import os
import sys
import logging
import time
import random
from typing import List, Dict, Any
from langchain.document_loaders import (
    PyPDFLoader,
    TextLoader,
    CSVLoader
)
from langchain.text_splitter import RecursiveCharacterTextSplitter

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

# Add project root to path for imports
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
from app.config import CHUNK_SIZE, CHUNK_OVERLAP
from app.core.memory import MemoryManager

class DocumentProcessor:
    """Processes documents for ingestion into the vector database."""
    
    def __init__(self, memory_manager: MemoryManager):
        self.memory_manager = memory_manager
        self.text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=CHUNK_SIZE,
            chunk_overlap=CHUNK_OVERLAP
        )
        logger.info(f"DocumentProcessor initialized with chunk size {CHUNK_SIZE}, overlap {CHUNK_OVERLAP}")
    
    def process_file(self, file_path: str) -> List[str]:
        """Process a file and return a list of document chunks."""
        if not os.path.exists(file_path):
            raise FileNotFoundError(f"File not found: {file_path}")
        
        # Get the file extension
        _, extension = os.path.splitext(file_path)
        extension = extension.lower()
        
        logger.info(f"Processing file: {file_path} with extension {extension}")
        
        # Load the file using the appropriate loader
        if extension == '.pdf':
            loader = PyPDFLoader(file_path)
        elif extension == '.txt':
            loader = TextLoader(file_path)
        elif extension == '.csv':
            loader = CSVLoader(file_path)
        else:
            raise ValueError(f"Unsupported file type: {extension}")
        
        # Load and split the documents
        documents = loader.load()
        chunks = self.text_splitter.split_documents(documents)
        
        logger.info(f"Split file into {len(chunks)} chunks")
        return chunks
    
    def _retry_operation(self, operation, max_retries=3):
        """Retry an operation with exponential backoff."""
        for attempt in range(max_retries):
            try:
                return operation()
            except Exception as e:
                if "already accessed by another instance" in str(e) and attempt < max_retries - 1:
                    wait_time = random.uniform(0.5, 2.0) * (attempt + 1)
                    logger.warning(f"Vector store access conflict, retrying ({attempt+1}/{max_retries}) in {wait_time:.2f}s...")
                    time.sleep(wait_time)
                else:
                    # Different error or last attempt, re-raise
                    raise
    
    def ingest_file(self, file_path: str, metadata: Dict[str, Any] = None) -> List[str]:
        """Ingest a file into the vector database."""
        try:
            # Process the file
            chunks = self.process_file(file_path)
            
            # Add metadata to each chunk
            if metadata is None:
                metadata = {}
            
            # Add file path to metadata
            base_metadata = {
                "source": file_path,
                "file_name": os.path.basename(file_path)
            }
            base_metadata.update(metadata)
            
            # Prepare chunks and metadatas
            texts = [chunk.page_content for chunk in chunks]
            metadatas = []
            
            for i, chunk in enumerate(chunks):
                chunk_metadata = base_metadata.copy()
                if hasattr(chunk, 'metadata'):
                    chunk_metadata.update(chunk.metadata)
                chunk_metadata["chunk_id"] = i
                metadatas.append(chunk_metadata)
            
            # Store in vector database with retry mechanism
            logger.info(f"Adding {len(texts)} chunks to vector database")
            
            def add_to_vectordb():
                return self.memory_manager.add_texts(texts, metadatas)
                
            ids = self._retry_operation(add_to_vectordb)
            logger.info(f"Successfully added chunks with IDs: {ids[:3]}...")
            
            return ids
        except Exception as e:
            logger.error(f"Error ingesting file {file_path}: {str(e)}")
            # Return placeholder IDs if there's an error
            return [f"error-{random.randint(1000, 9999)}" for _ in range(len(chunks) if 'chunks' in locals() else 1)]
    
    def ingest_text(self, text: str, metadata: Dict[str, Any] = None) -> List[str]:
        """Ingest raw text into the vector database."""
        try:
            if metadata is None:
                metadata = {}
            
            # Split the text
            chunks = self.text_splitter.split_text(text)
            logger.info(f"Split text into {len(chunks)} chunks")
            
            # Prepare metadatas
            metadatas = []
            for i in range(len(chunks)):
                chunk_metadata = metadata.copy()
                chunk_metadata["chunk_id"] = i
                chunk_metadata["source"] = "direct_input"
                metadatas.append(chunk_metadata)
            
            # Store in vector database with retry mechanism
            def add_to_vectordb():
                return self.memory_manager.add_texts(chunks, metadatas)
                
            ids = self._retry_operation(add_to_vectordb)
            logger.info(f"Successfully added text chunks with IDs: {ids[:3] if len(ids) > 3 else ids}...")
            
            return ids
        except Exception as e:
            logger.error(f"Error ingesting text: {str(e)}")
            # Return placeholder IDs if there's an error
            return [f"error-{random.randint(1000, 9999)}" for _ in range(len(chunks) if 'chunks' in locals() else 1)]