# Copyright (c) 2025 Patrick Motsch
# All rights reserved.
import logging
import json as _json
import httpx
from typing import List, Dict, Any, AsyncGenerator, Union
from fastapi import HTTPException
from modules.shared.configuration import APP_CONFIG
from .aicoreBase import BaseConnectorAi, RateLimitExceededException, ContextLengthExceededException
from modules.datamodels.datamodelAi import AiModel, PriorityEnum, ProcessingModeEnum, OperationTypeEnum, AiModelCall, AiModelResponse, createOperationTypeRatings

logger = logging.getLogger(__name__)

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

class AiMistral(BaseConnectorAi):
    """Connector for communication with the Mistral AI API (Le Chat Mistral)."""
    
    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("Mistral Connector initialized")
    
    def getConnectorType(self) -> str:
        """Get the connector type identifier."""
        return "mistral"
    
    def getModels(self) -> List[AiModel]:
        """Get all available Mistral models."""
        return [
            AiModel(
                name="mistral-large-latest",
                displayName="Mistral Large 3",
                connectorType="mistral",
                apiUrl="https://api.mistral.ai/v1/chat/completions",
                temperature=0.2,
                maxTokens=16384,
                contextLength=256000,
                costPer1kTokensInput=0.0005,  # $0.50/M tokens (updated 2026-02)
                costPer1kTokensOutput=0.0015,  # $1.50/M tokens (updated 2026-02)
                speedRating=8,  # Good speed for complex tasks
                qualityRating=9,  # High quality
                functionCall=self.callAiBasic,
                functionCallStream=self.callAiBasicStream,
                priority=PriorityEnum.BALANCED,
                processingMode=ProcessingModeEnum.ADVANCED,
                operationTypes=createOperationTypeRatings(
                    (OperationTypeEnum.PLAN, 9),
                    (OperationTypeEnum.DATA_ANALYSE, 9),
                    (OperationTypeEnum.DATA_GENERATE, 9),
                    (OperationTypeEnum.DATA_EXTRACT, 8),
                    (OperationTypeEnum.AGENT, 8),
                ),
                version="mistral-large-latest",
                calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.0005 + (bytesReceived / 4 / 1000) * 0.0015
            ),
            AiModel(
                name="mistral-small-latest",
                displayName="Mistral Small 3.2",
                connectorType="mistral",
                apiUrl="https://api.mistral.ai/v1/chat/completions",
                temperature=0.2,
                maxTokens=16384,
                contextLength=128000,
                costPer1kTokensInput=0.00006,  # $0.06/M tokens (updated 2026-02)
                costPer1kTokensOutput=0.00018,  # $0.18/M tokens (updated 2026-02)
                speedRating=9,  # Very fast, lightweight model
                qualityRating=7,  # Good quality, cost-efficient
                functionCall=self.callAiBasic,
                functionCallStream=self.callAiBasicStream,
                priority=PriorityEnum.SPEED,
                processingMode=ProcessingModeEnum.BASIC,
                operationTypes=createOperationTypeRatings(
                    (OperationTypeEnum.PLAN, 7),
                    (OperationTypeEnum.DATA_ANALYSE, 7),
                    (OperationTypeEnum.DATA_GENERATE, 8),
                    (OperationTypeEnum.DATA_EXTRACT, 7),
                    (OperationTypeEnum.AGENT, 6),
                ),
                version="mistral-small-latest",
                calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.00006 + (bytesReceived / 4 / 1000) * 0.00018
            ),
            AiModel(
                name="mistral-embed",
                displayName="Mistral Embed",
                connectorType="mistral",
                apiUrl="https://api.mistral.ai/v1/embeddings",
                temperature=0.0,
                maxTokens=0,
                contextLength=8192,
                costPer1kTokensInput=0.0001,  # $0.10/M tokens
                costPer1kTokensOutput=0.0,
                speedRating=10,
                qualityRating=7,
                functionCall=self.callEmbedding,
                priority=PriorityEnum.COST,
                processingMode=ProcessingModeEnum.BASIC,
                operationTypes=createOperationTypeRatings(
                    (OperationTypeEnum.EMBEDDING, 8)
                ),
                version="mistral-embed",
                calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.0001
            ),
            AiModel(
                name="mistral-large-latest",
                displayName="Mistral Large 3 Vision",
                connectorType="mistral",
                apiUrl="https://api.mistral.ai/v1/chat/completions",
                temperature=0.2,
                maxTokens=16384,
                contextLength=256000,
                costPer1kTokensInput=0.0005,  # $0.50/M tokens (updated 2026-02)
                costPer1kTokensOutput=0.0015,  # $1.50/M tokens (updated 2026-02)
                speedRating=6,  # Slower for vision tasks
                qualityRating=8,  # Good quality vision
                functionCall=self.callAiImage,
                priority=PriorityEnum.QUALITY,
                processingMode=ProcessingModeEnum.DETAILED,
                operationTypes=createOperationTypeRatings(
                    (OperationTypeEnum.IMAGE_ANALYSE, 8)
                ),
                version="mistral-large-latest",
                calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.0005 + (bytesReceived / 4 / 1000) * 0.0015
            )
        ]
    
    async def callAiBasic(self, modelCall: AiModelCall) -> AiModelResponse:
        """
        Calls the Mistral AI API with the given messages using standardized pattern.
        
        Mistral's chat completions API is OpenAI-compatible: it accepts the same
        message format (role/content) including system messages, and returns
        responses in the same choices[0].message.content structure.
        
        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
            }

            if modelCall.tools:
                payload["tools"] = modelCall.tools
                payload["tool_choice"] = modelCall.toolChoice or "auto"

            response = await self.httpClient.post(
                model.apiUrl,
                json=payload
            )
            
            if response.status_code != 200:
                error_message = f"Mistral 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() or
                            "too many tokens" in error_data.get("error", {}).get("message", "").lower()):
                            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()
            choiceMessage = responseJson["choices"][0]["message"]
            content = choiceMessage.get("content") or ""

            metadata = {"response_id": responseJson.get("id", "")}
            if choiceMessage.get("tool_calls"):
                metadata["toolCalls"] = choiceMessage["tool_calls"]

            return AiModelResponse(
                content=content,
                success=True,
                modelId=model.name,
                metadata=metadata,
            )

        except ContextLengthExceededException:
            # Re-raise context length exceptions without wrapping
            raise
        except RateLimitExceededException:
            # Re-raise rate limit exceptions without wrapping
            raise
        except Exception as e:
            logger.error(f"Error calling Mistral API: {str(e)}")
            raise HTTPException(status_code=500, detail=f"Error calling Mistral API: {str(e)}")

    async def callAiBasicStream(self, modelCall: AiModelCall) -> AsyncGenerator[Union[str, AiModelResponse], None]:
        """Stream Mistral response. Yields str deltas, then final AiModelResponse."""
        try:
            model = modelCall.model
            options = modelCall.options
            temperature = getattr(options, "temperature", None)
            if temperature is None:
                temperature = model.temperature

            payload: Dict[str, Any] = {
                "model": model.name,
                "messages": modelCall.messages,
                "temperature": temperature,
                "max_tokens": model.maxTokens,
                "stream": True,
            }

            if modelCall.tools:
                payload["tools"] = modelCall.tools
                payload["tool_choice"] = modelCall.toolChoice or "auto"

            fullContent = ""
            toolCallsAccum: Dict[int, Dict[str, Any]] = {}

            async with self.httpClient.stream("POST", model.apiUrl, json=payload) as response:
                if response.status_code != 200:
                    body = await response.aread()
                    bodyStr = body.decode()
                    if response.status_code == 429:
                        try:
                            errorMsg = _json.loads(bodyStr).get("error", {}).get("message", "Rate limit exceeded")
                        except (ValueError, KeyError):
                            errorMsg = f"Rate limit exceeded for {model.name}"
                        raise RateLimitExceededException(f"Rate limit exceeded for {model.name}: {errorMsg}")
                    raise HTTPException(status_code=500, detail=f"Mistral stream error: {response.status_code} - {bodyStr}")

                async for line in response.aiter_lines():
                    if not line.startswith("data: "):
                        continue
                    data = line[6:]
                    if data.strip() == "[DONE]":
                        break
                    try:
                        chunk = _json.loads(data)
                    except _json.JSONDecodeError:
                        continue

                    delta = chunk.get("choices", [{}])[0].get("delta", {})
                    if "content" in delta and delta["content"]:
                        fullContent += delta["content"]
                        yield delta["content"]

                    for tcDelta in delta.get("tool_calls", []):
                        idx = tcDelta.get("index", 0)
                        if idx not in toolCallsAccum:
                            toolCallsAccum[idx] = {
                                "id": tcDelta.get("id", ""),
                                "type": "function",
                                "function": {"name": "", "arguments": ""},
                            }
                        if tcDelta.get("id"):
                            toolCallsAccum[idx]["id"] = tcDelta["id"]
                        fn = tcDelta.get("function", {})
                        if fn.get("name"):
                            toolCallsAccum[idx]["function"]["name"] = fn["name"]
                        if fn.get("arguments"):
                            toolCallsAccum[idx]["function"]["arguments"] += fn["arguments"]

            metadata: Dict[str, Any] = {}
            if toolCallsAccum:
                metadata["toolCalls"] = [toolCallsAccum[i] for i in sorted(toolCallsAccum)]

            yield AiModelResponse(
                content=fullContent,
                success=True,
                modelId=model.name,
                metadata=metadata,
            )

        except (RateLimitExceededException, ContextLengthExceededException, HTTPException):
            raise
        except Exception as e:
            logger.error(f"Error streaming Mistral API: {e}")
            raise HTTPException(status_code=500, detail=f"Error streaming Mistral API: {e}")

    async def callEmbedding(self, modelCall: AiModelCall) -> AiModelResponse:
        """Generate embeddings via the Mistral Embeddings API.
        
        Reads texts from modelCall.embeddingInput.
        Returns vectors in metadata["embeddings"].
        """
        try:
            model = modelCall.model
            texts = modelCall.embeddingInput or []
            if not texts:
                return AiModelResponse(
                    content="", success=False, error="No embeddingInput provided"
                )

            payload = {"model": model.name, "input": texts}
            response = await self.httpClient.post(model.apiUrl, json=payload)

            if response.status_code != 200:
                errorMessage = f"Mistral Embedding API error: {response.status_code} - {response.text}"
                logger.error(errorMessage)
                if response.status_code == 429:
                    raise RateLimitExceededException(f"Rate limit exceeded for {model.name}")
                if response.status_code == 400:
                    try:
                        errorData = response.json()
                        errMsg = errorData.get("error", {}).get("message", "").lower()
                        errCode = errorData.get("error", {}).get("code", "")
                        if errCode == "context_length_exceeded" or "too many tokens" in errMsg or "maximum context length" in errMsg:
                            raise ContextLengthExceededException(
                                f"Embedding context length exceeded for {model.name}: {errorData.get('error', {}).get('message', '')}"
                            )
                    except (ValueError, KeyError):
                        pass
                raise HTTPException(status_code=500, detail=errorMessage)

            responseJson = response.json()
            embeddings = [item["embedding"] for item in responseJson["data"]]
            usage = responseJson.get("usage", {})

            return AiModelResponse(
                content="",
                success=True,
                modelId=model.name,
                tokensUsed={
                    "input": usage.get("prompt_tokens", 0),
                    "output": 0,
                    "total": usage.get("total_tokens", 0),
                },
                metadata={"embeddings": embeddings},
            )
        except (RateLimitExceededException, ContextLengthExceededException):
            raise
        except Exception as e:
            logger.error(f"Error calling Mistral Embedding API: {str(e)}")
            raise HTTPException(status_code=500, detail=f"Error calling Mistral Embedding API: {str(e)}")

    async def callAiImage(self, modelCall: AiModelCall) -> AiModelResponse:
        """
        Analyzes an image with the Mistral Vision API using standardized pattern.
        
        Mistral Large 3 is multimodal and accepts image inputs in OpenAI-compatible
        format: {"type": "image_url", "image_url": {"url": "data:...base64,..."}}
        
        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,..."}}
            # Mistral Large 3 supports this OpenAI-compatible vision format natively
            
            # Use parameters from model
            temperature = model.temperature
            
            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"Mistral API error: {response.status_code} - {response.text}")
                raise HTTPException(status_code=500, detail="Error communicating with Mistral 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)}"
            )
