Q-Impact

Q-Impact

Chats about how Qatari laws and economic indicators influence one another, and what a change to a law could do for growth and the NDS3 2030 targets, for policy analysts.

Made by malmarri on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.

About

What it is: Q-Impact is a policy research app made for Qatar's Ministry of Commerce and Industry. It links Qatari laws to economic indicators and shows what a change to a law could mean for growth and the NDS3 2030 targets. The main page is a chat. A second view maps each policy area to its related indicators and the laws behind them.

Who it suits: Analysts and researchers who work on Qatari law, economic policy and the 2030 targets.

Industry | Who uses it | How they use it | Example
Government | Policy analysts | Ask how a change to a law would affect the economy | Checking a proposed change against growth targets
Economics | Economists | Look up which indicators relate to a policy area | Finding the indicators behind a trade law
Legal research | Law researchers | Find a law from a plain English question | Reaching an Arabic law that uses different wording

If you do not work with Qatari law or policy, you can skip it.

Where to use it:

• A web browser, on the Space's page at Hugging Face
• Python on Windows, macOS or Linux, for a copy you run yourself

Use case | Who | What they get back
Policy question | Analysts | An answer with the laws, indicators and 2030 targets it touches
Scenario check | Economists | Projected GDP, labour productivity and net FDI figures under an assumed change
Indicator lookup | Researchers | The indicators for each policy area and the laws behind them

How to set it up: Online, nothing needs installing. For a copy on your own computer you need a few things first.

• Python
• The pip tool
• The packages listed in requirements.txt

1. Install the Hugging Face hub package.
2. Download the Space's files into a folder.
3. Move into that folder.
4. Install Gradio.
5. Install the packages from requirements.txt.
6. Start app.py.

pip install -U huggingface_hub
hf download malmarri/team-qh-167-qatarsignals-app --repo-type space --local-dir team-qh-167-qatarsignals-app
cd team-qh-167-qatarsignals-app
pip install gradio
pip install -r requirements.txt
python app.py

For its answers, the chat sends requests to Hugging Face Inference Providers, which serves an open model. The source does not say what a local run needs for that.

How to use it: Open the Space's page, or start app.py from your own copy, and ask a question about a Qatari policy or a change to one in the chat. It reads the recent turns and answers in the shape your question needs. Next to the chat, a chart plots the scenario's GDP growth against the NDS3 target of 4%, with the main related indicator.

python app.py

Tool | What it does
AI policy chat | Answers questions about a policy or a change to it, across several turns
Law search | Finds the law in Al Meezan by keywords, phrases and meaning, and names its instrument type
Indicator view | Shows the indicators related to the policy and their recent trend
Scenario model | Applies a Solow growth model to GDP, productivity and net FDI up to 2030
NDS3 targets | Says how a change moves the 2030 targets it affects, with page references
Policy-indicator map | Lists each policy area's related indicators and the laws behind it

Example: You ask the chat how more foreign ownership across sectors would affect Qatar's growth. It finds the relevant law and its type, then shows related indicators and their trend. It runs a scenario from a change size you set, says how the 2030 targets move, and adds a confidence score and sources.

For newcomers and experts: NDS3 is Qatar's plan with targets for 2030, and the app ties laws to those targets. To start, ask one plain English question about a policy and see which laws and indicators come back.

Search is hybrid: keywords for exact terms and named laws, plus semantic search with the intfloat/multilingual-e5-small embedding model, so an English question can reach an Arabic law. Every number in an answer is checked against computed evidence before it is shown. The confidence score and sources are added by Python code, and a policy's effect in a scenario is an assumption you set, never a forecast.

Ask a policy question in the chat
Find laws by keyword and by meaning
Match indicators and run the scenario
Check numbers, then add confidence and sources

Good to know:

• Limits: a policy's effect in a scenario is an assumption you set, or a labelled suggested default, and never a prediction
• License: MIT
• Cost to run: none named, though the chat model is reached through Hugging Face Inference Providers
• Data it reaches: Al Meezan laws and articles, a World Bank file of Qatar indicators, a list of 74 NDS3 targets, and the team's data set on Hugging Face
• Not for: treating a scenario as a forecast of what a law will do

On Hugging Face, 13 November 2026

• Likes: 2
• Built with: gradio
• License: mit

Links

• Space: https://huggingface.co/spaces/malmarri/team-qh-167-qatarsignals-app
• Files: https://huggingface.co/spaces/malmarri/team-qh-167-qatarsignals-app/tree/main

Try it

Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.