from datetime import datetime
from typing import List, Optional
from uuid import UUID
from enum import Enum
from pydantic import Field, field_validator, BaseModel
from fastapi import UploadFile

from src.models import DefaultBase, Pagination

equipment_keywords = ["boiler", "turbine", "generator", "condenser", "pump", "valve", "motor", "transformer", "compressor", "fan", "heater", "cooler", "pipe", "tubes", "panel", "switchgear", "relay", "breaker", "bearing", "gearbox", "chiller", "evaporator", "conveyor", "filter", "separator", "reactor", "furnace", "burner", "nozzle", "rotor", "stator", "shaft", "seal", "coupling", "damper", "duct", "expander", "economizer", "superheater", "deaerator", "feedwater", "drum", "tank", "vessel", "silo", "hopper", "agitator", "mixer", "blower", "injector", "strainer", "screen", "scrubber", "stack", "chimney", "insulator", "busbar", "rectifier", "inverter", "panel", "switch", "sensor", "transmitter", "controller", "PLC", "DCS", "SCADA", "soot_blower", "economizer"]

class FlowType(str, Enum):
    LLM_ONLY = "llm_only"
    ORIGINAL = "rag_retrieval_first"
    LLM_FIRST = "rag_llm_first"
    BATCH_SUMMARIZE = "rag_batch_summarize"
    AGENTIC = "rag_agentic"

class ChatMessageBase(DefaultBase):
    role: str = Field(..., description="Role of the message sender (e.g., 'user', 'assistant')")
    content: str = Field(..., description="Content of the chat message")
    created_at: Optional[datetime] = Field(None, nullable=True)
    updated_at: Optional[datetime] = Field(None, nullable=True)
    created_by: Optional[str] = Field(None, nullable=True)
    updated_by: Optional[str] = Field(None, nullable=True)

class ChatMessageCreate(ChatMessageBase):
    pass

class ChatMessageUpdate(ChatMessageBase):
    pass

class ChatMessageRead(ChatMessageBase):
    id: UUID = Field(..., description="Unique identifier for the chat message")

class ChatMessagePagination(Pagination):
    items: List[ChatMessageRead] = Field([], description="List of chat messages with pagination support")

class ChatMessageRead(ChatMessageBase):
    id: UUID = Field(..., description="Unique identifier for the chat message")

class ChatMessagePagination(Pagination):
    items: List[ChatMessageRead] = Field([], description="List of chat messages with pagination support")

class FileAttachment(DefaultBase):
    filename: str
    url: str
    size: Optional[int] = None
    content_type: Optional[str] = None

class ChatMessage(DefaultBase):
    role: str
    content: str
    attachments: Optional[List[FileAttachment]] = Field(default_factory=list, description="Optional list of attached files")

class ChatRequest(DefaultBase):
    conversation_id: Optional[UUID] = None
    message_id: Optional[UUID] = None
    message: Optional[str] = None
    equipment_id: Optional[UUID] = None
    attachments: Optional[List[UploadFile]] = None
    flow_type: Optional[FlowType] = Field(default=FlowType.ORIGINAL, description="Type of processing flow to use")
    questions_batch: Optional[List[str]] = Field(default=None, description="List of questions for batch processing (only used with batch_summarize flow)")
    model: Optional[str] = Field(default=None, description="LLM model name to use")
    context_state: Optional[dict] = Field(default=None, description="Current context state (symptoms, failure modes, etc.)")
    enable_background_analysis: Optional[bool] = Field(default=True, description="Toggle to enable/disable background RCFA analysis")

class Performance(DefaultBase):
    prompt_tokens: Optional[int] = None
    response_tokens: Optional[int] = None
    tokens_used: Optional[int] = None
    response_time: Optional[float] = None
    response_length: Optional[int] = None

class ChatResponse(DefaultBase):
    conversation_id: UUID
    message: str
    user_message: str
    context: Optional[List] = None
    attachments: Optional[List] = None
    sources: Optional[List] = None
    sources_filepath: Optional[List] = None
    timestamp: str
    response_timestamp: str
    chat_model: str
    time_taken: Optional[float] = None  # float, seconds
    prompt_tokens: Optional[float] = None
    response_tokens: Optional[float] = None
    tokens_used: Optional[float] = None

class ChatHistory(DefaultBase):
    id: str
    user_id: str
    message: str
    attachments: Optional[List] = None  # Store as comma-separated text array
    attachments_filepath: Optional[List] = None  # Store as comma-separated text array
    response: str
    context: Optional[List] = None
    sources: Optional[List] = None
    sources_filepath: Optional[List] = None  # Store as comma-separated text array
    time_taken: Optional[float] = None
    prompt_tokens: Optional[float] = None
    response_tokens: Optional[float] = None
    tokens_used: Optional[float] = None
    timestamp: Optional[datetime] = None
    response_timestamp: Optional[datetime] = None
    conversation_id: str
    chat_model: Optional[str] = None
    embedding_model: Optional[str] = None
    ocr_model: Optional[str] = None
    feedback: Optional[str] = None
    feedback_comment: Optional[str] = None
    feedback_screenshot: Optional[str] = None
    last_location_tag: Optional[str] = None
    context_info: Optional[dict] = None
    references: Optional[List[dict]] = None
    updated_at: Optional[datetime] = None
    created_at: Optional[datetime] = None

    @field_validator('user_id', 'conversation_id', mode='before')
    @classmethod
    def convert_uuid_to_str(cls, v):
        return str(v) if v else None

class ChatHistoryMetadata(BaseModel):
    rcfa_phase: str
    rcfa_state: dict
    title: str

class ChatHistoryResponse(BaseModel):
    messages: List[ChatHistory]
    metadata: ChatHistoryMetadata

class Conversation(DefaultBase):
    user_id: str
    conversation_id: str
    conversation_title: str
    created_at: Optional[datetime] = None
    updated_at: Optional[datetime] = None
    is_exported: bool = False
    exported_at: Optional[datetime] = None
    report_name: Optional[str] = None
    report_content: Optional[str] = None
    rcfa_phase: Optional[str] = None
    rcfa_state: Optional[dict] = None

    @field_validator('user_id', 'conversation_id', mode='before')
    @classmethod
    def convert_uuid_to_str(cls, v):
        return str(v) if v else None

class ConversationExportUpdate(BaseModel):
    is_exported: bool = True
    report_name: str
    report_content: str

class ConversationUpdate(DefaultBase):
    conversation_title: str = Field(..., description="New title for the conversation")

class ConversationDataPagination(Pagination):
    items: List[Conversation] = Field([], description="List of conversations with pagination support")

class IntentDetection(BaseModel):
    intent: str = Field(
        description="Identifikasi intensi dari masukkan pengguna dengan pendekatan 'Intent Classification'. Identifikasi masukkan pengguna berdasarkan dengan enum.",
        enum=[
            "DefinisiKegagalan",
            "DefinisiEquipment",
            "DeskripsiDokumen",
            "Mendeskripsikan",
            "JelaskanGambar",
            "GejalaKegagalan",
            "LokasiKegagalan",
            "TindakanMitigasi",
            "StrategiPerawatan",
            "JenisSensor",
            "StandarAmbangBatas",
            "TipsDiagnostik",
            "RingkasanKeputusan",
            "RincianManfaatTahunan",
            "AnalisisEkonomi",
            "AnalisisKejadianUtama",
            "AkibatJikaTidakDiatasi",
            "EstimasiBiayaPenanggulangan",
            "InformasiLaporan",
            "Lainnya",
            "FileAttachment"  # Added intent for file attachment
        ]
    )
    equipment_name: str = Field(
        description="Nama equipment yang dibahas atau dimasukkan oleh pengguna. Jika tidak ada nama equipment yang relevan, berikan string kosong dengan respon ''.",
        enum=equipment_keywords
    )
    function_call: str = Field(
        description="Kebutuhan untuk melakukan function call jika masukkan pengguna terkait data reliability atau sensor equipment. Data reliability equipment terdiri dari nilai reliability, MTTR, MTBF, MDT, failure rate, next failure, dan jumlah failure historis equipment. Sedangkan data sensor terdiri dari nama sensor, status sensor, dan value sensor per equipment. Identifikasi hasil berdasarkan enum",
        enum=["Reliability", "MTTR", "MTBF", "Sensor", "MDT"]
    )
    confidence: float = Field(description="Level kepercayaan hasil dari identifikasi (0-1).")
    location_tag: float = Field(description="Unique indentifier equipment.")

class ClassifierResponse(BaseModel):
    intent_detection: IntentDetection
    response: str

class UpdateFeedback(BaseModel):
    update: str  # "good" | "bad"
    comment: Optional[str] = None
    screenshot: Optional[str] = None  # base64 compressed image

class EvaluateRequest(DefaultBase):
    response_id: UUID
    user_id: UUID
    user_message: str
    ai_response: str
    context: Optional[List[str]] = None
    ground_truth: str

class EvaluateResponse(DefaultBase):
    faithfullness: float
    answer_relevance: float
    context_recall: float
    context_relevancy: float

class RootCauseRequest(BaseModel):
    equipment_type: Optional[str] = Field(None, description="Tipe dari equipment, contoh: centrifugal_pump")
    problem: Optional[str] = Field(None, description="Masalah spesifik yang dialami, contoh: high_vibration")

class RootCauseResponse(BaseModel):
    equipment_type: str
    problem_observed: Optional[str] = None
    possible_causes_to_check: Optional[List[str]] = None
    available_problems: Optional[List[str]] = None
    available_equipment_types: Optional[List[str]] = None
    instruction_for_llm: str
    error: Optional[str] = None
class ModelRead(BaseModel):
    name: str
    display_name: str
    description: Optional[str] = None
    provider: str

class AdminModelRead(BaseModel):
    name: str
    display_name: str
    model: str
    model_server: str
    api_key: str
    provider: str
    description: Optional[str] = None
    stream: bool
    enabled: bool
    is_default: bool
    generate_cfg: Optional[dict] = None

class ModelConfigCreate(BaseModel):
    name: str
    display_name: str
    model: str
    model_server: str
    api_key: Optional[str] = "not-needed"
    provider: Optional[str] = "openai"
    description: Optional[str] = None
    stream: Optional[bool] = True
    enabled: Optional[bool] = True
    generate_cfg: Optional[dict] = None

class ModelConfigUpdate(BaseModel):
    display_name: str
    model: str
    model_server: str
    api_key: Optional[str] = "not-needed"
    provider: Optional[str] = "openai"
    description: Optional[str] = None
    stream: Optional[bool] = True
    enabled: Optional[bool] = True
    generate_cfg: Optional[dict] = None

class MemoryRead(DefaultBase):
    """A private, user-owned long-term memory shown in the Memory Lab."""

    id: UUID
    conversation_id: Optional[UUID] = None
    source_type: str
    title: Optional[str] = None
    summary: str
    excerpt: Optional[str] = None
    location_tag: Optional[str] = None
    equipment: Optional[str] = None
    confidence: Optional[float] = None
    created_at: Optional[datetime] = None


class MemoryLabSnapshot(DefaultBase):
    """Safe developer-facing view of the current user's private memories."""

    items: List[MemoryRead] = Field(default_factory=list)
    total: int = 0
    retrieval_limit: int = 50
    explicit_only: bool = True
    user_private: bool = True
    secrets_blocked: bool = True