from typing import Optional, List, Dict, Any, Callable, TYPE_CHECKING, Tuple
from pydantic import BaseModel, Field, ConfigDict
from enum import Enum

# Import ContentPart for runtime use (needed for Pydantic model rebuilding)
from modules.datamodels.datamodelExtraction import ContentPart
# Import JSON utilities for safe conversion
from modules.shared.jsonUtils import extractJsonString, tryParseJson, repairBrokenJson

# Operation Types
class OperationTypeEnum(str, Enum):

    # Planning Operation
    PLAN = "plan"

    # Data Operations
    DATA_ANALYSE = "dataAnalyse"
    DATA_GENERATE = "dataGenerate"
    DATA_EXTRACT = "dataExtract"

    # Image Operations
    IMAGE_ANALYSE = "imageAnalyse"
    IMAGE_GENERATE = "imageGenerate"

    # Web Operations
    WEB_SEARCH = "webSearch"    # Returns list of URLs only
    WEB_CRAWL = "webCrawl"      # Web crawl for a given URL


# Operation Type Rating - Helper class for capability ratings
class OperationTypeRating(BaseModel):
    """Represents an operation type with its capability rating (1-10)."""
    operationType: OperationTypeEnum = Field(description="The operation type")
    rating: int = Field(ge=1, le=10, description="Capability rating (1-10, higher = better for this operation type)")
    
    def __str__(self) -> str:
        return f"{self.operationType.value}({self.rating})"
    
    def __repr__(self) -> str:
        return f"OperationTypeRating({self.operationType.value}, {self.rating})"


# Helper function to create operation type ratings easily
def createOperationTypeRatings(*ratings: Tuple[OperationTypeEnum, int]) -> List[OperationTypeRating]:
    """
    Helper function to create operation type ratings easily.
    
    Usage:
        operationTypes = createOperationTypeRatings(
            (OperationTypeEnum.DATA_ANALYSE, 8),
            (OperationTypeEnum.WEB_SEARCH, 10),
            (OperationTypeEnum.WEB_CRAWL, 9)
        )
    """
    return [OperationTypeRating(operationType=ot, rating=rating) for ot, rating in ratings]


# Processing Modes
class ProcessingModeEnum(str, Enum):
    BASIC = "basic"
    ADVANCED = "advanced"
    DETAILED = "detailed"

# Priority Levels
class PriorityEnum(str, Enum):
    SPEED = "speed"
    QUALITY = "quality"
    COST = "cost"
    BALANCED = "balanced"


# Model Capabilities - REMOVED: Not used in business logic

    
class AiModel(BaseModel):
    """Enhanced AI model definition with dynamic capabilities."""
    
    # Core identification
    name: str = Field(description="Actual LLM model name used for API calls")
    displayName: str = Field(description="Human-readable model name with module prefix")
    connectorType: str = Field(description="Type of connector (openai, anthropic, perplexity, tavily, etc.)")
    
    # API configuration
    apiUrl: str = Field(description="API endpoint URL for this model")
    temperature: float = Field(default=0.2, ge=0.0, le=2.0, description="Default temperature for this model")
    
    # Token and context limits
    maxTokens: int = Field(description="Maximum tokens this model can generate")
    contextLength: int = Field(description="Maximum context length this model can handle")
    
    # Cost information
    costPer1kTokensInput: float = Field(default=0.0, description="Cost per 1000 input tokens")
    costPer1kTokensOutput: float = Field(default=0.0, description="Cost per 1000 output tokens")
    
    # Performance ratings
    speedRating: int = Field(ge=1, le=10, description="Speed rating (1-10, higher = faster)")
    qualityRating: int = Field(ge=1, le=10, description="Quality rating (1-10, higher = better)")

    # Function reference (not serialized)
    functionCall: Optional[Callable] = Field(default=None, exclude=True, description="Function to call for this model")
    calculatePriceUsd: Optional[Callable] = Field(default=None, exclude=True, description="Function to calculate price in USD")
    
    # Selection criteria - capabilities with ratings
    priority: PriorityEnum = Field(default=PriorityEnum.BALANCED, description="Default priority for this model. See PriorityEnum for available values.")
    processingMode: ProcessingModeEnum = Field(default=ProcessingModeEnum.BASIC, description="Default processing mode. See ProcessingModeEnum for available values.")
    operationTypes: List[OperationTypeRating] = Field(default=[], description="Operation types this model can handle with capability ratings (1-10)")
    minContextLength: Optional[int] = Field(default=None, description="Minimum context length required")
    isAvailable: bool = Field(default=True, description="Whether model is currently available")
    
    # Metadata
    version: Optional[str] = Field(default=None, description="Model version")
    lastUpdated: Optional[str] = Field(default=None, description="Last update timestamp")
    
    model_config = ConfigDict(arbitrary_types_allowed=True)  # Allow Callable type


class SelectionRule(BaseModel):
    """A rule for model selection."""
    name: str = Field(description="Rule name identifier")
    condition: str = Field(description="Description of when this rule applies")
    weight: float = Field(description="Weight for scoring (higher = more important)")
    operationTypes: List[OperationTypeEnum] = Field(description="Operation types this rule applies to")
    priority: PriorityEnum = Field(default=PriorityEnum.BALANCED, description="Priority level for this rule")
    minQualityRating: Optional[int] = Field(default=None, description="Minimum quality rating")
    maxCost: Optional[float] = Field(default=None, description="Maximum cost threshold")
    minContextLength: Optional[int] = Field(default=None, description="Minimum context length required")


class AiCallOptions(BaseModel):
    """Options for centralized AI processing with clear operation types and tags."""
    operationType: OperationTypeEnum = Field(default=OperationTypeEnum.DATA_ANALYSE, description="Type of operation")
    priority: PriorityEnum = Field(default=PriorityEnum.BALANCED, description="Priority level")
    compressPrompt: bool = Field(default=True, description="Whether to compress the prompt")
    compressContext: bool = Field(default=True, description="If False: process each chunk; If True: summarize and work on summary")
    processDocumentsIndividually: bool = Field(default=True, description="If True, process each document separately; else pool docs")
    maxCost: Optional[float] = Field(default=None, description="Max cost budget")
    maxProcessingTime: Optional[int] = Field(default=None, description="Max processing time in seconds")
    processingMode: ProcessingModeEnum = Field(default=ProcessingModeEnum.BASIC, description="Processing mode")
    resultFormat: Optional[str] = Field(default=None, description="Expected result format: txt, json, csv, xml, etc.")
    
    safetyMargin: float = Field(default=0.1, ge=0.0, le=0.5, description="Safety margin for token limits (0.0-0.5)")
    
    # Model generation parameters
    temperature: Optional[float] = Field(default=None, ge=0.0, le=2.0, description="Temperature for response generation (0.0-2.0, lower = more consistent)")
    maxParts: Optional[int] = Field(default=1000, ge=1, le=1000, description="Maximum number of continuation parts to fetch")
    

class AiCallRequest(BaseModel):
    """Centralized AI call request payload for interface use."""

    prompt: str = Field(description="The user prompt")
    context: Optional[str] = Field(default=None, description="Optional external context (e.g., extracted docs)")
    options: AiCallOptions = Field(default_factory=AiCallOptions)
    contentParts: Optional[List['ContentPart']] = None  # NEW: Content parts for model-aware chunking


class AiCallResponse(BaseModel):
    """Standardized AI call response."""

    content: str = Field(description="AI response content")
    modelName: str = Field(description="Selected model name")
    priceUsd: float = Field(default=0.0, description="Calculated price in USD")
    processingTime: float = Field(default=0.0, description="Duration in seconds")
    bytesSent: int = Field(default=0, description="Input data size in bytes")
    bytesReceived: int = Field(default=0, description="Output data size in bytes")
    errorCount: int = Field(default=0, description="0 for success, 1+ for errors")


class AiModelCall(BaseModel):
    """Standardized input for AI model calls."""
    
    messages: List[Dict[str, Any]] = Field(description="Messages in OpenAI format (role, content)")
    model: Optional[AiModel] = Field(default=None, description="The AI model being called")
    options: AiCallOptions = Field(default_factory=AiCallOptions, description="Additional model-specific options")
    
    model_config = ConfigDict(arbitrary_types_allowed=True)


class AiModelResponse(BaseModel):
    """Standardized output from AI model calls."""
    
    content: str = Field(description="The AI response content")
    success: bool = Field(default=True, description="Whether the call was successful")
    error: Optional[str] = Field(default=None, description="Error message if success=False")
    
    # Optional metadata that models can include
    modelId: Optional[str] = Field(default=None, description="Model identifier used")
    processingTime: Optional[float] = Field(default=None, description="Processing time in seconds")
    tokensUsed: Optional[Dict[str, int]] = Field(default=None, description="Token usage (input, output, total)")
    metadata: Optional[Dict[str, Any]] = Field(default=None, description="Additional model-specific metadata")
    
    model_config = ConfigDict(arbitrary_types_allowed=True)


# Structured prompt models for specialized operations

class AiCallPromptWebSearch(BaseModel):
    """Structured prompt format for WEB_SEARCH operation - returns list of URLs."""
    
    instruction: str = Field(description="Search instruction/query for finding relevant URLs")
    country: Optional[str] = Field(default=None, description="Two-digit country code (lowercase, e.g., ch, us, de, fr)")
    maxNumberPages: Optional[int] = Field(default=10, description="Maximum number of pages to search (default: 10)")
    language: Optional[str] = Field(default=None, description="Language code (lowercase, e.g., de, en, fr)")
    researchDepth: Optional[str] = Field(default="general", description="Research depth: fast (maxDepth=1), general (maxDepth=2), deep (maxDepth=3)")


class AiCallPromptWebCrawl(BaseModel):
    """Structured prompt format for WEB_CRAWL operation - crawls ONE specific URL and returns content."""
    
    instruction: str = Field(description="Instruction for what content to extract from URL")
    url: str = Field(description="Single URL to crawl")
    maxDepth: Optional[int] = Field(default=2, description="Maximum number of hops from starting page (default: 2)")
    maxWidth: Optional[int] = Field(default=10, description="Maximum pages to crawl per level (default: 10)")


class AiCallPromptImage(BaseModel):
    """Structured prompt format for image generation."""
    
    prompt: str = Field(description="Text description of the image to generate")
    size: Optional[str] = Field(default="1024x1024", description="Image size (1024x1024, 1792x1024, 1024x1792)")
    quality: Optional[str] = Field(default="standard", description="Image quality (standard, hd)")
    style: Optional[str] = Field(default="vivid", description="Image style (vivid, natural)")


class DocumentData(BaseModel):
    """Single document in response."""
    documentName: str = Field(description="Document name")
    documentData: Any = Field(description="Document data (can be str, bytes, dict, etc.)")
    mimeType: str = Field(description="MIME type of the document")


class AiProcessParameters(BaseModel):
    """Parameters for AI processing action."""
    aiPrompt: str = Field(description="AI instruction prompt")
    contentParts: Optional[List[ContentPart]] = Field(
        None,
        description="Already-extracted content parts (required if documents need to be processed)"
    )
    resultType: str = Field(
        default="txt",
        description="Output file extension (txt, json, pdf, docx, xlsx, etc.)"
    )


class AiResponseMetadata(BaseModel):
    """Metadata for AI response (varies by operation type)."""
    # Document Generation Metadata
    title: Optional[str] = Field(None, description="Document title")
    filename: Optional[str] = Field(None, description="Document filename")
    
    # Operation-Specific Metadata
    operationType: Optional[str] = Field(None, description="Type of operation performed")
    schemaVersion: Optional[str] = Field(None, description="Schema version (e.g., 'parameters_v1')", alias="schema")
    extractionMethod: Optional[str] = Field(None, description="Method used for extraction")
    sourceDocuments: Optional[List[str]] = Field(None, description="Source document references")
    
    # Additional metadata (for extensibility)
    additionalData: Optional[Dict[str, Any]] = Field(None, description="Additional operation-specific metadata")
    
    @classmethod
    def fromDict(cls, data: Optional[Dict[str, Any]]) -> Optional["AiResponseMetadata"]:
        """Create AiResponseMetadata from dict with camelCase field names."""
        if not data or not isinstance(data, dict):
            return None
        
        knownFields = {"title", "filename", "operationType", "schema", "extractionMethod", "sourceDocuments", "additionalData"}
        mappedData = {k: v for k, v in data.items() if k in knownFields}
        
        additionalFields = {k: v for k, v in data.items() if k not in knownFields}
        if additionalFields:
            mappedData["additionalData"] = additionalFields
        
        try:
            return cls(**mappedData)
        except Exception:
            return None


class AiResponse(BaseModel):
    """Unified response from all AI calls (planning, text, documents)."""
    content: str = Field(description="Response content (JSON string for planning, text for analysis, unified JSON for documents)")
    metadata: Optional[AiResponseMetadata] = Field(
        None,
        description="Response metadata (varies by operation type)"
    )
    documents: Optional[List[DocumentData]] = Field(
        None,
        description="Generated documents (only for document generation operations)"
    )
    
    def toJson(self) -> Dict[str, Any]:
        """
        Convert AI response content to JSON using enhanced stabilizing failsafe conversion methods.
        Centralizes AI result to JSON conversion in one place.
        
        Returns:
            Dict containing the parsed JSON content, or a safe fallback structure if parsing fails.
        """
        if not self.content:
            return {}
        
        # Use enhanced stabilizing failsafe JSON conversion methods from jsonUtils
        # First, try to extract and parse JSON using the safe methods
        obj, err, cleaned = tryParseJson(self.content)
        
        if err is None and isinstance(obj, dict):
            # Successfully parsed as dict
            return obj
        elif err is None and isinstance(obj, list):
            # Successfully parsed as list - wrap in dict for consistency
            return {"data": obj}
        
        # If parsing failed, try to repair broken JSON
        repaired = repairBrokenJson(self.content)
        if repaired is not None:
            return repaired
        
        # If all else fails, return a safe structure with the cleaned content
        # Extract JSON string even if it's not fully parseable
        extracted = extractJsonString(self.content)
        if extracted and extracted != self.content:
            # Try one more time with extracted string
            obj, err, _ = tryParseJson(extracted)
            if err is None and isinstance(obj, (dict, list)):
                return obj if isinstance(obj, dict) else {"data": obj}
        
        # Final fallback: return safe structure with raw content
        return {
            "content": self.content,
            "parseError": True
        }