import io
import os
import base64
from io import BytesIO
from PIL import Image
from dotenv import load_dotenv
from openai import OpenAI
from src.utils.constants import Constants
from src.utils.logging import get_logger

class ImageProcessor:
    """
    Class to handle image processing and description using OpenAI.
    """

    def __init__(self):
        """
        Initialize the image processor with OpenAI client.
        """
        self.logger = get_logger()
        self.logger.info("Initializing image processor")

        load_dotenv()
        self.openai_api_key = os.getenv("OPENAI_API_KEY")
        if not self.openai_api_key:
            self.logger.error("OPENAI_API_KEY environment variable is not set")
            raise ValueError("OPENAI_API_KEY environment variable is not set")

        try:
            self.client = OpenAI(api_key=self.openai_api_key)
            self.logger.info("OpenAI client initialized successfully")
        except Exception as e:
            self.logger.error(f"Failed to initialize OpenAI client: {str(e)}")
            raise

    def describe_image(self, image_data):
        """
        Generate a description of the image using OpenAI.

        Args:
            image_data: The image data, can be a file path, bytes, or a PIL Image object.

        Returns:
            str: Description of the image.
        """
        if not image_data:
            self.logger.debug("No image data provided, returning empty description")
            return ""

        self.logger.info("Processing image for description")
        try:
            # Handle different types of image_data
            if isinstance(image_data, str):
                # Assume it's a file path
                self.logger.debug(f"Loading image from file path: {image_data}")
                with open(image_data, "rb") as image_file:
                    image_bytes = image_file.read()
                image = Image.open(BytesIO(image_bytes))
            elif isinstance(image_data, Image.Image):
                # It's already a PIL Image object
                self.logger.debug("Using provided PIL Image object")
                image = image_data
            else:
                # Assume it's bytes
                self.logger.debug("Using provided image bytes")
                image = Image.open(BytesIO(image_data))
            original_size = image.size
            max_size = (1024, 1024)  # OpenAI's recommended max size
            image.thumbnail(max_size, Image.LANCZOS)
            if original_size != image.size:
                self.logger.debug(f"Resized image from {original_size} to {image.size}")

            # Convert to base64
            buffered = BytesIO()
            image.save(buffered, format="JPEG")
            base64_image = base64.b64encode(buffered.getvalue()).decode("utf-8")
            self.logger.debug("Image converted to base64 for API call")

            # Call OpenAI API
            self.logger.info(f"Calling OpenAI API with model: {Constants.MODEL}")
            response = self.client.chat.completions.create(
                model=Constants.MODEL,
                messages=[
                    {"role": "system", "content": "You are a helpful assistant that describes images."},
                    {"role": "user", "content": [
                        {"type": "text", "text": Constants.IMAGE_DESCRIPTION_PROMPT},
                        {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}
                    ]}
                ],
                max_tokens=300
            )

            description = response.choices[0].message.content.strip()
            description_preview = description[:50] + "..." if len(description) > 50 else description
            self.logger.info(f"Image description generated: {description_preview}")
            return description
        except Exception as e:
            self.logger.error(f"Error describing image: {str(e)}")
            raise

    def resize_image(self, image_data, max_width=300, max_height=300):
        """
        Resize an image to fit within the specified dimensions while maintaining aspect ratio.

        Args:
            image_data: The image data, can be a file path, bytes, or a PIL Image object.
            max_width (int, optional): Maximum width of the resized image. Defaults to 300.
            max_height (int, optional): Maximum height of the resized image. Defaults to 300.

        Returns:
            bytes: The resized image data.
        """
        if not image_data:
            self.logger.debug("No image data provided for resizing, returning None")
            return None

        self.logger.info(f"Resizing image to max dimensions: {max_width}x{max_height}")
        try:
            # Handle different types of image_data
            if isinstance(image_data, str):
                # Assume it's a file path
                self.logger.debug(f"Loading image from file path: {image_data}")
                image = Image.open(image_data)
            elif isinstance(image_data, Image.Image):
                # It's already a PIL Image object
                self.logger.debug("Using provided PIL Image object")
                image = image_data
            else:
                # Assume it's bytes
                self.logger.debug("Loading image from bytes")
                image = Image.open(BytesIO(image_data))

            original_size = image.size
            self.logger.debug(f"Original image size: {original_size}")

            # Resize image
            image.thumbnail((max_width, max_height), Image.LANCZOS)
            new_size = image.size
            self.logger.debug(f"Resized image to: {new_size}")

            # Convert back to bytes
            buffered = BytesIO()
            image.save(buffered, format="JPEG")
            result = buffered.getvalue()
            self.logger.info(f"Image resized successfully from {original_size} to {new_size}")
            return result
        except Exception as e:
            self.logger.error(f"Error resizing image: {str(e)}")
            raise