import logging
import sys
import os
import secrets
from enum import Enum
from typing import List, Optional, Dict, Any
from fastapi import FastAPI, HTTPException, BackgroundTasks, Request, status
from fastapi.responses import RedirectResponse
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field, validator
import uvicorn
import msal

# Đảm bảo đường dẫn module
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from chat.rag_engine import RAGEngine
from core.config import settings
from core.models import IngestedDocument, ProcessingPolicy
from ingestion.providers.sharepoint_provider import SharePointProvider
from ingestion.sync import SyncEngine
from extraction.dce import DocumentClassificationEngine
from extraction.ocr_service import OCRService
from extraction.text_extractor import TextExtractor
from chunking.markdown_chunker import MarkdownChunker
from indexing.vector_store import VectorStore

# --- Cấu hình Logging chuyên nghiệp ---
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S"
)
logger = logging.getLogger("RAG_API")

app = FastAPI(
    title="Enterprise SharePoint RAG API",
    description="Hệ thống hỏi đáp AI nội bộ sử dụng kiến trúc Modular Providers và Distributed VLM.",
    version="1.1.0",
    docs_url="/docs",
    redoc_url="/redoc"
)

# Thêm cấu hình CORS để Frontend có thể gọi API
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"], # Cho phép tất cả nguồn (hợp lý cho bản PoC)
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# --- Singleton Engine Instance ---
rag_engine = None
sync_status = {"running": False, "last_run": None, "processed": 0, "skipped": 0, "errors": []}

# --- Azure AD SSO Config ---
REDIRECT_URI = "http://localhost:8000/auth/callback"
AUTHORITY = f"https://login.microsoftonline.com/{settings.tenant_id}"
SCOPE = ["User.Read"]

def _build_msal_app():
    return msal.ConfidentialClientApplication(
        settings.client_id,
        authority=AUTHORITY,
        client_credential=settings.client_secret,
    )

@app.on_event("startup")
async def startup_event():
    global rag_engine
    try:
        logger.info(f"Đang khởi tạo RAG Engine với Provider: {settings.llm_provider}")
        # Đảm bảo host OpenSearch đúng trong môi trường dev
        if settings.opensearch_host == "opensearch" and os.environ.get("ENV") != "docker":
            settings.opensearch_host = "localhost"
            
        rag_engine = RAGEngine()
        
        # Thông báo sẵn sàng kèm địa chỉ truy cập
        host = "0.0.0.0"
        port = 8000
        logger.info("="*60)
        logger.info("🚀 RAG ENGINE ĐÃ SẴN SÀNG PHỤC VỤ!")
        logger.info(f"🔗 API Endpoint: http://localhost:{port}")
        logger.info(f"📖 Swagger UI:  http://localhost:{port}/docs")
        logger.info(f"📊 Health Check: http://localhost:{port}/health")
        logger.info("="*60)
    except Exception as e:
        logger.critical(f"❌ THẤT BẠI khi khởi động RAG Engine: {e}")

# --- SCHEMAS ---

class ChatRole(str, Enum):
    user = "user"
    assistant = "assistant"
    system = "system"

class ChatHistoryItem(BaseModel):
    role: ChatRole = Field(..., description="Vai trò của người gửi (user/assistant)")
    content: str = Field(..., min_length=1, description="Nội dung tin nhắn")

class ChatRequest(BaseModel):
    query: str = Field(
        ..., 
        min_length=2, 
        max_length=1000, 
        example="Quy trình bảo trì thiết bị là gì?",
        description="Câu hỏi của người dùng"
    )
    history: List[ChatHistoryItem] = Field(
        default_factory=list,
        description="Lịch sử cuộc trò chuyện để duy trì ngữ cảnh"
    )

class SourceCitation(BaseModel):
    file_name: str
    page: int
    url: Optional[str] = None

class ChatResponse(BaseModel):
    answer: str = Field(..., description="Câu trả lời từ AI")
    sources: List[SourceCitation] = Field(default_factory=list, description="Danh sách các nguồn trích dẫn từ tài liệu")
    context_used: Optional[str] = Field(None, description="Ngữ cảnh thực tế đã được trích xuất từ VectorDB (Dùng cho Debug/UI)")

class SyncResponse(BaseModel):
    status: str
    message: str

class LoginRequest(BaseModel):
    email: str = Field(..., description="Email người dùng")

class LoginResponse(BaseModel):
    email: str
    display_name: str
    role: str

# --- ENDPOINTS ---

@app.get("/health", tags=["System"])
async def health_check():
    """Kiểm tra trạng thái kết nối tới OpenSearch và LLM."""
    return {
        "status": "online" if rag_engine else "offline",
        "engine_ready": rag_engine is not None,
        "config": {
            "provider": settings.llm_provider,
            "opensearch_host": settings.opensearch_host
        }
    }

@app.get("/auth/login", tags=["Auth"])
async def sso_login():
    """
    Redirect sang Azure AD login page.
    Dùng chung App Registration với SharePoint ingestion.
    """
    msal_app = _build_msal_app()
    auth_url = msal_app.get_authorization_request_url(
        SCOPE,
        redirect_uri=REDIRECT_URI,
        state=secrets.token_hex(16)
    )
    return RedirectResponse(url=auth_url)

@app.get("/auth/callback", tags=["Auth"])
async def sso_callback(request: Request):
    """
    Azure AD redirect về đây với authorization code.
    Đổi code lấy token, lấy thông tin user, redirect về frontend.
    """
    code = request.query_params.get("code")
    if not code:
        raise HTTPException(status_code=400, detail="Missing authorization code")
    
    msal_app = _build_msal_app()
    result = msal_app.acquire_token_by_authorization_code(
        code,
        scopes=SCOPE,
        redirect_uri=REDIRECT_URI
    )
    
    if "error" in result:
        logger.error(f"SSO error: {result.get('error_description', result.get('error'))}")
        raise HTTPException(status_code=401, detail="Authentication failed")
    
    # Lấy thông tin user từ token
    id_token_claims = result.get("id_token_claims", {})
    email = id_token_claims.get("preferred_username", id_token_claims.get("email", ""))
    name = id_token_claims.get("name", email.split("@")[0])
    oid = id_token_claims.get("oid", "")
    
    # Xác định role
    role = "admin" if "admin" in email.lower() else "user"
    
    logger.info(f"SSO login: {email} (role={role})")
    
    # Redirect về frontend với user info
    import json
    import urllib.parse
    user_data = json.dumps({"email": email, "display_name": name, "role": role})
    encoded = urllib.parse.quote(user_data)
    return RedirectResponse(url=f"http://localhost:8000?user={encoded}")

@app.post("/auth/login-email", response_model=LoginResponse, tags=["Auth"])
async def login_email_endpoint(request: LoginRequest):
    """
    Đăng nhập bằng email (fallback khi không dùng SSO).
    """
    email = request.email.strip().lower()
    if not email or "@" not in email:
        raise HTTPException(status_code=400, detail="Email không hợp lệ.")
    
    local_part = email.split("@")[0]
    display_name = local_part.replace(".", " ").title()
    role = "admin" if "admin" in email else "user"
    
    logger.info(f"Email login: {email} (role={role})")
    
    return LoginResponse(email=email, display_name=display_name, role=role)

@app.post("/chat", response_model=ChatResponse, tags=["RAG"], status_code=status.HTTP_200_OK)
async def chat_endpoint(request: ChatRequest, http_request: Request):
    """
    Điểm cuối xử lý hội thoại RAG.
    Header 'X-User-Email' (optional): Email user để filter quyền.
    Header 'X-User-Role' (optional): "admin" = bypass ACL.
    """
    if not rag_engine:
        raise HTTPException(
            status_code=status.HTTP_503_SERVICE_UNAVAILABLE, 
            detail="Hệ thống RAG đang khởi động hoặc gặp sự cố kết nối Database."
        )
    
    try:
        user_email = http_request.headers.get("X-User-Email")
        user_role = http_request.headers.get("X-User-Role", "user")
        is_admin = user_role == "admin" or not user_email
        
        history_data = [item.dict() for item in request.history]
        
        logger.info(f"Chat query: {request.query[:50]} (user={user_email or 'none'}, role={user_role})")
        result = rag_engine.chat(request.query, history=history_data, user_email=user_email, is_admin=is_admin)
        
        return ChatResponse(
            answer=result["answer"],
            sources=result["sources"],
            context_used=result.get("context_used")
        )
    except Exception as e:
        logger.error(f"Lỗi thực thi RAG: {str(e)}")
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail="Đã xảy ra lỗi nội bộ trong quá trình xử lý ngôn ngữ."
        )


def extract_text_from_pdf_bytes(pdf_bytes: bytes) -> str:
    """Trích xuất text trực tiếp từ PDF có text layer."""
    try:
        import fitz
        doc = fitz.open(stream=pdf_bytes, filetype="pdf")
        return "\n\n".join(page.get_text() for page in doc)
    except Exception:
        return ""


def run_sync_background():
    """Chạy đồng bộ SharePoint → DCE → OCR/Extract → Chunk → Index."""
    global sync_status
    sync_status = {"running": True, "last_run": None, "processed": 0, "skipped": 0, "errors": []}
    
    try:
        provider = SharePointProvider()
        dce = DocumentClassificationEngine(provider=provider)
        ocr = OCRService()
        chunker = MarkdownChunker(max_chunk_size=1000, overlap=100)
        vector_db = VectorStore(index_name="poc_sharepoint_docs")
        
        items, _ = provider.fetch_changes({})
        logger.info(f"Sync: Found {len(items)} items from SharePoint")
        
        for item in items:
            if item.get("is_folder") or item.get("is_deleted"):
                continue
            
            name = item.get("name", "")
            item_id = item.get("id", "")
            
            item_details = provider.get_item_details(item_id)
            permissions = provider.get_item_permissions(item_id)
            doc = IngestedDocument(
                site_id=settings.sharepoint_site_id,
                drive_id="",
                item_id=item_id,
                name=name,
                web_url=item_details.get("web_url", ""),
                download_url=item_details.get("download_url"),
                is_folder=False,
                size=item.get("size", 0),
            )
            
            classification = dce.classify(doc, target_item=item)
            
            if classification.processing_policy in (ProcessingPolicy.UNSUPPORTED, ProcessingPolicy.METADATA_ONLY, ProcessingPolicy.REQUIRES_REVIEW):
                sync_status["skipped"] += 1
                continue
            
            try:
                file_bytes = provider.download_file(item)
            except Exception as e:
                sync_status["errors"].append(f"{name}: download failed")
                continue
            
            if not file_bytes:
                sync_status["errors"].append(f"{name}: empty file")
                continue
            
            pages = []
            ext = name.lower().rsplit(".", 1)[-1] if "." in name else ""
            
            if classification.processing_policy == ProcessingPolicy.SKIP_OCR:
                if ext == "pdf":
                    text = extract_text_from_pdf_bytes(file_bytes)
                    if text.strip():
                        from core.models import OCRPageResult
                        pages = [OCRPageResult(page=1, text=text, confidence=1.0)]
                elif ext in ("docx", "doc"):
                    pages = TextExtractor.extract_from_docx(file_bytes)
                elif ext in ("xlsx", "xls"):
                    pages = TextExtractor.extract_from_xlsx(file_bytes)
                elif ext in ("txt", "md", "csv"):
                    pages = TextExtractor.extract_from_text(file_bytes)
            elif classification.processing_policy == ProcessingPolicy.REQUIRES_OCR:
                pages = ocr.process_pdf_bytes(file_bytes)
            
            if not pages:
                sync_status["skipped"] += 1
                continue
            
            metadata = {
                "item_id": item_id,
                "name": name,
                "web_url": item_details.get("web_url"),
                "download_url": item_details.get("download_url"),
                "site_id": settings.sharepoint_site_id,
                "permissions": permissions
            }
            chunks = chunker.chunk_document(pages, metadata)
            
            if chunks:
                vector_db.delete_by_file_id(item_id)
                vector_db.embed_and_index(chunks)
                sync_status["processed"] += 1
                logger.info(f"Sync: Indexed {name} → {len(chunks)} chunks")
            else:
                sync_status["skipped"] += 1
        
        sync_status["last_run"] = "completed"
        logger.info(f"Sync completed: {sync_status['processed']} processed, {sync_status['skipped']} skipped")
        
    except Exception as e:
        sync_status["last_run"] = "failed"
        sync_status["errors"].append(str(e))
        logger.error(f"Sync failed: {e}")
    finally:
        sync_status["running"] = False


@app.post("/sync", response_model=SyncResponse, tags=["Ingestion"])
async def sync_endpoint(background_tasks: BackgroundTasks):
    """
    Trigger đồng bộ dữ liệu từ SharePoint.
    Chạy trong background, trả về trạng thái ngay lập tức.
    """
    if sync_status["running"]:
        return SyncResponse(status="already_running", message="Đồng bộ đang chạy, vui lòng đợi.")
    
    background_tasks.add_task(run_sync_background)
    return SyncResponse(status="started", message="Đồng bộ đã bắt đầu trong background.")


@app.get("/sync/status", tags=["Ingestion"])
async def sync_status_endpoint():
    """Kiểm tra trạng thái đồng bộ."""
    return sync_status

if __name__ == "__main__":
    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)