StoryboardBeats-Mini v0.1

What it does
Given a short story prompt, this model predicts:

Narrative beats (multi-label): opening, rising_action, key_moment, twist, resolution
Suggested visual style: Realistic, Anime, Comic, Watercolor, or Sketch

Why it's unique
A tiny, proprietary prototype that links text prompts to story structure and visual style — designed for pre-visualization workflows. Lightweight and CPU-friendly (scikit‑learn).

Files

model.joblib — scikit-learn pipelines (TF‑IDF + Logistic Regression) for beats (multi-label) and style.
inference.py — minimal interface with load_model() and predict().
requirements.txt — dependencies to run inference.py.

Quick use (local)

pip install -r requirements.txt
python -c "import inference; print(inference.predict(['A robot in a neon city discovers a secret, but time runs out.']))"

Metrics (synthetic split)

beats (subset accuracy): 0.717
style (accuracy): 1.000

⚠️ Trained on synthetic data; not suitable for production. Educational / research use only.

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