# Copyright (c) 2025 Patrick Motsch
# All rights reserved.
"""
Workflow execution models for action definitions, AI responses, and workflow-level structures.
"""

from typing import Dict, Any, List, Optional, TYPE_CHECKING
from pydantic import BaseModel, Field
from modules.shared.i18nRegistry import i18nModel
from modules.shared.jsonUtils import extractJsonString, tryParseJson, repairBrokenJson

# Import DocumentReferenceList at runtime (needed for ActionDefinition)
from modules.datamodels.datamodelDocref import DocumentReferenceList

@i18nModel("Aktionsdefinition")
class ActionDefinition(BaseModel):
    """Action definition with selection and parameters from planning phase"""
    
    # Core action selection (Stage 1)
    action: str = Field(description="Compound action name (method.action)", json_schema_extra={"label": "Aktion"})
    actionObjective: str = Field(description="Objective for this action", json_schema_extra={"label": "Aktionsziel"})
    userMessage: Optional[str] = Field(
        None,
        description="User-friendly message in user's language explaining what this action will do (generated by AI in prompts)",
        json_schema_extra={"label": "Benutzernachricht"},
    )
    parametersContext: Optional[str] = Field(
        None,
        description="Context for parameter generation",
        json_schema_extra={"label": "Parameter-Kontext"},
    )
    learnings: List[str] = Field(
        default_factory=list,
        description="Learnings from previous actions",
        json_schema_extra={"label": "Erkenntnisse"},
    )
    
    # Resources (ALWAYS defined in Stage 1 if action needs them)
    documentList: Optional[DocumentReferenceList] = Field(
        None,
        description="Document references (ALWAYS defined in Stage 1 if action needs documents)",
        json_schema_extra={"label": "Dokumentenliste"},
    )
    connectionReference: Optional[str] = Field(
        None,
        description="Connection reference (ALWAYS defined in Stage 1 if action needs connection)",
        json_schema_extra={"label": "Verbindungsreferenz"},
    )
    
    # Parameters (may be defined in Stage 1 OR Stage 2, depending on action and actionObjective)
    parameters: Optional[Dict[str, Any]] = Field(
        None,
        description="Action-specific parameters (generated in Stage 2 for complex actions, or inferred from actionObjective for simple actions)",
        json_schema_extra={"label": "Parameter"},
    )
    
    def hasParameters(self) -> bool:
        """Check if parameters have been generated (Stage 2 complete or inferred)"""
        return self.parameters is not None
    
    def needsStage2(self) -> bool:
        """Determine if Stage 2 parameter generation is needed (generic, deterministic check)
        
        Generic logic (works for any action, dynamically added or removed):
        - If parameters are already set → Stage 2 not needed
        - If parameters are None → Stage 2 needed (to generate parameters from actionObjective and context)
        
        Note: Stage 1 always defines documentList and connectionReference if the action needs them.
        Stage 2 only generates the action-specific parameters dictionary.
        """
        # Generic check: if parameters are not set, Stage 2 is needed
        return self.parameters is None
    
    def updateFromStage1StringReferences(self, stringRefs: Optional[List[str]], connectionRef: Optional[str]):
        """Update documentList and connectionReference from Stage 1 string references
        
        Called when Stage 1 AI returns string references that need to be converted to typed models.
        """
        if stringRefs:
            self.documentList = DocumentReferenceList.from_string_list(stringRefs)
        if connectionRef:
            self.connectionReference = connectionRef


@i18nModel("KI-Antwort-Metadaten")
class AiResponseMetadata(BaseModel):
    """Metadata for AI response (varies by operation type)."""
    
    # Document Generation Metadata
    title: Optional[str] = Field(None, description="Document title", json_schema_extra={"label": "Titel"})
    filename: Optional[str] = Field(None, description="Document filename", json_schema_extra={"label": "Dateiname"})
    
    # Operation-Specific Metadata
    operationType: Optional[str] = Field(None, description="Type of operation performed", json_schema_extra={"label": "Vorgangstyp"})
    schemaVersion: Optional[str] = Field(
        None,
        description="Schema version (e.g., 'parameters_v1')",
        alias="schema",
        json_schema_extra={"label": "Schema-Version"},
    )
    extractionMethod: Optional[str] = Field(None, description="Method used for extraction", json_schema_extra={"label": "Extraktionsmethode"})
    sourceDocuments: Optional[List[str]] = Field(None, description="Source document references", json_schema_extra={"label": "Quelldokumente"})
    
    # Additional metadata (for extensibility)
    additionalData: Optional[Dict[str, Any]] = Field(
        None,
        description="Additional operation-specific metadata",
        json_schema_extra={"label": "Zusätzliche Daten"},
    )


@i18nModel("Dokumentdaten")
class DocumentData(BaseModel):
    """Single document in response"""
    documentName: str = Field(description="Document name", json_schema_extra={"label": "Dokumentname"})
    documentData: Any = Field(description="Document data (can be str, bytes, dict, etc.)", json_schema_extra={"label": "Dokumentdaten"})
    mimeType: str = Field(description="MIME type of the document", json_schema_extra={"label": "MIME-Typ"})
    sourceJson: Optional[Dict[str, Any]] = Field(
        None,
        description="Source JSON structure (preserved when rendering to xlsx/docx/pdf)",
        json_schema_extra={"label": "Quell-JSON"},
    )


@i18nModel("Extraktionsparameter")
class ExtractContentParameters(BaseModel):
    """Parameters for extraction action.
    
    This model is defined together with the `methodAi.extractContent()` action function.
    All action parameter models follow this pattern: defined in the same module as the action.
    However, since this is a workflow-level model used across the system, it's defined here.
    """
    documentList: DocumentReferenceList = Field(
        description="Document references to extract content from",
        json_schema_extra={"label": "Dokumentenliste"},
    )
    extractionOptions: Optional[Any] = Field(  # ExtractionOptions - forward reference
        None,
        description="Extraction options (determined dynamically based on task and document characteristics)",
        json_schema_extra={"label": "Extraktionsoptionen"},
    )


@i18nModel("KI-Antwort")
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)",
        json_schema_extra={"label": "Inhalt"},
    )
    metadata: Optional[AiResponseMetadata] = Field(
        None,
        description="Response metadata (varies by operation type)",
        json_schema_extra={"label": "Metadaten"},
    )
    documents: Optional[List[DocumentData]] = Field(
        None,
        description="Generated documents (only for document generation operations)",
        json_schema_extra={"label": "Dokumente"},
    )
    
    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.
        
        Uses methods from jsonUtils:
        - tryParseJson() - Safe parsing with error handling
        - repairBrokenJson() - Repairs broken/incomplete JSON
        - extractJsonString() - Extracts JSON from text with code fences
        
        Returns:
            Dict containing the parsed JSON content, or a safe fallback structure if parsing fails.
            - If content is valid JSON dict: returns the dict directly
            - If content is valid JSON list: wraps in {"data": [...]}
            - If content is broken JSON: attempts repair using repairBrokenJson()
            - If all parsing fails: returns {"content": "...", "parseError": True}
        """
        # If content is already a dict, return it directly
        if isinstance(self.content, dict):
            return self.content
        
        # If content is already a list, wrap it
        if isinstance(self.content, list):
            return {"data": self.content}
        
        # Convert to string if needed
        contentStr = str(self.content) if not isinstance(self.content, str) else self.content
        
        # First, try to extract JSON from text (handles code fences, etc.)
        extractedJson = extractJsonString(contentStr)
        
        # Try to parse as JSON (returns tuple: obj, error, cleaned_str)
        parsedJson, parseError, _ = tryParseJson(extractedJson)
        
        if parsedJson is not None and parseError is None:
            # If it's a dict, return directly
            if isinstance(parsedJson, dict):
                return parsedJson
            # If it's a list, wrap in dict
            elif isinstance(parsedJson, list):
                return {"data": parsedJson}
        
        # Try to repair broken JSON
        repairedJson = repairBrokenJson(contentStr)
        if repairedJson:
            # repairBrokenJson returns Optional[Dict[str, Any]] - always a dict or None
            if isinstance(repairedJson, dict):
                return repairedJson
        
        # All parsing failed - return safe fallback
        contentStr = str(self.content) if not isinstance(self.content, str) else self.content
        return {"content": contentStr, "parseError": True}


# Workflow-level models

@i18nModel("Anfragekontext")
class RequestContext(BaseModel):
    """Normalized request context from user input"""
    
    originalPrompt: str = Field(description="Original user prompt", json_schema_extra={"label": "Ursprüngliche Eingabe"})
    documents: List[Any] = Field(  # ChatDocument - forward reference
        default_factory=list,
        description="Documents provided by user",
        json_schema_extra={"label": "Dokumente"},
    )
    userLanguage: str = Field(description="User's language", json_schema_extra={"label": "Benutzersprache"})
    detectedComplexity: str = Field(
        description="Complexity level: simple, moderate, complex",
        json_schema_extra={"label": "Erkannte Komplexität"},
    )
    requiresDocuments: bool = Field(default=False, description="Whether request requires documents", json_schema_extra={"label": "Benötigt Dokumente"})
    requiresWebResearch: bool = Field(default=False, description="Whether request requires web research", json_schema_extra={"label": "Benötigt Web-Recherche"})
    requiresAnalysis: bool = Field(default=False, description="Whether request requires analysis", json_schema_extra={"label": "Benötigt Analyse"})
    expectedOutputFormat: Optional[str] = Field(None, description="Expected output format", json_schema_extra={"label": "Erwartetes Ausgabeformat"})
    expectedOutputType: Optional[str] = Field(None, description="Expected output type: answer, document, analysis", json_schema_extra={"label": "Erwarteter Ausgabetyp"})


@i18nModel("Verständnis-Ergebnis")
class UnderstandingResult(BaseModel):
    """Result from initial understanding phase (combined AI call)"""
    
    parameters: Dict[str, Any] = Field(
        default_factory=dict,
        description="Basic parameters (language, format, detail level)",
        json_schema_extra={"label": "Parameter"},
    )
    intention: Dict[str, Any] = Field(
        default_factory=dict,
        description="User intention (primaryGoal, secondaryGoals, intentionType)",
        json_schema_extra={"label": "Absicht"},
    )
    context: Dict[str, Any] = Field(
        default_factory=dict,
        description="Extracted context (topics, requirements, constraints)",
        json_schema_extra={"label": "Kontext"},
    )
    documentReferences: List[Dict[str, Any]] = Field(
        default_factory=list,
        description="Document references with purpose and relevance",
        json_schema_extra={"label": "Dokumentenreferenzen"},
    )
    tasks: List["TaskDefinition"] = Field(  # Forward reference
        default_factory=list,
        description="Task definitions with deliverables",
        json_schema_extra={"label": "Aufgaben"},
    )


@i18nModel("Aufgabenbeschreibung")
class TaskDefinition(BaseModel):
    """Task definition from understanding phase"""
    
    id: str = Field(description="Task identifier", json_schema_extra={"label": "Aufgaben-ID"})
    objective: str = Field(description="Task objective", json_schema_extra={"label": "Ziel"})
    deliverable: Dict[str, Any] = Field(
        description="Deliverable specification (type, format, style, detailLevel)",
        json_schema_extra={"label": "Lieferobjekt"},
    )
    requiresWebResearch: bool = Field(default=False, description="Whether task requires web research", json_schema_extra={"label": "Benötigt Web-Recherche"})
    requiresDocumentAnalysis: bool = Field(default=False, description="Whether task requires document analysis", json_schema_extra={"label": "Benötigt Dokumentenanalyse"})
    requiresContentGeneration: bool = Field(default=True, description="Whether task requires content generation", json_schema_extra={"label": "Benötigt Inhaltserstellung"})
    requiredDocuments: List[str] = Field(
        default_factory=list,
        description="Document references needed for this task",
        json_schema_extra={"label": "Benötigte Dokumente"},
    )
    extractionOptions: Optional[Any] = Field(  # ExtractionOptions - forward reference
        None,
        description="Extraction options for document processing (determined dynamically based on task and document characteristics)",
        json_schema_extra={"label": "Extraktionsoptionen"},
    )


@i18nModel("Workflow-Aufgabenergebnis")
class WorkflowTaskResult(BaseModel):
    """Result from task execution"""
    
    taskId: str = Field(description="Task identifier", json_schema_extra={"label": "Aufgaben-ID"})
    actionResult: Any = Field(description="ActionResult from task execution", json_schema_extra={"label": "Aktionsergebnis"})  # ActionResult - forward reference