mulaxprime/Tsela

Offline AI assistant helping Botswana students navigate university admissions — no internet required. # Tsela — Offline University Admissions Guidance for Botswana Tsela is an offline, on-device AI assistant that helps Botswana students navigate university admissions — points requirements, fees, and deadlines — with zero internet dependency at inference time. Built for the Africa Deep Tech Challenge 2026. Full technical writeup, design decisions, and benchmark results: **REPORT.md** ## Why Reliable admissions information for Botswana's universities is scattered and often unreliable — and not every student researching their options has consistent internet access. Tsela runs entirely offline once set up, using a small quantized language model backed by a verified knowledge base covering six institutions: University of Botswana (UB), BIUST, BA ISAGO, Botho University, Limkokwing University of Creative Technology, and the Botswana School of Business Sciences (formerly BAC). ## Setup **Requirements:** - Python 3.10+ - llama.cpp built with `llama-cli` available — either on your system `PATH`, or point to it via the `LLAMA_CLI_PATH` environment variable **1. Clone the repo:** ```bash git clone github.com cd Tsela ``` **2. Download the model weights:** ```bash bash download_model.sh ``` This pulls the GGUF model file (~1.1 GB) from a public Hugging Face repo into `model/`. Safe to re-run — it skips the download if the file already exists. **3. Point to your `llama-cli` binary (if it's not on your system PATH):** ```bash export LLAMA_CLI_PATH=/path/to/llama-cli ``` **4. Run the GUI:** ```bash python app.py ``` ## Project Structure ``` Tsela/ ├── app.py # Dark-theme tkinter GUI ├── run_model.py # Model inference + points-lookup bypass logic ├── knowledge_base.py # Verified university admissions data ├── download_model.sh # Downloads model weights (required for ADTC submission) ├── metadata.json # ADTC submission metadata ├── REPORT.md # Full technical writeup ├── ben …