app/services/service_gemini.py

import json
import os
import io
import base64
from typing import Any, Dict, List, Type, Union, Optional

import google.generativeai as genai
from google.generativeai.types import GenerationConfig, HarmCategory, HarmBlockThreshold # For safety settings
import weave # Assuming weave is still used
from pydantic import BaseModel, ValidationError # For schema validation

# Assuming these utilities are in the same relative paths or accessible
from app.utils.converter import product_data_to_str
from app.utils.image_processing import (
    get_data_format, # Assuming this returns 'jpeg', 'png' etc.
    get_image_base64_and_type, # Assuming this fetches URL and returns (base64_str, type_str)
    get_image_data, # Assuming this reads local path and returns base64_str
)
from app.utils.logger import exception_to_str, setup_logger

# Assuming these are correctly defined and accessible
from ..config import get_settings 
from ..core import errors
from ..core.errors import BadRequestError, VendorError # Using your custom errors
from ..core.prompts import get_prompts # Assuming prompts are compatible or adapted
from .base import BaseAttributionService # Assuming this base class exists

# Environment and Weave setup ( 그대로 유지 )
ENV = os.getenv("ENV", "LOCAL")
if ENV == "LOCAL":
    weave_project_name = "cfai/attribution-exp"
elif ENV == "DEV":
    weave_project_name = "cfai/attribution-dev"
elif ENV == "UAT":
    weave_project_name = "cfai/attribution-uat"
elif ENV == "PROD":
    pass # No weave for PROD

if ENV != "PROD":
    # weave.init(project_name=weave_project_name) # Assuming weave.init() is called elsewhere or if needed here
    print(f"Weave project name (potentially initialized elsewhere): {weave_project_name}")

settings = get_settings()
prompts = get_prompts()
logger = setup_logger(__name__)

# Configure the Gemini client
try:
    if settings.GEMINI_API_KEY:
        genai.configure(api_key=settings.GEMINI_API_KEY)
    else:
        logger.error("GEMINI_API_KEY not found in settings.")
        # Potentially raise an error or handle this case as per application requirements
except AttributeError:
    logger.error("Settings object does not have GEMINI_API_KEY attribute.")
    # Handle missing settings attribute

# Define default safety settings for Gemini
# Adjust these as per your application's requirements
DEFAULT_SAFETY_SETTINGS = {
    HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
    HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
    HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
    HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
}

class GeminiService(BaseAttributionService):
    def __init__(self, model_name: str = "gemini-2.5-flash-preview-04-17"):
        """
        Initializes the GeminiService.
        Args:
            model_name (str): The name of the Gemini model to use.
        """
        try:
            self.model = genai.GenerativeModel(
                model_name,
                safety_settings=DEFAULT_SAFETY_SETTINGS
                # system_instruction can be set here if a global system message is always used
            )
            logger.info(f"GeminiService initialized with model: {model_name}")
        except Exception as e:
            logger.error(f"Failed to initialize Gemini GenerativeModel: {exception_to_str(e)}")
            # Depending on requirements, you might want to raise an error here
            # For now, we'll let it proceed, and calls will fail if model isn't initialized.
            self.model = None


    def _prepare_image_parts(
        self,
        img_urls: Optional[List[str]] = None,
        img_paths: Optional[List[str]] = None,
        pil_images: Optional[List[Any]] = None, # PIL.Image.Image objects
    ) -> List[Dict[str, Any]]:
        """
        Prepares image data in the format expected by Gemini API.
        Decodes base64 image data to bytes.
        Converts PIL images to bytes.
        """
        image_parts = []

        # Process image URLs
        if img_urls:
            for img_url in img_urls:
                try:
                    base64_data, img_type = get_image_base64_and_type(img_url)
                    if base64_data and img_type:
                        # Gemini expects raw bytes, so decode base64
                        image_bytes = base64.b64decode(base64_data)
                        mime_type = f"image/{img_type.lower()}"
                        image_parts.append({"mime_type": mime_type, "data": image_bytes})
                    else:
                        logger.warning(f"Could not retrieve or identify type for image URL: {img_url}")
                except Exception as e:
                    logger.error(f"Error processing image URL {img_url}: {exception_to_str(e)}")

        # Process image paths
        if img_paths:
            for img_path in img_paths:
                try:
                    base64_data = get_image_data(img_path) # Assuming this returns base64 string
                    img_type = get_data_format(img_path) # Assuming this returns 'png', 'jpeg'
                    if base64_data and img_type:
                        image_bytes = base64.b64decode(base64_data)
                        mime_type = f"image/{img_type.lower()}"
                        image_parts.append({"mime_type": mime_type, "data": image_bytes})
                    else:
                        logger.warning(f"Could not retrieve or identify type for image path: {img_path}")
                except Exception as e:
                    logger.error(f"Error processing image path {img_path}: {exception_to_str(e)}")
        
        # Process PIL images
        if pil_images:
            for i, pil_image in enumerate(pil_images):
                try:
                    img_format = pil_image.format or 'PNG' # Default to PNG if format is not available
                    mime_type = f"image/{img_format.lower()}"
                    with io.BytesIO() as img_byte_arr:
                        pil_image.save(img_byte_arr, format=img_format)
                        image_bytes = img_byte_arr.getvalue()
                        image_parts.append({"mime_type": mime_type, "data": image_bytes})
                except Exception as e:
                    logger.error(f"Error processing PIL image #{i}: {exception_to_str(e)}")
        
        return image_parts

    @weave.op() # Assuming weave.op can be used as a decorator directly
    async def extract_attributes(
        self,
        attributes_model: Type[BaseModel],
        ai_model: str, # This will be the Gemini model name, e.g., "gemini-1.5-flash-latest"
        img_urls: Optional[List[str]] = None,
        product_taxonomy: str = "",
        product_data: Optional[Dict[str, Union[str, List[str]]]] = None,
        pil_images: Optional[List[Any]] = None,
        img_paths: Optional[List[str]] = None,
    ) -> Dict[str, Any]:
        if not self.model:
            raise VendorError("Gemini model not initialized.")
        if self.model.model_name != ai_model: # If a different model is requested for this specific call
             logger.info(f"Switching to model {ai_model} for this extraction request.")
             # Note: This creates a new model object for the call.
             # If this happens frequently, consider how model instances are managed.
             current_model = genai.GenerativeModel(ai_model, safety_settings=DEFAULT_SAFETY_SETTINGS)
        else:
            current_model = self.model

        # Construct the prompt text
        # Combining system and human prompts as Gemini typically takes a list of contents.
        # System instructions can also be part of the model's initialization.
        system_message = prompts.EXTRACT_INFO_SYSTEM_MESSAGE
        human_message = prompts.EXTRACT_INFO_HUMAN_MESSAGE.format(
            product_taxonomy=product_taxonomy,
            product_data=product_data_to_str(product_data if product_data else {}),
        )
        full_prompt_text = f"{system_message}\n\n{human_message}"
        
        # For logging or debugging the prompt
        logger.info(f"Gemini Prompt Text: {full_prompt_text[:500]}...") # Log a snippet

        content_parts = [full_prompt_text]
        
        # Prepare image parts
        try:
            image_parts = self._prepare_image_parts(img_urls, img_paths, pil_images)
            content_parts.extend(image_parts)
        except Exception as e:
            logger.error(f"Failed during image preparation: {exception_to_str(e)}")
            raise BadRequestError(f"Image processing failed: {e}")

        if not image_parts and (img_urls or img_paths or pil_images):
             logger.warning("Image sources provided, but no image parts were successfully prepared.")
        
        # Define generation config for JSON output
        # Pydantic's model_json_schema() generates an OpenAPI compliant schema dictionary.
        try:
            schema_for_gemini = attributes_model.model_json_schema()
        except Exception as e:
            logger.error(f"Error generating JSON schema from Pydantic model: {exception_to_str(e)}")
            raise VendorError(f"Could not generate schema for attributes_model: {e}")

        generation_config = GenerationConfig(
            response_mime_type="application/json",
            response_schema=schema_for_gemini, # Gemini expects the schema here
            temperature=0.0, # For deterministic output, similar to low top_p
            max_output_tokens=2048, # Adjust as needed, was 1000 for OpenAI
            # top_p, top_k can also be set if needed
        )
        
        logger.info(f"Extracting attributes via Gemini model: {current_model.model_name}...")
        try:
            response = await current_model.generate_content_async(
                contents=content_parts,
                generation_config=generation_config,
                # request_options={"timeout": 120} # Example: set timeout in seconds
            )
        except Exception as e: # Catches google.api_core.exceptions and others
            error_message = exception_to_str(e)
            logger.error(f"Gemini API call failed: {error_message}")
            # More specific error handling for Gemini can be added here
            # e.g., if isinstance(e, google.api_core.exceptions.InvalidArgument):
            # raise BadRequestError(f"Invalid argument to Gemini: {error_message}")
            raise VendorError(errors.VENDOR_THROW_ERROR.format(error_message=error_message))

        # Process the response
        try:
            # Check for safety blocks or refusals
            if not response.candidates:
                 # This can happen if all candidates were filtered due to safety or other reasons.
                block_reason_detail = "Unknown reason (no candidates)"
                if response.prompt_feedback and response.prompt_feedback.block_reason:
                    block_reason_detail = f"Blocked due to: {response.prompt_feedback.block_reason.name}"
                    if response.prompt_feedback.block_reason_message:
                         block_reason_detail += f" - {response.prompt_feedback.block_reason_message}"
                logger.error(f"Gemini response was blocked or empty. {block_reason_detail}")
                raise VendorError(f"Gemini response blocked or empty. {block_reason_detail}")


            # Assuming the first candidate is the one we want
            candidate = response.candidates[0]
            
            if candidate.finish_reason not in [1, 2]: # 1=STOP, 2=MAX_TOKENS
                finish_reason_str = candidate.finish_reason.name if candidate.finish_reason else "UNKNOWN"
                logger.warning(f"Gemini generation finished with reason: {finish_reason_str}")
                # Potentially raise error if finish reason is SAFETY, RECITATION, etc.
                if finish_reason_str == "SAFETY":
                    safety_ratings_str = ", ".join([f"{sr.category.name}: {sr.probability.name}" for sr in candidate.safety_ratings])
                    raise VendorError(f"Gemini content generation stopped due to safety concerns. Ratings: [{safety_ratings_str}]")


            if not candidate.content.parts or not candidate.content.parts[0].text:
                logger.error("Gemini response content is empty or not in the expected text format.")
                raise VendorError(errors.VENDOR_ERROR_INVALID_JSON + " (empty response text)")

            response_text = candidate.content.parts[0].text
            
            # Parse and validate the JSON response using the Pydantic model
            parsed_data = attributes_model.model_validate_json(response_text)
            return parsed_data.model_dump() # Return as dict

        except ValidationError as ve:
            logger.error(f"Pydantic validation failed for Gemini response: {ve}")
            logger.debug(f"Invalid JSON received from Gemini: {response_text[:500]}...") # Log snippet of invalid JSON
            raise VendorError(errors.VENDOR_ERROR_INVALID_JSON + f" Details: {ve}")
        except json.JSONDecodeError as je:
            logger.error(f"JSON decoding failed for Gemini response: {je}")
            logger.debug(f"Non-JSON response received: {response_text[:500]}...")
            raise VendorError(errors.VENDOR_ERROR_INVALID_JSON + f" Details: {je}")
        except VendorError: # Re-raise VendorErrors
            raise
        except Exception as e:
            error_message = exception_to_str(e)
            logger.error(f"Error processing Gemini response: {error_message}")
            # Log the raw response text if available and an error occurred
            raw_response_snippet = response_text[:500] if 'response_text' in locals() else "N/A"
            logger.debug(f"Problematic Gemini response snippet: {raw_response_snippet}")
            raise VendorError(f"Failed to process Gemini response: {error_message}")

    @weave.op()
    async def follow_schema(
        self, 
        schema: Dict[str, Any], # This should be an OpenAPI schema dictionary
        data: Dict[str, Any],
        ai_model: str = "gemini-1.5-flash-latest" # Model for this specific task
    ) -> Dict[str, Any]:
        if not self.model: # Check if the main model was initialized
            logger.warning("Main Gemini model not initialized. Attempting to initialize a temporary one for follow_schema.")
            try:
                current_model = genai.GenerativeModel(ai_model, safety_settings=DEFAULT_SAFETY_SETTINGS)
            except Exception as e:
                raise VendorError(f"Failed to initialize Gemini model for follow_schema: {exception_to_str(e)}")
        elif self.model.model_name != ai_model:
             logger.info(f"Switching to model {ai_model} for this follow_schema request.")
             current_model = genai.GenerativeModel(ai_model, safety_settings=DEFAULT_SAFETY_SETTINGS)
        else:
            current_model = self.model

        logger.info(f"Following schema via Gemini model: {current_model.model_name}...")
        
        # Prepare the prompt
        # System message can be part of the model or prepended here.
        system_message = prompts.FOLLOW_SCHEMA_SYSTEM_MESSAGE
        # The human message needs to contain the data to be transformed.
        # Ensure `json_info` placeholder is correctly used by your prompt string.
        try:
            data_as_json_string = json.dumps(data, indent=2)
        except TypeError as te:
            logger.error(f"Could not serialize 'data' to JSON for prompt: {te}")
            raise BadRequestError(f"Input data for schema following is not JSON serializable: {te}")

        human_message = prompts.FOLLOW_SCHEMA_HUMAN_MESSAGE.format(json_info=data_as_json_string)
        full_prompt_text = f"{system_message}\n\n{human_message}"
        
        content_parts = [full_prompt_text]

        # Define generation config for JSON output using the provided schema
        generation_config = GenerationConfig(
            response_mime_type="application/json",
            response_schema=schema, # The provided schema dictionary
            temperature=0.0, # For deterministic output
            max_output_tokens=2048, # Adjust as needed
        )

        try:
            response = await current_model.generate_content_async(
                contents=content_parts,
                generation_config=generation_config,
            )
        except Exception as e:
            error_message = exception_to_str(e)
            logger.error(f"Gemini API call failed for follow_schema: {error_message}")
            raise VendorError(errors.VENDOR_THROW_ERROR.format(error_message=error_message))

        # Process response
        try:
            if not response.candidates:
                block_reason_detail = "Unknown reason (no candidates)"
                if response.prompt_feedback and response.prompt_feedback.block_reason:
                    block_reason_detail = f"Blocked due to: {response.prompt_feedback.block_reason.name}"
                logger.error(f"Gemini response was blocked or empty in follow_schema. {block_reason_detail}")
                # OpenAI version returned {"status": "refused"}, mimicking similar for block
                return {"status": "refused", "reason": block_reason_detail}

            candidate = response.candidates[0]

            if candidate.finish_reason not in [1, 2]: # 1=STOP, 2=MAX_TOKENS
                finish_reason_str = candidate.finish_reason.name if candidate.finish_reason else "UNKNOWN"
                logger.warning(f"Gemini generation (follow_schema) finished with reason: {finish_reason_str}")
                if finish_reason_str == "SAFETY":
                     safety_ratings_str = ", ".join([f"{sr.category.name}: {sr.probability.name}" for sr in candidate.safety_ratings])
                     return {"status": "refused", "reason": f"Safety block. Ratings: [{safety_ratings_str}]"}


            if not candidate.content.parts or not candidate.content.parts[0].text:
                logger.error("Gemini response content (follow_schema) is empty.")
                # Mimic OpenAI's refusal structure or raise error
                return {"status": "refused", "reason": "Empty content from Gemini"}


            response_text = candidate.content.parts[0].text
            parsed_data = json.loads(response_text) # The schema is enforced by Gemini
            return parsed_data

        except json.JSONDecodeError as je:
            logger.error(f"JSON decoding failed for Gemini response (follow_schema): {je}")
            logger.debug(f"Non-JSON response received: {response_text[:500]}...")
            # The original code raised ValueError(errors.VENDOR_ERROR_INVALID_JSON)
            # Let's use VendorError for consistency if that's preferred, or ValueError
            raise VendorError(errors.VENDOR_ERROR_INVALID_JSON + f" (follow_schema) Details: {je}")
        except Exception as e:
            error_message = exception_to_str(e)
            logger.error(f"Error processing Gemini response (follow_schema): {error_message}")
            raw_response_snippet = response_text[:500] if 'response_text' in locals() else "N/A"
            logger.debug(f"Problematic Gemini response snippet (follow_schema): {raw_response_snippet}")
            raise VendorError(f"Failed to process Gemini response (follow_schema): {error_message}")