  cli.py               CLI and startup
  config.py            Environment configuration
  model_endpoint.py    Space Transformers inference
  ocr.py               Nemotron-Parse adapter
  server.py            Gradio/FastAPI routes
  service.py           Assessment orchestration
  trace.py             Trace subsystem adapter
static/                Custom frontend
traces/                Privacy-safe trace runtime and tools
experiments/           Preserved historical model experiments
tests/                 Application and trace tests

English-Only Interface

This version intentionally uses an English-only interface and requests English analysis from the model. Most notices and scam messages targeted by the project contain English or English mixed with common local terms. The local model also understands the task instructions and produces structured English results more reliably than Urdu output.

Screenshot OCR may detect text from other languages, but the generated assessment is intended to be in English. Urdu-language output is not currently supported.

Privacy-Safe Traces

Trace publishing is optional. Published records contain minimized, deterministic metadata and exclude raw message text, OCR text, screenshots, links, identifiers, and complete model responses.

python -m traces.scripts.analyze_trace_dataset

See the trace dataset card for the schema and privacy rules.

Safety

• Redact CNIC numbers, account details, OTPs, PINs, and card information.
• Never trust a phone number or link solely because it appears in a message.
• Confirm through an official website, app, card, statement, or helpline you locate independently.
• Treat the output as decision support, not proof that a message is genuine.