import os
import json
import time
import logging
from pathlib import Path
from typing import List, Dict, Optional
from dataclasses import dataclass
import fitz  # PyMuPDF
from sentence_transformers import SentenceTransformer
from llama_cpp import Llama
from fastapi.encoders import jsonable_encoder

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


@dataclass
class ProductSpec:
    name: str
    description: Optional[str] = None
    price: Optional[float] = None
    attributes: Dict[str, str] = None
    tables: List[Dict] = None

    def to_dict(self):
        return jsonable_encoder(self)


class PDFProcessor:
    def __init__(self):
        self.emb_model = self._initialize_emb_model("all-MiniLM-L6-v2")
        # self.llm = self._initialize_llm("llama-2-7b.Q2_K.gguf")
        self.llm = self._initialize_llm("deepseek-llm-7b-base.Q2_K.gguf")
        self.output_dir = Path("./output")
        self.output_dir.mkdir(exist_ok=True)

    def _initialize_emb_model(self, model_name):
        try:
            from sentence_transformers import SentenceTransformer
            return SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
        except:
            # Load model directly
            from transformers import AutoTokenizer, AutoModel

            tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/" + model_name)
            model = AutoModel.from_pretrained("sentence-transformers/" + model_name)
            return model

    def _initialize_llm(self, model_name):
        """Initialize LLM with automatic download if needed"""
        # model_path = os.path.join("models/", model_name)
        # if os.path.exists(model_path):
        #     return Llama(
        #         model_path=model_path,
        #         n_ctx=1024,
        #         n_gpu_layers=-1,
        #         n_threads=os.cpu_count() - 1,
        #         verbose=False
        #     )
        return Llama.from_pretrained(
            repo_id="TheBloke/deepseek-llm-7B-base-GGUF",
            filename=model_name,
        )

    def process_pdf(self, pdf_path: str) -> Dict:
        """Process PDF using PyMuPDF"""
        start_time = time.time()

        # Open PDF
        doc = fitz.open(pdf_path)
        text_blocks = []
        tables = []

        # Extract text and tables
        for page_num, page in enumerate(doc):
            # Extract text blocks
            text_blocks.extend(self._extract_text_blocks(page))

            # Extract tables
            tables.extend(self._extract_tables(page, page_num))

        # Process text blocks with LLM
        products = []
        for block in text_blocks:
            product = self._process_text_block(block)
            if product:
                product.tables = tables
                products.append(product.to_dict())

        logger.info(f"Processed {len(products)} products in {time.time() - start_time:.2f}s")
        return {"products": products, "tables": tables}

    def _extract_text_blocks(self, page) -> List[str]:
        """Extract text blocks from a PDF page"""
        blocks = []
        for block in page.get_text("blocks"):
            blocks.append(block[4])  # The text content is at index 4
        return blocks

    def _extract_tables(self, page, page_num: int) -> List[Dict]:
        """Extract tables from a PDF page"""
        tables = []
        try:
            tab = page.find_tables()
            if tab.tables:
                for table_idx, table in enumerate(tab.tables):
                    table_data = table.extract()
                    if table_data:
                        tables.append({
                            "page": page_num + 1,
                            "cells": table_data,
                            "header": table.header.names if table.header else [],
                            "content": table_data
                        })
        except Exception as e:
            logger.warning(f"Error extracting tables from page {page_num}: {e}")
        return tables

    def _process_text_block(self, text: str) -> Optional[ProductSpec]:
        """Process text block with LLM"""
        prompt = self._generate_query_prompt(text)

        try:
            response = self.llm.create_chat_completion(
                messages=[{"role": "user", "content": prompt}],
                temperature=0.1,
                max_tokens=512
            )
            return self._parse_response(response['choices'][0]['message']['content'])
        except Exception as e:
            logger.warning(f"Error processing text block: {e}")
            return None

    def _generate_query_prompt(self, text: str) -> str:
        """Generate extraction prompt"""
        return f"""Extract product specifications from this text:
{text}

Return JSON format:
{{
    "name": "product name",
    "description": "product description",
    "price": numeric_price,
    "attributes": {{ "key": "value" }}
}}"""

    def _parse_response(self, response: str) -> Optional[ProductSpec]:
        """Parse LLM response"""
        try:
            json_start = response.find('{')
            json_end = response.rfind('}') + 1
            data = json.loads(response[json_start:json_end])
            return ProductSpec(
                name=data.get('name', ''),
                description=data.get('description'),
                price=data.get('price'),
                attributes=data.get('attributes', {})
            )
        except (json.JSONDecodeError, KeyError) as e:
            logger.warning(f"Parse error: {e}")
            return None


def process_pdf_catalog(pdf_path: str):
    processor = PDFProcessor()
    try:
        result = processor.process_pdf(pdf_path)
        return result, "Processing completed successfully!"
    except Exception as e:
        logger.error(f"Processing failed: {e}")
        return {}, "Error processing PDF"