import io
from typing import Any, Sequence, List

from PIL import Image

from services.exceptions import GenerationError
from services.image.ImageGenerationResult import ImageGenerationResult
from services.image.ImageGenerator import ImageGenerator
from services.progress import ProgressCallback, call_progress
from services.registry import register_service
from services.utils.google_service import DEFAULT_GEMINI_IMAGE_MODEL, \
    iter_response_parts


@register_service
class GoogleImageGenerator(ImageGenerator):
    """Image generator backed by the Google Gemini API."""
    service_id = "google_gemini_image"

    def __init__(self, model_name: str | None = None, api_key: str | None = None) -> None:
        super().__init__(model_name=model_name)
        try:
            from google import genai  # type: ignore[import-not-found]
        except ModuleNotFoundError as exc:
            raise GenerationError(
                "The 'google-genai' package is required to use the Google image backend."
            ) from exc

        client_kwargs: dict[str, Any] = {}
        if api_key is not None:
            client_kwargs["api_key"] = api_key

        # If api_key is omitted, the client will read GOOGLE_API_KEY from the environment
        self._client = genai.Client(**client_kwargs)

    @classmethod
    def default_model_name(cls) -> str:
        return DEFAULT_GEMINI_IMAGE_MODEL

    def close(self) -> None:
        # The google client does not expose an explicit close method currently
        return

    def generate(
            self,
            prompt: str,
            images: Sequence[Image.Image] | None = None,
            *,
            progress: ProgressCallback | None = None,
            aspect_ratio: str | None = None,
            **kwargs: Any,
    ) -> ImageGenerationResult:
        from google.genai import types  # type: ignore[import-not-found]

        call_progress(progress, 0.1, "Encoding inputs")

        request_parts: List[object] = [prompt]
        images = images or []

        for image in images:
            buffer = io.BytesIO()
            image.save(buffer, format="PNG")
            image_bytes = buffer.getvalue()

            part: Any | None = None

            part_class = getattr(types, "Part", None)
            from_bytes = getattr(part_class, "from_bytes", None)
            if callable(from_bytes):
                part = from_bytes(data=image_bytes, mime_type="image/png")

            if part is None:
                input_image_class = getattr(types, "InputImage", None)
                if input_image_class is not None:
                    part = input_image_class(mime_type="image/png", data=image_bytes)

            if part is None:
                raise GenerationError(
                    "The installed google-genai client does not support image inputs."
                )

            request_parts.append(part)

        call_progress(progress, 0.4, "Calling Google Gemini image model")

        response = self._client.models.generate_content(
            model=self.model_name,
            contents=request_parts,
        )

        call_progress(progress, 0.7, "Decoding image")

        generated_images: List[Image.Image] = []
        for part in iter_response_parts(response):
            inline_data = getattr(part, "inline_data", None)
            if inline_data is None:
                continue

            data: bytes | None = getattr(inline_data, "data", None)
            if data is None:
                continue

            buffer = io.BytesIO(data)
            try:
                img = Image.open(buffer).convert("RGBA")
            except OSError as exc:
                raise GenerationError("Received invalid image data from Gemini.") from exc
            img.load()
            generated_images.append(img)

        if not generated_images:
            raise GenerationError("The Google image model did not return any image data.")

        call_progress(progress, 0.95, "Preparing image result")

        return ImageGenerationResult(
            provider="google",
            model=self.model_name,
            images=generated_images,
            raw_response=response,
        )