"""Inference pipeline: images -> LLM -> validated JSON -> summary."""

from __future__ import annotations

import json
import logging
import re
from pathlib import Path
from typing import Any, Dict, List, Sequence

from llm_clients import analyze
from prompt_loader import load_system_prompt
from schemas import ValidationError, build_summary, validate_trend_payload
from settings import Settings

LOGGER = logging.getLogger("pipeline")

DEFAULT_USER_PROMPT = "Analyze this garment image and output the micro-trend JSON per your schema."


def _extract_json(text: str) -> Dict[str, Any]:
    """Parse JSON; if raw text contains extra prose, grab the first JSON object."""
    try:
        return json.loads(text)
    except json.JSONDecodeError:
        pass

    match = re.search(r"\{.*\}", text, flags=re.S)
    if not match:
        raise json.JSONDecodeError("No JSON object found", text, 0)
    return json.loads(match.group(0))


def process_images(
    images: Sequence[bytes],
    model_choice: str,
    settings: Settings,
    system_prompt_path: Path | str | None = None,
    user_prompt: str = DEFAULT_USER_PROMPT,
) -> Dict[str, Any]:
    system_prompt = load_system_prompt(system_prompt_path) if system_prompt_path else load_system_prompt()

    raw_text = analyze(images, system_prompt, user_prompt, model_choice, settings)
    payload = _extract_json(raw_text)
    validated = validate_trend_payload(payload)
    summary = build_summary(validated)

    return {"trends": validated, "summary": summary}