inference.py

import joblib, numpy as np, os
MODEL = joblib.load("pipeline_full.joblib") if os.path.exists("pipeline_full.joblib") else joblib.load("model.joblib")

def run(inputs):
    try:
        # expecting either {"text":"..."} or raw list
        if isinstance(inputs, dict):
            if "text" in inputs:
                X = [inputs["text"]]
            elif "inputs" in inputs and isinstance(inputs["inputs"], dict) and "features" in inputs["inputs"]:
                X = [inputs["inputs"]["features"]]
            else:
                X = [inputs]
        else:
            X = [inputs]
        if hasattr(MODEL, "predict_proba"):
            return {"prediction": MODEL.predict_proba(X).tolist()}
        else:
            return {"prediction": MODEL.predict(X).tolist()}
    except Exception as e:
        return {"error": str(e)}