# app/core/gemini.py
import logging
from typing import List
import google.generativeai as genai
from google.generativeai.types import HarmCategory, HarmBlockThreshold # For safety settings
from google.api_core import exceptions as google_exceptions

from app.config import settings

logger = logging.getLogger(__name__)

# --- Global variable to hold the initialized model client ---
gemini_flash_client = None
gemini_initialization_error = None # Store potential init error

# --- Configure and Initialize ---
try:
    if settings.GEMINI_API_KEY:
        genai.configure(api_key=settings.GEMINI_API_KEY)
        # Initialize the specific model we want to use
        gemini_flash_client = genai.GenerativeModel(
            model_name="gemini-2.0-flash",
            # Optional: Add default safety settings
            # Adjust these based on your expected content and risk tolerance
            safety_settings={
                HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
                HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
                HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
                HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
            },
            # Optional: Add default generation config (can be overridden per request)
            # generation_config=genai.types.GenerationConfig(
            #     # candidate_count=1, # Usually default is 1
            #     # stop_sequences=["\n"],
            #     # max_output_tokens=2048,
            #     # temperature=0.9, # Controls randomness (0=deterministic, >1=more random)
            #     # top_p=1,
            #     # top_k=1
            # )
        )
        logger.info("Gemini AI client initialized successfully for model 'gemini-1.5-flash-latest'.")
    else:
        # Store error if API key is missing
        gemini_initialization_error = "GEMINI_API_KEY not configured. Gemini client not initialized."
        logger.error(gemini_initialization_error)

except Exception as e:
    # Catch any other unexpected errors during initialization
    gemini_initialization_error = f"Failed to initialize Gemini AI client: {e}"
    logger.exception(gemini_initialization_error) # Log full traceback
    gemini_flash_client = None # Ensure client is None on error


# --- Function to get the client (optional, allows checking error) ---
def get_gemini_client():
    """
    Returns the initialized Gemini client instance.
    Raises an exception if initialization failed.
    """
    if gemini_initialization_error:
        raise RuntimeError(f"Gemini client could not be initialized: {gemini_initialization_error}")
    if gemini_flash_client is None:
         # This case should ideally be covered by the check above, but as a safeguard:
         raise RuntimeError("Gemini client is not available (unknown initialization issue).")
    return gemini_flash_client

# Define the prompt as a constant
OCR_ITEM_EXTRACTION_PROMPT = """
Extract the shopping list items from this image.
List each distinct item on a new line.
Ignore prices, quantities, store names, discounts, taxes, totals, and other non-item text.
Focus only on the names of the products or items to be purchased.
If the image does not appear to be a shopping list or receipt, state that clearly.
Example output for a grocery list:
Milk
Eggs
Bread
Apples
Organic Bananas
"""

async def extract_items_from_image_gemini(image_bytes: bytes) -> List[str]:
    """
    Uses Gemini Flash to extract shopping list items from image bytes.

    Args:
        image_bytes: The image content as bytes.

    Returns:
        A list of extracted item strings.

    Raises:
        RuntimeError: If the Gemini client is not initialized.
        google_exceptions.GoogleAPIError: For API call errors (quota, invalid key etc.).
        ValueError: If the response is blocked or contains no usable text.
    """
    client = get_gemini_client() # Raises RuntimeError if not initialized

    # Prepare image part for multimodal input
    image_part = {
        "mime_type": "image/jpeg", # Or image/png, image/webp etc. Adjust if needed or detect mime type
        "data": image_bytes
    }

    # Prepare the full prompt content
    prompt_parts = [
        OCR_ITEM_EXTRACTION_PROMPT, # Text prompt first
        image_part                 # Then the image
    ]

    logger.info("Sending image to Gemini for item extraction...")
    try:
        # Make the API call
        # Use generate_content_async for async FastAPI
        response = await client.generate_content_async(prompt_parts)

        # --- Process the response ---
        # Check for safety blocks or lack of content
        if not response.candidates or not response.candidates[0].content.parts:
             logger.warning("Gemini response blocked or empty.", extra={"response": response})
             # Check finish_reason if available
             finish_reason = response.candidates[0].finish_reason if response.candidates else 'UNKNOWN'
             safety_ratings = response.candidates[0].safety_ratings if response.candidates else 'N/A'
             if finish_reason == 'SAFETY':
                 raise ValueError(f"Gemini response blocked due to safety settings. Ratings: {safety_ratings}")
             else:
                 raise ValueError(f"Gemini response was empty or incomplete. Finish Reason: {finish_reason}")

        # Extract text - assumes the first part of the first candidate is the text response
        raw_text = response.text # response.text is a shortcut for response.candidates[0].content.parts[0].text
        logger.info("Received raw text from Gemini.")
        # logger.debug(f"Gemini Raw Text:\n{raw_text}") # Optional: Log full response text

        # Parse the text response
        items = []
        for line in raw_text.splitlines(): # Split by newline
            cleaned_line = line.strip() # Remove leading/trailing whitespace
            # Basic filtering: ignore empty lines and potential non-item lines
            if cleaned_line and len(cleaned_line) > 1: # Ignore very short lines too?
                # Add more sophisticated filtering if needed (e.g., regex, keyword check)
                items.append(cleaned_line)

        logger.info(f"Extracted {len(items)} potential items.")
        return items

    except google_exceptions.GoogleAPIError as e:
        logger.error(f"Gemini API Error: {e}", exc_info=True)
        # Re-raise specific Google API errors for endpoint to handle (e.g., quota)
        raise e
    except Exception as e:
        # Catch other unexpected errors during generation or processing
        logger.error(f"Unexpected error during Gemini item extraction: {e}", exc_info=True)
        # Wrap in a generic ValueError or re-raise
        raise ValueError(f"Failed to process image with Gemini: {e}") from e