"""Wizara Vision API endpoint handlers."""

from __future__ import annotations

import os
from typing import Any, Callable

from PIL import Image

from .responses import (
    detections_to_objects,
    error_response,
    parse_advanced_settings,
    success_response,
    unsupported_response,
)


def resolve_image_path(image_file: Any, base_dir: str) -> str | None:
    if isinstance(image_file, str):
        candidate = image_file
        if not os.path.isabs(candidate):
            candidate = os.path.join(base_dir, candidate)
        return candidate if os.path.exists(candidate) else None
    if isinstance(image_file, dict):
        path = image_file.get("path")
        return path if path and os.path.exists(path) else None
    path = getattr(image_file, "path", None)
    return path if path and os.path.exists(path) else None


def merge_settings(
    *,
    model_mode: str,
    temp: float,
    top_p: float,
    top_k: int,
    short_size: int | None,
    advanced_settings: str | None,
) -> dict[str, Any]:
    settings = {
        "model_mode": model_mode,
        "temp": temp,
        "top_p": top_p,
        "top_k": top_k,
        "short_size": short_size,
    }
    settings.update(parse_advanced_settings(advanced_settings))
    return settings


def handle_detect(
    *,
    image_file: Any,
    categories: str,
    task_type: str,
    model_mode: str,
    temp: float,
    top_p: float,
    top_k: int,
    short_size: int | None,
    advanced_settings: str | None,
    base_dir: str,
    run_image_gpu: Callable[..., tuple],
    generate_prompt: Callable[[str, str], str],
    parse_results: Callable[[str, str], list[dict[str, Any]]],
) -> dict[str, Any]:
    if not image_file:
        return error_response("Image is required.", code="MISSING_IMAGE")

    image_path = resolve_image_path(image_file, base_dir)
    if not image_path:
        return error_response("Invalid image file path.", code="INVALID_IMAGE")

    settings = merge_settings(
        model_mode=model_mode,
        temp=temp,
        top_p=top_p,
        top_k=top_k,
        short_size=short_size,
        advanced_settings=advanced_settings,
    )

    category = categories.strip() or "objects"
    task = task_type.strip() or "Detection"
    question_override = settings.get("question_override")
    if not question_override:
        question_override = generate_prompt(task, category)

    try:
        image = Image.open(image_path).convert("RGB")
        width, height = image.size
        _, stats, raw_text, _, _ = run_image_gpu(
            image_path,
            category,
            settings.get("model_mode", model_mode),
            float(settings.get("temp", temp)),
            float(settings.get("top_p", top_p)),
            int(settings.get("top_k", top_k)),
            settings.get("short_size", short_size),
            question_override,
        )
        category_str = " ".join(part.strip() for part in category.split(",") if part.strip())
        detections = parse_results(raw_text, category_str)
        objects = detections_to_objects(detections)
        return success_response(
            image_width=width,
            image_height=height,
            objects=objects,
            task=task.lower(),
            extra={
                "stats": stats,
                "raw_text": raw_text,
                "prompt": question_override,
            },
        )
    except Exception as exc:  # noqa: BLE001
        return error_response(str(exc), code="INFERENCE_FAILED")


def handle_ocr(
    *,
    image_file: Any,
    model_mode: str,
    temp: float,
    top_p: float,
    top_k: int,
    short_size: int | None,
    advanced_settings: str | None,
    base_dir: str,
    run_image_gpu: Callable[..., tuple],
    generate_prompt: Callable[[str, str], str],
    parse_results: Callable[[str, str], list[dict[str, Any]]],
) -> dict[str, Any]:
    return handle_detect(
        image_file=image_file,
        categories="text",
        task_type="OCR",
        model_mode=model_mode,
        temp=temp,
        top_p=top_p,
        top_k=top_k,
        short_size=short_size,
        advanced_settings=advanced_settings,
        base_dir=base_dir,
        run_image_gpu=run_image_gpu,
        generate_prompt=generate_prompt,
        parse_results=parse_results,
    )


def handle_unsupported(task: str) -> dict[str, Any]:
    return unsupported_response(task)