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LICENSE

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+MIT License
+
+Copyright (c) 2025 Eric Maldon
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+language: 
+- en
+pipeline_tag: text-classification
+license: mit
+tags:
+- storyboard
+- beats
+- style-suggestion
+- nlp
+- sklearn
+- educational
+model-index:
+- name: StoryboardBeats-Mini-0.1
+  results: []
+---
+
+# 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)
+```bash
+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.

inference.py

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+import joblib
+from typing import List, Dict
+
+ARTIFACT_PATH = "model.joblib"
+_model = None
+
+def load_model():
+    global _model
+    if _model is None:
+        _model = joblib.load(ARTIFACT_PATH)
+    return _model
+
+def predict(inputs: List[str]) -> Dict:
+    """
+    Inputs: list of prompts (strings)
+    Returns: dict with beats (multi-label) and style for each input
+    """
+    model = load_model()
+    beats_clf = model["beats_model"]
+    style_clf = model["style_model"]
+    beats_classes = model["beats_classes"]
+    # Beats probabilities -> threshold 0.5
+    beats_proba = beats_clf.predict_proba(inputs)
+    beats_pred = (beats_proba >= 0.5).astype(int)
+    # Style label
+    style_pred = style_clf.predict(inputs)
+    results = []
+    for i, text in enumerate(inputs):
+        beats = [b for b, v in zip(beats_classes, beats_pred[i]) if v == 1]
+        results.append({
+            "input": text,
+            "beats": beats,
+            "style": str(style_pred[i]),
+            "beats_proba": {b: float(p) for b, p in zip(beats_classes, beats_proba[i])}
+        })
+    return {"results": results}

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b3d859b303324de2e96fdcb6110b231ba36806470a62075202cd6d65d3f97918
+size 38088

requirements.txt

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+scikit-learn>=1.2
+joblib>=1.3
+numpy>=1.24