import os
import uuid
import shutil
import logging
from typing import List, Optional, Dict, Any
from pathlib import Path


from langchain.schema import Document as LangchainDocument
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS

import fitz  # PyMuPDF
import markdown
from fastapi import FastAPI, File, UploadFile, HTTPException, Form, Depends, BackgroundTasks
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from pydantic import BaseModel
from dotenv import load_dotenv

from openrouter_llm import OpenRouterFreeAdapter, OpenRouterFreeChain

# Load environment variables
load_dotenv()

# Import LangChain components for embedding

# Import our free-only OpenRouter adapter

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

# Initialize FastAPI app
app = FastAPI(title="AskMyDocs API - Free LLM Edition")

# Add CORS middleware for frontend integration
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Set to specific domain in production
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Configuration
OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY", "")
HF_MODEL_NAME = os.getenv(
    "HF_MODEL_NAME", "sentence-transformers/all-mpnet-base-v2")
UPLOAD_DIR = os.getenv("UPLOAD_DIR", "./uploads")
DB_DIR = os.getenv("DB_DIR", "./vectordb")

print(HF_MODEL_NAME)
# Ensure directories exist
os.makedirs(UPLOAD_DIR, exist_ok=True)
os.makedirs(DB_DIR, exist_ok=True)

# Initialize OpenRouter adapter (singleton)
openrouter_adapter = None

# Pydantic models


class QueryRequest(BaseModel):
    query: str
    collection_id: str


class QueryResponse(BaseModel):
    answer: str
    sources: List[str]


class Document(BaseModel):
    id: str
    filename: str
    content_type: str


class DocumentList(BaseModel):
    documents: List[Document]


class LLMInfo(BaseModel):
    model: str
    is_free: bool = True
    provider: str = "openrouter"


class LLMModelsList(BaseModel):
    current_model: str
    free_models: List[Dict[str, Any]]


# Global variable to store vector databases (in memory for simplicity)
# In production, you would use persistent storage
vector_dbs = {}

# Helper functions


def get_embeddings():
    """Get HuggingFace embedding model."""
    return HuggingFaceEmbeddings(model_name=HF_MODEL_NAME)


def get_openrouter_adapter():
    """Get or initialize the OpenRouter adapter for free models."""
    global openrouter_adapter

    if openrouter_adapter is None:
        openrouter_adapter = OpenRouterFreeAdapter(api_key=OPENROUTER_API_KEY)

    return openrouter_adapter


def extract_text_from_pdf(file_path):
    """Extract text content from PDF files."""
    text = ""
    try:
        doc = fitz.open(file_path)
        for page in doc:
            text += page.get_text()
        return text
    except Exception as e:
        logger.error(f"Error extracting text from PDF: {e}")
        raise HTTPException(
            status_code=500, detail=f"Error processing PDF: {str(e)}")


def extract_text_from_markdown(file_path):
    """Convert Markdown to plain text."""
    try:
        with open(file_path, 'r', encoding='utf-8') as f:
            md_content = f.read()
        html = markdown.markdown(md_content)
        # Simple HTML to text conversion - in production use a more robust method
        text = html.replace('<p>', '\n\n').replace(
            '</p>', '').replace('<br>', '\n')
        text = text.replace('<h1>', '\n\n# ').replace('</h1>', '\n')
        text = text.replace('<h2>', '\n\n## ').replace('</h2>', '\n')
        text = text.replace('<h3>', '\n\n### ').replace('</h3>', '\n')
        # Remove other HTML tags
        import re
        text = re.sub('<[^<]+?>', '', text)
        return text
    except Exception as e:
        logger.error(f"Error processing Markdown: {e}")
        raise HTTPException(
            status_code=500, detail=f"Error processing Markdown: {str(e)}")


def extract_text_from_file(file_path, content_type):
    """Extract text based on file type."""
    if content_type == "application/pdf":
        return extract_text_from_pdf(file_path)
    elif content_type == "text/markdown":
        return extract_text_from_markdown(file_path)
    elif content_type == "text/plain":
        with open(file_path, 'r', encoding='utf-8') as f:
            return f.read()
    else:
        raise HTTPException(
            status_code=400, detail=f"Unsupported file type: {content_type}")


def process_documents(collection_id: str, file_paths: List[tuple]):
    """Process documents and create vector store."""
    try:
        # Create text splitter
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000,
            chunk_overlap=100,
            length_function=len,
        )

        all_docs = []
        for file_path, content_type, filename in file_paths:
            text_content = extract_text_from_file(file_path, content_type)
            chunks = text_splitter.split_text(text_content)

            # Create Document objects with metadata
            docs = [
                LangchainDocument(
                    page_content=chunk,
                    metadata={"source": filename, "chunk": i}
                )
                for i, chunk in enumerate(chunks)
            ]
            all_docs.extend(docs)

        # Create vector store
        embeddings = get_embeddings()
        vector_db = FAISS.from_documents(all_docs, embeddings)

        # Save vector store
        collection_path = os.path.join(DB_DIR, collection_id)
        os.makedirs(collection_path, exist_ok=True)
        vector_db.save_local(collection_path)

        # Store in memory (would be replaced by database lookup in production)
        vector_dbs[collection_id] = vector_db

        logger.info(
            f"Successfully processed {len(all_docs)} chunks from {len(file_paths)} documents")
    except Exception as e:
        logger.error(f"Error processing documents: {e}")
        raise HTTPException(
            status_code=500, detail=f"Error processing documents: {str(e)}")


@app.get("/")
async def index():
    return {"message": "Welcome to ask my doc"}


@app.get("/health")
async def health_check():
    return {"status": "healthy"}


@app.post("/upload", response_model=Document)
async def upload_file(
    background_tasks: BackgroundTasks,
    collection_id: str = Form(...),
    file: UploadFile = File(...),
):
    """Upload a document and process it for querying."""
    try:
        # Generate a unique ID for the document
        doc_id = str(uuid.uuid4())

        # Create collection directory if it doesn't exist
        collection_dir = os.path.join(UPLOAD_DIR, collection_id)
        os.makedirs(collection_dir, exist_ok=True)

        # Define the file path
        file_path = os.path.join(collection_dir, file.filename)

        # Determine content type
        content_type = file.content_type
        if not content_type:
            if file.filename.endswith('.pdf'):
                content_type = "application/pdf"
            elif file.filename.endswith('.md'):
                content_type = "text/markdown"
            elif file.filename.endswith('.txt'):
                content_type = "text/plain"
            else:
                raise HTTPException(
                    status_code=400, detail="Unsupported file type")

        # Save the file
        with open(file_path, "wb") as f:
            shutil.copyfileobj(file.file, f)

        # Process the document in the background
        background_tasks.add_task(
            process_documents,
            collection_id,
            [(file_path, content_type, file.filename)]
        )

        return Document(
            id=doc_id,
            filename=file.filename,
            content_type=content_type
        )
    except Exception as e:
        logger.error(f"Error uploading file: {e}")
        raise HTTPException(
            status_code=500, detail=f"Error uploading file: {str(e)}")


@app.get("/collections/{collection_id}/documents", response_model=DocumentList)
async def list_documents(collection_id: str):
    """List all documents in a collection."""
    try:
        collection_dir = os.path.join(UPLOAD_DIR, collection_id)
        if not os.path.exists(collection_dir):
            return DocumentList(documents=[])

        documents = []
        for filename in os.listdir(collection_dir):
            file_path = os.path.join(collection_dir, filename)
            if os.path.isfile(file_path):
                content_type = "application/octet-stream"
                if filename.endswith('.pdf'):
                    content_type = "application/pdf"
                elif filename.endswith('.md'):
                    content_type = "text/markdown"
                elif filename.endswith('.txt'):
                    content_type = "text/plain"

                documents.append(Document(
                    # In production, store and retrieve actual IDs
                    id=str(uuid.uuid4()),
                    filename=filename,
                    content_type=content_type
                ))

        return DocumentList(documents=documents)
    except Exception as e:
        logger.error(f"Error listing documents: {e}")
        raise HTTPException(
            status_code=500, detail=f"Error listing documents: {str(e)}")


@app.post("/query", response_model=QueryResponse)
async def query_documents(request: QueryRequest):
    """Query documents using natural language."""
    try:
        collection_id = request.collection_id

        # Check if vector DB exists in memory
        if collection_id in vector_dbs:
            vector_db = vector_dbs[collection_id]
        else:
            # Load from disk
            collection_path = os.path.join(DB_DIR, collection_id)
            if not os.path.exists(collection_path):
                raise HTTPException(
                    status_code=404, detail=f"Collection {collection_id} not found")

            embeddings = get_embeddings()
            vector_db = FAISS.load_local(collection_path, embeddings)
            vector_dbs[collection_id] = vector_db

        # Get the retriever
        retriever = vector_db.as_retriever(search_kwargs={"k": 3})

        # Get relevant documents
        docs = retriever.get_relevant_documents(request.query)

        # Extract sources
        sources = []
        for doc in docs:
            if doc.metadata.get("source") not in sources:
                sources.append(doc.metadata.get("source"))

        # Get context from documents
        context = [doc.page_content for doc in docs]

        # Get OpenRouter adapter for free LLMs
        adapter = get_openrouter_adapter()
        chain = OpenRouterFreeChain(adapter)

        # Generate answer
        answer = chain.run(request.query, context)

        return QueryResponse(
            answer=answer,
            sources=sources
        )
    except Exception as e:
        logger.error(f"Error querying documents: {e}")
        raise HTTPException(
            status_code=500, detail=f"Error querying documents: {str(e)}")


@app.delete("/collections/{collection_id}/documents/{filename}")
async def delete_document(collection_id: str, filename: str):
    """Delete a document from a collection."""
    try:
        file_path = os.path.join(UPLOAD_DIR, collection_id, filename)
        if not os.path.exists(file_path):
            raise HTTPException(
                status_code=404, detail=f"Document {filename} not found")

        os.remove(file_path)

        # Rebuild vector store if needed
        collection_path = os.path.join(DB_DIR, collection_id)
        if os.path.exists(collection_path):
            # In production, you would selectively remove documents rather than rebuilding
            shutil.rmtree(collection_path)

            # If there are still documents, rebuild the vector store
            collection_dir = os.path.join(UPLOAD_DIR, collection_id)
            if os.path.exists(collection_dir) and os.listdir(collection_dir):
                file_paths = []
                for fname in os.listdir(collection_dir):
                    fpath = os.path.join(collection_dir, fname)
                    if os.path.isfile(fpath):
                        content_type = "application/octet-stream"
                        if fname.endswith('.pdf'):
                            content_type = "application/pdf"
                        elif fname.endswith('.md'):
                            content_type = "text/markdown"
                        elif fname.endswith('.txt'):
                            content_type = "text/plain"
                        file_paths.append((fpath, content_type, fname))

                if file_paths:
                    process_documents(collection_id, file_paths)

            # Remove from in-memory cache
            if collection_id in vector_dbs:
                del vector_dbs[collection_id]

        return JSONResponse(content={"message": f"Document {filename} deleted"})
    except Exception as e:
        logger.error(f"Error deleting document: {e}")
        raise HTTPException(
            status_code=500, detail=f"Error deleting document: {str(e)}")


@app.get("/llm/info", response_model=LLMInfo)
async def get_llm_info():
    """Get the current LLM information."""
    adapter = get_openrouter_adapter()

    return LLMInfo(
        model=adapter.model,
        is_free=True,
        provider="openrouter"
    )


@app.get("/llm/models", response_model=LLMModelsList)
async def list_free_models():
    """List all available free models."""
    adapter = get_openrouter_adapter()
    free_models = adapter.list_free_models()

    # Create a simplified list for the frontend
    model_list = []
    for model in free_models:
        model_info = {
            "id": model.get("id"),
            "name": model.get("name", model.get("id")),
            "context_length": model.get("context_length", 4096),
            "provider": model.get("id").split("/")[0] if "/" in model.get("id") else "unknown"
        }
        model_list.append(model_info)

    return LLMModelsList(
        current_model=adapter.model,
        free_models=model_list
    )


@app.post("/llm/change-model")
async def change_model(model_info: LLMInfo):
    """Change the LLM model (only to another free model)."""
    adapter = get_openrouter_adapter()

    # Make sure the model has the :free suffix if it doesn't already
    model_id = model_info.model
    if not model_id.endswith(":free") and ":free" not in model_id:
        model_id = f"{model_id}:free"

    # Set the new model
    adapter.model = model_id

    return JSONResponse(content={"message": f"Model changed to {model_id}"})

if __name__ == "__main__":
    import uvicorn
    # Check if we have an OpenRouter adapter and initialize it
    adapter = get_openrouter_adapter()
    logger.info(f"Starting AskMyDocs with free model: {adapter.model}")
    uvicorn.run(app, host="0.0.0.0", port=7860)