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app.py

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+"""
+Gradio demo for the Watsonx Docs Type Classifier.
+Loads the best trained model from models/ and serves predictions.
+"""
+
+from pathlib import Path
+
+import gradio as gr
+import joblib
+import numpy as np
+from sentence_transformers import SentenceTransformer
+
+LABELS = ["conceptual", "how-to"]
+
+model_name = (Path("models") / "best_model_name.txt").read_text().strip()
+embedder = SentenceTransformer(model_name)
+clf = joblib.load(Path("models") / "best_model.joblib")
+
+
+def softmax(x):
+    e = np.exp(x - np.max(x))
+    return e / e.sum()
+
+
+def predict(text: str) -> dict:
+    if not text.strip():
+        return {label: 0.0 for label in LABELS}
+    embedding = embedder.encode([text], convert_to_numpy=True)
+    if hasattr(clf, "predict_proba"):
+        probs = clf.predict_proba(embedding)[0]
+    else:
+        scores = clf.decision_function(embedding)[0]
+        # LinearSVC returns a scalar for binary; wrap in array
+        if np.ndim(scores) == 0:
+            scores = np.array([-scores, scores])
+        probs = softmax(scores)
+    return {label: float(p) for label, p in zip(LABELS, probs)}
+
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(
+        label="Document text (title + body)",
+        lines=8,
+        placeholder="Paste the title and opening text of a Watsonx documentation page here.",
+    ),
+    outputs=gr.Label(num_top_classes=2, label="Predicted document type"),
+    title="Watsonx Docs Type Classifier",
+    description=(
+        "Predicts whether a Watsonx documentation page is **conceptual** or **how-to**. "
+        "Paste the page title and opening text below."
+    ),
+)
+
+if __name__ == "__main__":
+    demo.launch(share=False)