from __future__ import annotations

import time
from typing import Dict, List, Optional
import uuid

from fastapi import FastAPI, Request
from pydantic import BaseModel, Field

from .logging_utils import get_logger

logger = get_logger("api")

# Import BM25 utilities
try:
    from .bm25_utils import search_bm25
    BM25_AVAILABLE = True
except ImportError as e: 
    logger.warning(f"BM25 not available: {e}")
    BM25_AVAILABLE = False

# Import FAISS utilities
try:
    from .modern_bert_utils import search_modernbert
    FAISS_AVAILABLE = True
    logger.info("✓ FAISS search loaded successfully")
except ImportError as e:
    logger.error(f"FAISS import failed: {e}")  # ← Change to error so it's visible
    FAISS_AVAILABLE = False
    search_modernbert = None  # ← Define it as None
except Exception as e:  # ← Catch other errors too
    logger.error(f"Unexpected error loading FAISS: {e}")
    FAISS_AVAILABLE = False
    search_modernbert = None

# Import LLM link for explanations
try:
    from .llm_utils import explain_results
except ImportError as e:
    logger.warning(f"LLM not available: {e}")


app = FastAPI(title="Off-the-Beaten-Path Travel API")

# Middleware for logging requests
@app.middleware("http")
async def log_requests(request: Request, call_next):
    start_time = time.time()
    request_id = request.headers.get("X-Request-ID", str(uuid.uuid4()))

    response = await call_next(request)
    process_time = (time.time() - start_time) * 1000
    
    log_data = {
        "request_id": request_id,
        "method": request.method,
        "path": request.url.path,
        "status_code": response.status_code,
        "duration_ms": round(process_time, 2)
    }
    
    logger.info("Request processed", extra={"props": log_data})
    
    return response

# Add event handler to preload bm25 index data to limit search time
@app.on_event("startup")
async def startup_event():
    """Preload BM25 index on API startup."""
    if BM25_AVAILABLE:
        logger.info("Preloading BM25 index...")
        try:
            from .bm25_utils import _load_blogs_from_db
            _load_blogs_from_db()
            logger.info("✓ BM25 index preloaded and ready!")
        except Exception as e:
            logger.error(f"✗ Failed to preload BM25 index: {e}")


# ----------------------------
# Models
# ----------------------------

class Retrieval(BaseModel):
    model: str = Field(pattern="^(bm25|faiss)$")
    k: int = 12


class SearchRequest(BaseModel):
    query: str
    retrieval: Retrieval
    ui: Optional[Dict] = None
    llm_explanations: bool = False


class Result(BaseModel):
    destination: str
    country: str
    lat: Optional[float] = None
    lon: Optional[float] = None
    score: Optional[float] = None
    distance: Optional[float] = None
    trend_delta: Optional[float] = None
    context_cues: Dict[str, Dict[str, int]] = {}
    snippets: List[str] = []
    full_content: str
    why: Dict[str, object] = {}


class SearchResponse(BaseModel):
    query: str
    params: Dict[str, object]
    results: List[Result]
    explanations: List[str]


# ----------------------------
# Utility functions
# ----------------------------

def generate_explanations(req: SearchRequest, results):
    q = req.query
    explanations = []
    for r in results[0:3]:
        content = r.full_content
        try:
            gen_text = explain_results(q, content)
            explanations.append(gen_text)
        except Exception as e:
            logger.error(f"LLM explanation failed: {e}")
            explanations.append("Explanation unavailable.")
    
    return explanations


# ----------------------------
# Search functions
# ----------------------------

# BM25 Search Handler
def bm25_search(req: SearchRequest) -> List[Result]:
    """Handle BM25 search using the database."""
    if not BM25_AVAILABLE:
        # Return empty results if BM25 not available
        logger.warning("BM25 search requested but model is not available.")
        return []
    
    logger.info(f"Executing BM25 search for query: '{req.query}'")
    # Call the BM25 utility function
    raw_results = search_bm25(req.query, top_n = req.retrieval.k)
    
    logger.info(f"BM25 found {len(raw_results)} raw results")

    results = []
    for r in raw_results:
        if "destination" not in r:
            logger.error("BM25 result missing required field 'destination'", extra={"props": r})
            continue
        # Create snippets from content preview and description
        snippets = []
        if r.get("description"):
            snippets.append(r["description"])
        if r.get("content_preview"):
            snippets.append(r["content_preview"])
        
        results.append(
            Result(
                destination = r["destination"],
                country = r.get("country", ""),
                lat = r.get("lat"),
                lon = r.get("lon"),
                score = round(r["score"], 4),
                trend_delta = None,
                context_cues = {},
                snippets = snippets[:2],  # Limit to 2 snippets
                full_content = r.get('full_content'),
                why = {
                    "model": "BM25",
                    "page_title": r.get("page_title", ""),
                    "page_url": r.get("page_url", ""),
                    "blog_url": r.get("blog_url", ""),
                    "author": r.get("author", ""),
                },
            )
        )
    
    return results

# FAISS Search Handler
def faiss_search(req: SearchRequest) -> List[Result]:
    """Handle FAISS search using the database."""

    logger.info(f"Executing FAISS search for query: '{req.query}'")
    
    # Call the FAISS utility function
    raw_results = search_modernbert(req.query, top_k = req.retrieval.k)
    
    logger.info(f"FAISS found {len(raw_results)} raw results")

    results = []
    for r in raw_results:
        # Create snippets from content preview and description
        snippets = []
        if r.get("description"):
            snippets.append(r["description"])
        if r.get("content_preview"):
            snippets.append(r["content_preview"])
        
        results.append(
            Result(
                destination = r["destination"],
                country = r.get("country", ""),
                lat = r.get("lat"),
                lon = r.get("lon"),
                distance = round(r["distance"], 4),
                trend_delta = None,
                context_cues = {},
                snippets = snippets[:2],  # Limit to 2 snippets
                full_content = r.get('full_content'),
                why = {
                    "model": "FAISS",
                    "page_title": r.get("page_title", ""),
                    "page_url": r.get("page_url", ""),
                    "blog_url": r.get("blog_url", ""),
                    "author": r.get("author", ""),
                },
            )
        )
    
    return results

# ----------------------------
# API
# ----------------------------
@app.get("/health")
def health():
    return {
        "status": f"ok {BM25_AVAILABLE} {FAISS_AVAILABLE}",
        "bm25_model_available": BM25_AVAILABLE,
        "faiss_search_available": FAISS_AVAILABLE,
    }

@app.get("/stats")
def get_database_stats():
    """Get database statistics for EDA"""
    from sqlalchemy import func, create_engine
    from sqlalchemy.orm import Session
    from .bm25_utils import Whole_Blogs  # Import inside function
    import os
    
    # Create engine here
    DATABASE_URL = os.getenv("DATABASE_URL")
    if not DATABASE_URL:
        from fastapi import HTTPException
        raise HTTPException(status_code=500, detail="DATABASE_URL not configured")
    
    engine = create_engine(DATABASE_URL)
    
    try:
        with Session(engine) as session:
            total_posts = session.query(func.count(Whole_Blogs.id)).scalar()
            unique_locations = session.query(func.count(func.distinct(Whole_Blogs.location_name))).scalar()
            unique_blogs = session.query(func.count(func.distinct(Whole_Blogs.blog_url))).scalar()
            unique_authors = session.query(func.count(func.distinct(Whole_Blogs.page_author))).scalar()
            
            # Sample coordinates
            coordinates = session.query(
                Whole_Blogs.location_name,
                Whole_Blogs.latitude,
                Whole_Blogs.longitude,
                func.count(Whole_Blogs.id).label('cnt')
            ).filter(
                Whole_Blogs.latitude.isnot(None),
                Whole_Blogs.longitude.isnot(None)
            ).group_by(
                Whole_Blogs.location_name,
                Whole_Blogs.latitude,
                Whole_Blogs.longitude
            ).all()
            
            logger.info(f"Stats requested: {total_posts} posts, {unique_locations} locations")
            
            return {
                "total_posts": total_posts,
                "unique_locations": unique_locations,
                "unique_blogs": unique_blogs,
                "unique_authors": unique_authors,
                "coordinates": [
                        {
                        "location": loc,
                        "lat": float(lat), 
                        "lon": float(lon),
                        "count": cnt
                        } for loc, lat, lon, cnt in coordinates
                ]
            }
    except Exception as e:
        logger.error(f"Database stats error: {e}")
        from fastapi import HTTPException
        raise HTTPException(status_code=500, detail=str(e))


@app.post("/search", response_model=SearchResponse)
def search(req: SearchRequest):
    """
    Return a search result based on type of search
    Either BM25 or FAISS
    """
    
    # Route to BM25 if selected
    if req.retrieval.model == "bm25":
        results = bm25_search(req)
        explanations = generate_explanations(req, results)  if req.llm_explanations else []

        return SearchResponse(
            query = req.query,
            params = {
                "retrieval": req.retrieval.model_dump(),
                "model_used": "bm25",
            },
            results = results,
            explanations = explanations
        )
    
    # Route to FAISS if selected
    if req.retrieval.model == "faiss":
        results = faiss_search(req)
        explanations = generate_explanations(req, results) if req.llm_explanations else []

        return SearchResponse(
            query = req.query,
            params = {
                "retrieval": req.retrieval.model_dump(),
                "model_used": "faiss",
            },
            results = results,
            explanations = explanations
        )