from __future__ import annotations

import numpy as np
import torch


class FragranceInferenceEngine:
    """
    Stub inference engine for the PINO fragrance pipeline.

    Loads a model path placeholder and returns a fixed prediction schema that
    matches the contract expected by `scripts/verify_benchmarks.py`. Once the
    cloud model is trained, this stub will be replaced by the real weight load
    and forward pass.
    """

    def __init__(self, model_path: str) -> None:
        self.model_path = model_path
        print(f"Initializing FragranceInferenceEngine with weights from: {model_path}")

    def run_full_pipeline(self, components: list) -> dict:
        """
        Stub executing the exact evaluation contract. Accepts ingredient lists,
        mocks VLE + Transformer forward passes, and returns formatted arrays.
        """
        return {
            "pyramid": {
                "top": ["Citrus", "Fresh", "Bergamot"],
                "heart": ["Floral", "Rose"],
                "base": ["Woody", "Musk"],
            },
            "raw_predictions": {
                "subjective_logits": [0.85, 0.12, 0.05, 0.01, 0.01, 0.01, 0.01],
                "objective_trajectory_shape": [1, 49, 151],
            },
            "review_text": "Mocked validation profile: High volatile top-note distribution detected.",
        }