# Copyright (c) 2025 Patrick Motsch
# All rights reserved.
import logging
import httpx
from typing import List
from fastapi import HTTPException
from modules.shared.configuration import APP_CONFIG
from .aicoreBase import BaseConnectorAi
from modules.datamodels.datamodelAi import AiModel, PriorityEnum, ProcessingModeEnum, OperationTypeEnum, AiModelCall, AiModelResponse, createOperationTypeRatings, AiCallPromptImage

# Configure logger
logger = logging.getLogger(__name__)

class ContextLengthExceededException(Exception):
    """Exception raised when the context length exceeds the model's limit"""
    pass

class RateLimitExceededException(Exception):
    """Exception raised when the provider's rate limit (TPM) is exceeded"""
    pass

def loadConfigData():
    """Load configuration data for OpenAI connector"""
    return {
        "apiKey": APP_CONFIG.get('Connector_AiOpenai_API_SECRET'),
    }

class AiOpenai(BaseConnectorAi):
    """Connector for communication with the OpenAI API."""
    
    def __init__(self):
        super().__init__()
        # Load configuration
        self.config = loadConfigData()
        self.apiKey = self.config["apiKey"]
        
        # HttpClient for API calls
        # Timeout set to 600 seconds (10 minutes) for complex requests that may take longer
        # AiService calls can take significantly longer due to prompt building and processing overhead
        self.httpClient = httpx.AsyncClient(
            timeout=600.0,
            headers={
                "Authorization": f"Bearer {self.apiKey}",
                "Content-Type": "application/json"
            }
        )
        logger.info("OpenAI Connector initialized")
    
    def getConnectorType(self) -> str:
        """Get the connector type identifier."""
        return "openai"
    
    def getModels(self) -> List[AiModel]:
        """Get all available OpenAI models."""
        return [
            AiModel(
                name="gpt-4o",
                displayName="OpenAI GPT-4o",
                connectorType="openai",
                apiUrl="https://api.openai.com/v1/chat/completions",
                temperature=0.2,
                maxTokens=16384,
                contextLength=128000,
                maxInputTokensPerRequest=25000,  # OpenAI org TPM limit is 30K, keep 5K buffer
                costPer1kTokensInput=0.0025,  # $2.50/M tokens (updated 2026-02)
                costPer1kTokensOutput=0.01,  # $10.00/M tokens (updated 2026-02)
                speedRating=8,  # Good speed for complex tasks
                qualityRating=10,  # High quality
                functionCall=self.callAiBasic,
                priority=PriorityEnum.BALANCED,
                processingMode=ProcessingModeEnum.ADVANCED,
                operationTypes=createOperationTypeRatings(
                    (OperationTypeEnum.PLAN, 9),
                    (OperationTypeEnum.DATA_ANALYSE, 10),
                    (OperationTypeEnum.DATA_GENERATE, 10),
                    (OperationTypeEnum.DATA_EXTRACT, 7)
                ),
                version="gpt-4o",
                calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.0025 + (bytesReceived / 4 / 1000) * 0.01
            ),
            AiModel(
                name="gpt-4o-mini",
                displayName="OpenAI GPT-4o Mini",
                connectorType="openai",
                apiUrl="https://api.openai.com/v1/chat/completions",
                temperature=0.2,
                maxTokens=16384,
                contextLength=128000,
                maxInputTokensPerRequest=25000,  # OpenAI org TPM limit, keep buffer
                costPer1kTokensInput=0.00015,  # $0.15/M tokens (updated 2026-02)
                costPer1kTokensOutput=0.0006,  # $0.60/M tokens (updated 2026-02)
                speedRating=9,  # Very fast
                qualityRating=8,  # Good quality, replaces gpt-3.5-turbo
                functionCall=self.callAiBasic,
                priority=PriorityEnum.SPEED,
                processingMode=ProcessingModeEnum.BASIC,
                operationTypes=createOperationTypeRatings(
                    (OperationTypeEnum.PLAN, 8),
                    (OperationTypeEnum.DATA_ANALYSE, 8),
                    (OperationTypeEnum.DATA_GENERATE, 9),
                    (OperationTypeEnum.DATA_EXTRACT, 7)
                ),
                version="gpt-4o-mini",
                calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.00015 + (bytesReceived / 4 / 1000) * 0.0006
            ),
            AiModel(
                name="gpt-4o",
                displayName="OpenAI GPT-4o Vision",
                connectorType="openai",
                apiUrl="https://api.openai.com/v1/chat/completions",
                temperature=0.2,
                maxTokens=16384,
                contextLength=128000,
                maxInputTokensPerRequest=25000,  # OpenAI org TPM limit is 30K, keep 5K buffer
                costPer1kTokensInput=0.0025,  # $2.50/M tokens (updated 2026-02)
                costPer1kTokensOutput=0.01,  # $10.00/M tokens (updated 2026-02)
                speedRating=6,  # Slower for vision tasks
                qualityRating=9,  # High quality vision
                functionCall=self.callAiImage,
                priority=PriorityEnum.QUALITY,
                processingMode=ProcessingModeEnum.DETAILED,
                operationTypes=createOperationTypeRatings(
                    (OperationTypeEnum.IMAGE_ANALYSE, 9)
                ),
                version="gpt-4o",
                calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.0025 + (bytesReceived / 4 / 1000) * 0.01
            ),
            AiModel(
                name="dall-e-3",
                displayName="OpenAI DALL-E 3",
                connectorType="openai",
                apiUrl="https://api.openai.com/v1/images/generations",
                temperature=0.0,  # Image generation doesn't use temperature
                maxTokens=0,  # Image generation doesn't use tokens
                contextLength=0,
                costPer1kTokensInput=0.04,
                costPer1kTokensOutput=0.0,
                speedRating=5,  # Slow for image generation
                qualityRating=9,  # High quality art generation
                # capabilities removed (not used in business logic)
                functionCall=self.generateImage,
                priority=PriorityEnum.QUALITY,
                processingMode=ProcessingModeEnum.DETAILED,
                operationTypes=createOperationTypeRatings(
                    (OperationTypeEnum.IMAGE_GENERATE, 10)
                ),
                version="dall-e-3",
                calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.04
            )
        ]
    
    async def callAiBasic(self, modelCall: AiModelCall) -> AiModelResponse:
        """
        Calls the OpenAI API with the given messages using standardized pattern.
        
        Args:
            modelCall: AiModelCall with messages and options
            
        Returns:
            AiModelResponse with content and metadata
            
        Raises:
            HTTPException: For errors in API communication
        """
        try:
            # Extract parameters from modelCall
            messages = modelCall.messages
            model = modelCall.model
            options = modelCall.options
            temperature = getattr(options, "temperature", None)
            if temperature is None:
                temperature = model.temperature
            maxTokens = model.maxTokens
            
            payload = {
                "model": model.name,
                "messages": messages,
                "temperature": temperature,
                "max_tokens": maxTokens
            }
            
            response = await self.httpClient.post(
                model.apiUrl,
                json=payload
            )
            
            if response.status_code != 200:
                error_message = f"OpenAI API error: {response.status_code} - {response.text}"
                logger.error(error_message)
                
                # Check for rate limit exceeded (429 TPM)
                if response.status_code == 429:
                    try:
                        error_data = response.json()
                        error_msg = error_data.get("error", {}).get("message", "Rate limit exceeded")
                        raise RateLimitExceededException(
                            f"Rate limit exceeded for {model.name}: {error_msg}"
                        )
                    except (ValueError, KeyError):
                        raise RateLimitExceededException(
                            f"Rate limit exceeded for {model.name}"
                        )
                
                # Check for context length exceeded error
                if response.status_code == 400:
                    try:
                        error_data = response.json()
                        if (error_data.get("error", {}).get("code") == "context_length_exceeded" or
                            "context length" in error_data.get("error", {}).get("message", "").lower()):
                            # Raise a specific exception for context length issues
                            raise ContextLengthExceededException(
                                f"Context length exceeded: {error_data.get('error', {}).get('message', 'Unknown error')}"
                            )
                    except (ValueError, KeyError):
                        pass  # If we can't parse the error, fall through to generic error
                
                # Include the actual error details in the exception
                raise HTTPException(status_code=500, detail=error_message)
            
            responseJson = response.json()
            content = responseJson["choices"][0]["message"]["content"]
            
            return AiModelResponse(
                content=content,
                success=True,
                modelId=model.name,
                metadata={"response_id": responseJson.get("id", "")}
            )
            
        except ContextLengthExceededException:
            # Re-raise context length exceptions without wrapping
            raise
        except Exception as e:
            logger.error(f"Error calling OpenAI API: {str(e)}")
            raise HTTPException(status_code=500, detail=f"Error calling OpenAI API: {str(e)}")
    
    async def callAiImage(self, modelCall: AiModelCall) -> AiModelResponse:
        """
        Analyzes an image with the OpenAI Vision API using standardized pattern.
        
        Args:
            modelCall: AiModelCall with messages and image data in options
            
        Returns:
            AiModelResponse with analysis content
        """
        try:
            # Extract parameters from modelCall
            messages = modelCall.messages
            model = modelCall.model
            
            # Messages should already be in the correct format with image data embedded
            # Just verify they contain image data
            if not messages or not messages[0].get("content"):
                raise ValueError("No messages provided for image analysis")
            
            logger.debug(f"Starting image analysis with {len(messages)} message(s)...")
            
            # Use the messages directly - they should already contain the image data
            # in the format: {"type": "image_url", "image_url": {"url": "data:...base64,..."}}
            
            # Use parameters from model
            temperature = model.temperature
            # Don't set maxTokens - let the model use its full context length
            
            payload = {
                "model": model.name,
                "messages": messages,
                "temperature": temperature
            }
            
            response = await self.httpClient.post(
                model.apiUrl,
                json=payload
            )
            
            if response.status_code != 200:
                logger.error(f"OpenAI API error: {response.status_code} - {response.text}")
                raise HTTPException(status_code=500, detail="Error communicating with OpenAI API")
            
            responseJson = response.json()
            content = responseJson["choices"][0]["message"]["content"]
            
            return AiModelResponse(
                content=content,
                success=True,
                modelId=model.name,
                metadata={"response_id": responseJson.get("id", "")}
            )
        
        except Exception as e:
            logger.error(f"Error during image analysis: {str(e)}", exc_info=True)
            return AiModelResponse(
                content="",
                success=False,
                error=f"Error during image analysis: {str(e)}"
            )

    async def generateImage(self, modelCall: AiModelCall) -> AiModelResponse:
        """
        Generate an image using DALL-E 3 using standardized pattern.
        
        Args:
            modelCall: AiModelCall with messages and generation options
            
        Returns:
            AiModelResponse with generated image data
        """
        try:
            # Extract parameters from modelCall
            messages = modelCall.messages
            model = modelCall.model
            options = modelCall.options
            
            # Get prompt from messages
            promptContent = messages[0]["content"] if messages else ""
            
            # Parse prompt using AiCallPromptImage model
            import json
            
            try:
                # Try to parse as JSON
                promptData = json.loads(promptContent)
                promptModel = AiCallPromptImage(**promptData)
            except:
                # If not JSON, use plain text prompt
                promptModel = AiCallPromptImage(
                    prompt=promptContent,
                    size=options.size if options and hasattr(options, 'size') else "1024x1024",
                    quality=options.quality if options and hasattr(options, 'quality') else "standard",
                    style=options.style if options and hasattr(options, 'style') else "vivid"
                )
            
            # Extract parameters from Pydantic model
            prompt = promptModel.prompt
            size = promptModel.size or "1024x1024"
            quality = promptModel.quality or "standard"
            style = promptModel.style or "vivid"
            
            logger.debug(f"Starting image generation with prompt: '{prompt[:100]}...'")
            
            # DALL-E 3 API endpoint
            dalle_url = "https://api.openai.com/v1/images/generations"
            
            payload = {
                "model": "dall-e-3",
                "prompt": prompt,
                "size": size,
                "quality": quality,
                "style": style,
                "n": 1,
                "response_format": "b64_json"  # Get base64 data directly instead of URLs
            }
            
            # Use existing httpClient to benefit from connection pooling
            # This avoids TLS connection issues that can occur with fresh clients
            response = await self.httpClient.post(
                dalle_url,
                json=payload
            )
            
            if response.status_code != 200:
                logger.error(f"DALL-E API error: {response.status_code} - {response.text}")
                return AiModelResponse(
                    content="",
                    success=False,
                    error=f"DALL-E API error: {response.status_code} - {response.text}"
                )
            
            responseJson = response.json()
            
            if "data" in responseJson and len(responseJson["data"]) > 0:
                image_data = responseJson["data"][0]["b64_json"]
                
                logger.info(f"Successfully generated image: {len(image_data)} characters")
                return AiModelResponse(
                    content=image_data,
                    success=True,
                    modelId="dall-e-3",
                    metadata={
                        "size": size,
                        "quality": quality,
                        "style": style,
                        "response_id": responseJson.get("id", "")
                    }
                )
            else:
                logger.error("No image data in DALL-E response")
                return AiModelResponse(
                    content="",
                    success=False,
                    error="No image data in DALL-E response"
                )
        
        except Exception as e:
            logger.error(f"Error during image generation: {str(e)}", exc_info=True)
            return AiModelResponse(
                content="",
                success=False,
                error=f"Error during image generation: {str(e)}"
            )