import os
import tempfile
import requests
import re
from fastapi import HTTPException
from langchain_community.document_loaders import PyMuPDFLoader, Docx2txtLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.schema import Document

def load_and_chunk(url: str) -> list[Document]:
    resp = requests.get(url)
    if resp.status_code != 200:
        raise HTTPException(400, "Document download failed")

    content_type = resp.headers.get("Content-Type", "").lower()
    url_lower = url.lower()
    text = ""

    if "application/pdf" in content_type or ".pdf" in url_lower:
        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
            tmp.write(resp.content)
            tmp_path = tmp.name
        try:
            loader = PyMuPDFLoader(tmp_path)
            # Extract full text first for better chunking
            pages = loader.load()
            text = "\n".join([p.page_content for p in pages])
        finally:
            os.remove(tmp_path)

    elif ("application/vnd.openxmlformats-officedocument.wordprocessingml.document" in content_type
          or ".docx" in url_lower):
        with tempfile.NamedTemporaryFile(delete=False, suffix=".docx") as tmp:
            tmp.write(resp.content)
            tmp_path = tmp.name
        try:
            loader = Docx2txtLoader(tmp_path)
            docs = loader.load()
            text = "\n".join([d.page_content for d in docs])
        finally:
            os.remove(tmp_path)

    elif "text/plain" in content_type or ".txt" in url_lower:
        text = resp.content.decode("utf-8", errors="ignore")

    else:
        raise HTTPException(400, f"Unsupported document type: {content_type}")

    # Clean and normalize text
    text = re.sub(r'\s+', ' ', text).strip()
    
    # Semantic chunking
    splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=150,
        separators=["\n\n", "\n", ". ", "! ", "? ", " ", ""]
    )
    return splitter.split_text(text)