"""Script 07: Inference demo. Run a few hand-crafted reviews through the Proposed
model to show the per-aspect output the customer asked for.

Usage:
  python scripts/07_inference.py
"""
import json
import sys
from pathlib import Path

sys.path.insert(0, str(Path(__file__).resolve().parent.parent))

from src.utils import setup_logging
from src import config as cfg
from src.inference import AspectPredictor


# A few representative reviews + matching product metadata
DEMO_INPUTS = [
    {
        "review_text": (
            "The size runs really small, I ordered an XL but it fits like a Medium. "
            "That said, the fabric feels super soft and the print looks great."
        ),
        "product_meta": {
            "features_text": "100% Cotton, Slim Fit, Machine Wash Cold",
            "categories_text": "Clothing > Men > T-Shirts > Graphic Tees",
            "price": 19.99,
            "average_rating": 4.2,
            "rating_number": 312,
        },
    },
    {
        "review_text": (
            "Cheap quality, the stitching came apart after one wash. "
            "Not worth even the discount price."
        ),
        "product_meta": {
            "features_text": "Polyester blend, machine washable",
            "categories_text": "Clothing > Women > Dresses > Casual",
            "price": 9.99,
            "average_rating": 3.1,
            "rating_number": 87,
        },
    },
    {
        "review_text": (
            "Absolutely beautiful dress. The colour matches the photo perfectly "
            "and it's very flattering on. Got many compliments. Worth every penny."
        ),
        "product_meta": {
            "features_text": "Stretch jersey, A-line silhouette, midi length",
            "categories_text": "Clothing > Women > Dresses > Formal",
            "price": 89.00,
            "average_rating": 4.7,
            "rating_number": 540,
        },
    },
]


def main():
    setup_logging()
    ckpt = cfg.CHECKPOINT_DIR / "meta_acsa" / "best.pt"
    if not ckpt.exists():
        raise SystemExit(f"Proposed model checkpoint not found at {ckpt}. "
                         "Train it first (scripts/04_train_proposed.py).")

    predictor = AspectPredictor()
    for i, item in enumerate(DEMO_INPUTS, 1):
        print(f"\n--- Demo {i} ---")
        print(f"Review: {item['review_text']}")
        print(f"Meta:   features={item['product_meta']['features_text'][:60]}...  "
              f"price={item['product_meta']['price']}")
        out = predictor.predict(item["review_text"], item["product_meta"])
        print("Aspect-level prediction:")
        for a in cfg.ASPECTS:
            print(f"  {a:<10} -> {out['aspects'][a]}")
        if "meta_attention" in out:
            top = max(out["meta_attention"], key=out["meta_attention"].get)
            print(f"  (model attended most to {top}: "
                  f"{out['meta_attention'][top]:.3f})")


if __name__ == "__main__":
    main()