Edge Vision Mathifyer

Edge Vision + Math Engine (Quickstart Bundle)

This bundle contains two self-contained projects:

1. edge-vision/ — Real-time, privacy-preserving computer vision demo on local hardware with OpenCV.
   • Streamlit UI that plays a sample video, measures FPS, and (optionally) uses a TFLite model if you add one.
2. math-engine/ — SymPy + NumPy solver for calculus/combinatorics/probability, with a Streamlit UI and Monte Carlo verification.

Tip: Use two terminals (or tabs) to run both UIs.

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0) Create Python env (recommended)

python3 -m venv .venv && source .venv/bin/activate
# Windows: .venv\Scripts\activate

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1) Edge Vision — Run

cd edge-vision
pip install -r requirements.txt
# Run Streamlit dashboard (plays sample video and shows FPS)
streamlit run apps/streamlit_app.py

• Add your model at edge-vision/models/model.tflite (optional). If present, the app will try to run TFLite inference.
• Otherwise, it will fall back to a simple OpenCV edge detector as a placeholder.

CLI benchmark (optional):

python tools/benchmark.py --video data/sample.mp4

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2) Math Engine — Run

cd math-engine
pip install -r requirements.txt
streamlit run streamlit_app.py

Examples in the UI:

• Calculus: integral(sin(x)^2, x)
• Probability: P(sum_{i=1..3} die() >= 10)
• Combinatorics: C(10,3)

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Notes

• tflite-runtime is platform-specific; we do not auto-install it. If you need it, install for your device (Raspberry Pi/ARM, etc.).
• Everything runs locally and offline. Replace data/sample.mp4 with your own footage for edge-vision.