import base64
import io
import json
import os
import time
from typing import Any, Dict, Optional
from PIL import Image
import requests


def _image_to_base64(image: Image.Image) -> str:
    buffer = io.BytesIO()
    image_format = (image.format or "PNG").upper()
    if image_format not in {"PNG", "JPEG", "JPG"}:
        image_format = "PNG"
    image.save(buffer, format=image_format)
    return base64.b64encode(buffer.getvalue()).decode("utf-8")


def _extract_status(payload: Dict[str, Any]) -> Optional[str]:
    status_info = payload.get("status") or payload.get("state")
    if isinstance(status_info, dict):
        state = status_info.get("state") or status_info.get("status")
        if isinstance(state, str):
            return state.lower()
    elif isinstance(status_info, str):
        return status_info.lower()
    return None


def _poll_bria_status(
    status_url: str,
    headers: Dict[str, str],
    timeout_seconds: int = 120,
    poll_interval: float = 1.5,
) -> Dict[str, Any]:
    deadline = time.time() + timeout_seconds
    while True:
        response = requests.get(status_url, headers=headers, timeout=30)
        response.raise_for_status()
        payload: Dict[str, Any] = response.json()
        state = _extract_status(payload)

        if state in {"succeeded", "success", "completed", "done"}:
            if isinstance(payload.get("result"), dict):
                return payload["result"]
            if payload.get("results") is not None:
                return payload["results"]
            return payload

        if state in {"failed", "error", "cancelled", "canceled"}:
            raise RuntimeError(
                f"Bria VLM API request failed: {json.dumps(payload, indent=2)}"
            )

        if time.time() > deadline:
            raise TimeoutError(
                f"Bria VLM API request timed out while polling {status_url}"
            )

        time.sleep(poll_interval)


def _submit_bria_request(
    url: str, payload: Dict[str, Any], api_token: str
) -> Dict[str, Any]:
    headers = {
        "Content-Type": "application/json",
        "api_token": api_token,
    }
    response = requests.post(url, json=payload, headers=headers, timeout=30)
    response.raise_for_status()
    initial_payload: Dict[str, Any] = response.json()

    status_url = (
        initial_payload.get("status_url")
        or initial_payload.get("statusUrl")
        or (initial_payload.get("status") or {}).get("status_url")
    )

    if status_url:
        return _poll_bria_status(status_url, headers)

    if isinstance(initial_payload.get("result"), dict):
        return initial_payload["result"]
    if initial_payload.get("results") is not None:
        return initial_payload["results"]

    return initial_payload


def _parse_vlm_response(data: Any, prompt_role: str) -> str:
    if isinstance(data, dict):
        direct_match = data.get(prompt_role)
        if isinstance(direct_match, str):
            return direct_match

        for key in ("prompt", "structured_prompt", "structuredPrompt", "text"):
            if key in data:
                value = data[key]
                if isinstance(value, str):
                    return value
                if isinstance(value, dict):
                    nested = value.get(prompt_role)
                    if isinstance(nested, str):
                        return nested

        for key in ("result", "results"):
            if key in data:
                nested_result = _parse_vlm_response(data[key], prompt_role)
                if nested_result:
                    return nested_result

    if isinstance(data, list):
        for item in data:
            nested_result = _parse_vlm_response(item, prompt_role)
            if nested_result:
                return nested_result

    return json.dumps(data)


def get_prompt_api(image_path: str, prompt_role: str) -> str:
    """Send an image to the Bria VLM API and return the extracted prompt text.

    The payload keys are aligned with the current public docs but may require
    adjustment if your Bria workspace is configured differently. Override the
    default endpoint via the ``BRIA_API_VLM_ENDPOINT`` environment variable if
    you are using a custom workflow.
    """
    api_token = os.environ.get("BRIA_API_KEY")
    if not api_token:
        raise EnvironmentError(
            "BRIA_API_KEY environment variable is required to use the Bria VLM API."
        )

    base_url = os.environ.get("BRIA_API_BASE_URL", "https://engine.prod.bria-api.com")
    endpoint = os.environ.get("BRIA_API_VLM_ENDPOINT", "/v2/structured_prompt/generate")
    url = f"{base_url.rstrip('/')}{endpoint}"

    # convert image to base64
    with Image.open(image_path) as image:
        image_b64 = _image_to_base64(image)

    payload = {"images": [image_b64]}

    response = _submit_bria_request(url, payload, api_token)

    return response["structured_prompt"]


def get_image_from_url(image_url: str) -> Image.Image:
    """Get an image from a URL."""
    response = requests.get(image_url)
    return Image.open(io.BytesIO(response.content))


def generate_image(prompt: str) -> Image.Image:
    """Generate an image from a prompt using the Bria VLM API."""
    api_token = os.environ.get("BRIA_API_KEY")
    if not api_token:
        raise EnvironmentError(
            "BRIA_API_KEY environment variable is required to use the Bria VLM API."
        )

    base_url = os.environ.get("BRIA_API_BASE_URL", "https://engine.prod.bria-api.com")
    endpoint = os.environ.get("BRIA_API_GENERATE_ENDPOINT", "/v2/image/generate")
    url = f"{base_url.rstrip('/')}{endpoint}"

    payload = {"structured_prompt": prompt}

    response = _submit_bria_request(url, payload, api_token)

    return get_image_from_url(response["image_url"])