from __future__ import annotations

import ast
import asyncio
import json
import re
import string
import time
from typing import Callable, Dict, List, Optional, Tuple

from bson import ObjectId
from fastapi import HTTPException
from motor.motor_asyncio import AsyncIOMotorCollection
from openai import AsyncOpenAI

from app.core.config import settings
from app.schemas.categories import CategoryPrediction


class AutoCategoryService:
    """Classifies transaction notes into the closest Mongo-backed category."""

    def __init__(
        self,
        collection_getter: Callable[[], AsyncIOMotorCollection],
        subcategory_collection_getter: Callable[[], AsyncIOMotorCollection],
        openai_client: AsyncOpenAI,
        model: str,
        cache_ttl_seconds: int,
        db_timeout_seconds: float,
        model_timeout_seconds: float,
    ) -> None:
        self._collection_getter = collection_getter
        self._subcategory_collection_getter = subcategory_collection_getter
        self._openai_client = openai_client
        self._model = model
        self._cache_ttl_seconds = cache_ttl_seconds
        self._db_timeout_seconds = db_timeout_seconds
        self._model_timeout_seconds = model_timeout_seconds
        
        # User-specific cache for headcategories: {user_id: (data, timestamp)}
        self._headcategories_cache: Dict[str, Tuple[Dict[str, object], float]] = {}
        self._cache_lock = asyncio.Lock()

    def _collection(self) -> AsyncIOMotorCollection:
        return self._collection_getter()

    def _subcategory_collection(self) -> AsyncIOMotorCollection:
        return self._subcategory_collection_getter()

    async def categorize(self, notes: str, user_id: str) -> CategoryPrediction:
        """Categorize transaction notes using a two-step approach:
        1. First match notes to a headcategory title
        2. Then match notes to a category within that headcategory
        """
        # Step 1: Fetch all headcategories for the user (with caching)
        try:
            headcategories_data = await asyncio.wait_for(
                self._get_headcategories_cached(user_id), timeout=self._db_timeout_seconds
            )
        except asyncio.TimeoutError as exc:
            raise HTTPException(status_code=504, detail="Timed out loading headcategories from database.") from exc
        except Exception as exc:
            raise HTTPException(status_code=502, detail="Failed to load headcategories from database.") from exc

        if not headcategories_data or not headcategories_data.get("headcategories"):
            raise HTTPException(status_code=500, detail="No headcategories configured for this user.")

        # Step 2: Use LLM to match notes to a headcategory title
        headcategory_titles = [hc.get("title", "") for hc in headcategories_data["headcategories"]]
        formatted_headcategories = "\n".join([f"- {title}" for title in headcategory_titles if title])
        
        headcategory_prompt = (
            "Transaction note:\n"
            f"{notes}\n\n"
            "Available headcategories:\n"
            f"{formatted_headcategories}\n\n"
            "Respond with the exact headcategory title from the list above that best matches this transaction."
        )

        headcategory_request = dict(
            model=self._model,
            messages=[
                {
                    "role": "system",
                    "content": (
                        "You classify financial transactions into the closest headcategory. "
                        "Only use the provided headcategory title options. "
                        "Output valid JSON with key 'title'."
                    ),
                },
                {"role": "user", "content": headcategory_prompt},
            ],
        )

        try:
            headcategory_response = await asyncio.wait_for(
                self._create_model_response(headcategory_request),
                timeout=self._model_timeout_seconds,
            )
        except asyncio.TimeoutError as exc:
            raise HTTPException(status_code=504, detail="Timed out waiting for headcategory model response.") from exc
        except Exception as exc:
            error_msg = str(exc)
            raise HTTPException(
                status_code=502, 
                detail=f"Failed to call the model API for headcategory: {error_msg}"
            ) from exc

        try:
            headcategory_payload = self._parse_response_payload(headcategory_response)
        except ValueError as exc:
            raise HTTPException(status_code=502, detail="Failed to parse headcategory model output.") from exc

        matched_headcategory_title = headcategory_payload.get("title")
        if not isinstance(matched_headcategory_title, str):
            raise HTTPException(status_code=502, detail="Model response missing headcategory title field.")

        # Step 3: Find the matched headcategory and get its categories (optimized lookup)
        matched_headcategory = None
        matched_title_normalized = self._normalize_string(matched_headcategory_title)
        matched_title_lower = matched_headcategory_title.lower()
        
        # Try exact normalized match first (most common case)
        for hc in headcategories_data["headcategories"]:
            hc_title = hc.get("title", "")
            if self._normalize_string(hc_title) == matched_title_normalized:
                matched_headcategory = hc
                break
        
        # Try partial matching if exact normalized match fails
        if not matched_headcategory:
            for hc in headcategories_data["headcategories"]:
                hc_title = hc.get("title", "").lower()
                if matched_title_lower in hc_title or hc_title in matched_title_lower:
                    matched_headcategory = hc
                    break

        if not matched_headcategory:
            available_titles = ", ".join(headcategory_titles[:10])
            raise HTTPException(
                status_code=502,
                detail=(
                    f"Could not find matching headcategory for title: '{matched_headcategory_title}'. "
                    f"Available headcategories: {available_titles}"
                )
            )

        headcategory_id = matched_headcategory.get("_id")
        category_ids = matched_headcategory.get("category_ids", [])
        
        if not isinstance(headcategory_id, ObjectId):
            raise HTTPException(status_code=500, detail="Invalid headcategory ID format.")
        
        if not category_ids:
            raise HTTPException(status_code=500, detail="Selected headcategory has no categories.")

        # Step 4: Fetch categories from categories collection
        try:
            categories_data = await asyncio.wait_for(
                self._get_categories_by_ids(category_ids), timeout=self._db_timeout_seconds
            )
        except asyncio.TimeoutError as exc:
            raise HTTPException(status_code=504, detail="Timed out loading categories from database.") from exc
        except Exception as exc:
            raise HTTPException(status_code=502, detail="Failed to load categories from database.") from exc

        if not categories_data or not categories_data.get("categories"):
            raise HTTPException(status_code=500, detail="No categories found for the selected headcategory.")

        # Step 5: Use LLM to match notes to a specific category
        category_titles = [cat.get("title", "") for cat in categories_data["categories"]]
        formatted_categories = "\n".join([f"- {title}" for title in category_titles if title])
        
        category_prompt = (
            "Transaction note:\n"
            f"{notes}\n\n"
            "Available categories:\n"
            f"{formatted_categories}\n\n"
            "Respond with the exact category title from the list above that best matches this transaction."
        )

        category_request = dict(
            model=self._model,
            messages=[
                {
                    "role": "system",
                    "content": (
                        "You classify financial transactions into the closest category. "
                        "Only use the provided category title options. "
                        "Output valid JSON with key 'title'."
                    ),
                },
                {"role": "user", "content": category_prompt},
            ],
        )

        try:
            category_response = await asyncio.wait_for(
                self._create_model_response(category_request),
                timeout=self._model_timeout_seconds,
            )
        except asyncio.TimeoutError as exc:
            raise HTTPException(status_code=504, detail="Timed out waiting for category model response.") from exc
        except Exception as exc:
            error_msg = str(exc)
            raise HTTPException(
                status_code=502, 
                detail=f"Failed to call the model API for category: {error_msg}"
            ) from exc

        try:
            category_payload = self._parse_response_payload(category_response)
        except ValueError as exc:
            raise HTTPException(status_code=502, detail="Failed to parse category model output.") from exc

        matched_category_title = category_payload.get("title")
        if not isinstance(matched_category_title, str):
            raise HTTPException(status_code=502, detail="Model response missing category title field.")

        # Step 6: Find the matched category ID (optimized lookup)
        matched_category = None
        matched_cat_title_normalized = self._normalize_string(matched_category_title)
        matched_cat_title_lower = matched_category_title.lower()
        
        # Try exact normalized match first (most common case)
        for cat in categories_data["categories"]:
            cat_title = cat.get("title", "")
            if self._normalize_string(cat_title) == matched_cat_title_normalized:
                matched_category = cat
                break
        
        # Try partial matching if exact normalized match fails
        if not matched_category:
            for cat in categories_data["categories"]:
                cat_title = cat.get("title", "").lower()
                if matched_cat_title_lower in cat_title or cat_title in matched_cat_title_lower:
                    matched_category = cat
                    break

        if not matched_category:
            available_titles = ", ".join(category_titles[:10])
            raise HTTPException(
                status_code=502,
                detail=(
                    f"Could not find matching category for title: '{matched_category_title}'. "
                    f"Available categories: {available_titles}"
                )
            )

        category_id = matched_category.get("_id")
        if not isinstance(category_id, ObjectId):
            raise HTTPException(status_code=500, detail="Invalid category ID format.")

        # Get titles from matched objects
        headcategory_title = matched_headcategory.get("title", "")
        category_title = matched_category.get("title", "")

        return CategoryPrediction(
            headcategory_id=str(headcategory_id),
            headcategory_title=headcategory_title,
            category_id=str(category_id),
            category_title=category_title,
        )

    def _parse_response_payload(self, response) -> Dict[str, object]:
        raw_text = self._extract_response_text(response)
        if not raw_text:
            raise ValueError("Model response did not contain text content.")

        cleaned = self._strip_code_fence(raw_text)
        candidates = [cleaned]

        json_snippet = self._extract_json_snippet(cleaned)
        if json_snippet and json_snippet not in candidates:
            candidates.append(json_snippet)

        for candidate in candidates:
            for parser in (self._try_parse_json, self._try_parse_literal_dict, self._try_parse_key_values):
                payload = parser(candidate)
                if payload:
                    return payload

        raise ValueError("Unable to coerce model response into a payload.")

    @staticmethod
    def _extract_response_text(response) -> str:
        """Extract text from OpenAI API response (supports both Chat Completions and Responses API)."""
        # Try standard Chat Completions API format first
        if hasattr(response, "choices") and response.choices:
            message = response.choices[0].message
            if hasattr(message, "content") and message.content:
                return message.content.strip()
        
        # Try Responses API format
        text = getattr(response, "output_text", "") or ""
        if isinstance(text, str) and text.strip():
            return text.strip()

        outputs = getattr(response, "output", []) or []
        for output in outputs:
            contents = getattr(output, "content", []) or []
            for content in contents:
                value = getattr(content, "text", None)
                if isinstance(value, str) and value.strip():
                    return value.strip()

        return ""

    @staticmethod
    def _strip_code_fence(raw_text: str) -> str:
        text = raw_text.strip()
        if text.startswith("```") and text.endswith("```"):
            lines = text.split("\n")
            # Drop first and last fence line
            if len(lines) >= 2:
                text = "\n".join(lines[1:-1]).strip()
        return text

    @staticmethod
    def _extract_json_snippet(raw_text: str) -> Optional[str]:
        start = raw_text.find("{")
        end = raw_text.rfind("}")
        if start == -1 or end == -1 or end <= start:
            return None
        return raw_text[start : end + 1]

    @staticmethod
    def _try_parse_json(raw_text: str) -> Optional[Dict[str, object]]:
        text = raw_text.strip()
        if not text:
            return None
        try:
            payload = json.loads(text)
        except json.JSONDecodeError:
            return None
        return payload if isinstance(payload, dict) else None

    @staticmethod
    def _try_parse_literal_dict(raw_text: str) -> Optional[Dict[str, object]]:
        try:
            payload = ast.literal_eval(raw_text)
        except (SyntaxError, ValueError):
            return None
        return payload if isinstance(payload, dict) else None

    @staticmethod
    def _try_parse_key_values(raw_text: str) -> Optional[Dict[str, object]]:
        title: Optional[str] = None
        subcategory: Optional[str] = None
        for chunk in re.split(r"[\n;,]+", raw_text):
            if ":" in chunk:
                key, value = chunk.split(":", 1)
            elif "=" in chunk:
                key, value = chunk.split("=", 1)
            else:
                continue
            key_normalized = key.strip().lower()
            value_clean = value.strip().strip('"\'')
            if not value_clean:
                continue
            if key_normalized in {"title", "category"}:
                title = value_clean
            elif key_normalized in {"subcategory", "sub_category", "sub"}:
                subcategory = value_clean

        if title and subcategory:
            return {"title": title, "subcategory": subcategory}

        return None

    async def _get_headcategories_cached(self, user_id: str) -> Dict[str, object]:
        """Fetch headcategories from MongoDB with user-specific caching."""
        async with self._cache_lock:
            now = time.monotonic()
            # Check cache
            if user_id in self._headcategories_cache:
                cached_data, cached_time = self._headcategories_cache[user_id]
                if (now - cached_time) < self._cache_ttl_seconds:
                    return cached_data
                # Cache expired, remove it
                del self._headcategories_cache[user_id]

        # Fetch from database
        data = await self._get_headcategories(user_id)
        
        # Update cache
        async with self._cache_lock:
            self._headcategories_cache[user_id] = (data, time.monotonic())
        
        return data

    async def _get_headcategories(self, user_id: str) -> Dict[str, object]:
        """Fetch headcategories from MongoDB filtered by user_id."""
        head_collection = self._collection()

        # Convert user_id string to ObjectId
        try:
            user_object_id = ObjectId(user_id)
        except Exception as exc:
            raise HTTPException(status_code=400, detail=f"Invalid user_id format: {user_id}") from exc

        # Query headcategories filtered by user_id - only fetch needed fields for performance
        head_docs = await head_collection.find(
            {"user": user_object_id, "categories": {"$type": "array", "$ne": []}},
            {"_id": 1, "title": 1, "categories": 1}  # Only fetch needed fields
        ).to_list(length=1000)

        if not head_docs:
            return {"headcategories": []}

        # Build headcategories structure
        headcategories: List[Dict[str, object]] = []
        for head_doc in head_docs:
            head_id = head_doc.get("_id")
            if not isinstance(head_id, ObjectId):
                continue
                
            category_ids = [cid for cid in (head_doc.get("categories") or []) if isinstance(cid, ObjectId)]
            if not category_ids:
                continue
                
            headcategories.append({
                "_id": head_id,
                "title": head_doc.get("title", ""),
                "category_ids": category_ids,
            })

        return {"headcategories": headcategories}

    async def _get_categories_by_ids(self, category_ids: List[ObjectId]) -> Dict[str, object]:
        """Fetch categories from MongoDB by their ObjectIds."""
        subcategory_collection = self._subcategory_collection()

        if not category_ids:
            return {"categories": []}

        # Query categories collection with the provided ObjectIds
        categories: List[Dict[str, object]] = []
        cursor = subcategory_collection.find(
            {"_id": {"$in": category_ids}}, 
            {"title": 1, "_id": 1}
        )
        async for cat_doc in cursor:
            cat_id = cat_doc.get("_id")
            if isinstance(cat_id, ObjectId):
                categories.append({
                    "_id": cat_id,
                    "title": cat_doc.get("title", ""),
                })

        return {"categories": categories}

    async def _create_model_response(self, request_payload: Dict[str, object]):
        """Create a model response using OpenAI Chat Completions API."""
        try:
            return await self._openai_client.chat.completions.create(
                response_format={"type": "json_object"},
                **request_payload,
            )
        except TypeError as exc:
            # Fallback for older openai-python clients or custom API endpoints
            if "responses" in dir(self._openai_client):
                return await self._openai_client.responses.create(
                    response_format={"type": "json_object"},
                    **request_payload,
                )
            raise

    @staticmethod
    def _format_categories_for_llm(categories: List[Dict[str, object]]) -> str:
        """Format categories for LLM prompt."""
        lines = []
        for category in categories:
            subs = category.get("subcategories") or []
            subs_text = ", ".join([sub.get("title", "") for sub in subs if isinstance(sub, dict)]) if subs else "Unspecified"
            lines.append(f"- {category.get('title', 'Unknown')}: {subs_text}")
        return "\n".join(lines)
    
    @staticmethod
    def _normalize_string(s: str) -> str:
        """Normalize string by removing punctuation and extra spaces for better matching."""
        # Remove punctuation and convert to lowercase
        normalized = s.translate(str.maketrans('', '', string.punctuation)).lower().strip()
        # Replace multiple spaces with single space
        normalized = ' '.join(normalized.split())
        return normalized

    @staticmethod
    def _find_matching_ids(
        categories: List[Dict[str, object]], 
        title: str, 
        subcategory: str
    ) -> tuple[ObjectId | None, ObjectId | None]:
        """Find matching headcategory_id and category_id based on title and subcategory strings.
        
        Uses flexible matching:
        1. Exact match (case-insensitive)
        2. Normalized match (removes punctuation)
        3. Partial match (one contains the other)
        4. Word-based match (checks if key words match)
        """
        title_lower = title.strip().lower()
        subcategory_lower = subcategory.strip().lower()
        title_normalized = AutoCategoryService._normalize_string(title)
        subcategory_normalized = AutoCategoryService._normalize_string(subcategory)
        
        # First pass: exact match
        for category in categories:
            head_title = category.get("title", "").strip().lower()
            if head_title != title_lower:
                continue
            
            subcategories = category.get("subcategories", [])
            for sub in subcategories:
                if isinstance(sub, dict):
                    sub_title = sub.get("title", "").strip().lower()
                    if sub_title == subcategory_lower:
                        headcategory_id = category.get("headcategory_id")
                        category_id = sub.get("_id")
                        if isinstance(headcategory_id, ObjectId) and isinstance(category_id, ObjectId):
                            return headcategory_id, category_id
        
        # Second pass: normalized match (removes punctuation, handles "Wage" vs "Wage, Invoices")
        for category in categories:
            head_title = category.get("title", "").strip().lower()
            head_title_norm = AutoCategoryService._normalize_string(head_title)
            if head_title_norm != title_normalized and title_normalized not in head_title_norm and head_title_norm not in title_normalized:
                continue
            
            subcategories = category.get("subcategories", [])
            for sub in subcategories:
                if isinstance(sub, dict):
                    sub_title = sub.get("title", "").strip().lower()
                    sub_title_norm = AutoCategoryService._normalize_string(sub_title)
                    if (sub_title_norm == subcategory_normalized or 
                        subcategory_normalized in sub_title_norm or 
                        sub_title_norm in subcategory_normalized):
                        headcategory_id = category.get("headcategory_id")
                        category_id = sub.get("_id")
                        if isinstance(headcategory_id, ObjectId) and isinstance(category_id, ObjectId):
                            return headcategory_id, category_id
        
        # Third pass: partial match (one contains the other)
        for category in categories:
            head_title = category.get("title", "").strip().lower()
            # Check if title matches (exact or contains)
            if title_lower not in head_title and head_title not in title_lower:
                continue
            
            subcategories = category.get("subcategories", [])
            for sub in subcategories:
                if isinstance(sub, dict):
                    sub_title = sub.get("title", "").strip().lower()
                    # Check if subcategory matches (exact or contains)
                    if (subcategory_lower in sub_title or sub_title in subcategory_lower or
                        subcategory_lower.split()[0] in sub_title or sub_title.split()[0] in subcategory_lower):
                        headcategory_id = category.get("headcategory_id")
                        category_id = sub.get("_id")
                        if isinstance(headcategory_id, ObjectId) and isinstance(category_id, ObjectId):
                            return headcategory_id, category_id
        
        # Fourth pass: word-based matching (for cases like "Wage" matching "Wage, Invoices")
        title_words = set(title_lower.split())
        subcategory_words = set(subcategory_lower.split())
        
        for category in categories:
            head_title = category.get("title", "").strip().lower()
            head_title_words = set(head_title.split())
            
            # Check if there's significant word overlap for title
            if not title_words.intersection(head_title_words) and not head_title_words.intersection(title_words):
                continue
            
            subcategories = category.get("subcategories", [])
            for sub in subcategories:
                if isinstance(sub, dict):
                    sub_title = sub.get("title", "").strip().lower()
                    sub_title_words = set(sub_title.split())
                    
                    # Check if there's significant word overlap for subcategory
                    if (subcategory_words.intersection(sub_title_words) or 
                        sub_title_words.intersection(subcategory_words)):
                        headcategory_id = category.get("headcategory_id")
                        category_id = sub.get("_id")
                        if isinstance(headcategory_id, ObjectId) and isinstance(category_id, ObjectId):
                            return headcategory_id, category_id
        
        return None, None