import os
import tempfile
import requests

import pandas as pd
from PIL import Image
import pytesseract
from pptx import Presentation
import shutil

from fastapi import HTTPException
# import nltk
# from langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader
from langchain_community.document_loaders import PyMuPDFLoader, Docx2txtLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from  langchain_text_splitters.sentence_transformers import SentenceTransformersTokenTextSplitter  # give better results but slow can use later for project
from langchain.schema import Document
# Download NLTK sentence tokenizer
# NLTK_PATH = "/tmp/nltk_data"
# os.makedirs(NLTK_PATH, exist_ok=True)
# nltk.data.path.append(NLTK_PATH)
# nltk.download("punkt", download_dir=NLTK_PATH, quiet=True)
# nltk.download("punkt_tab", download_dir=NLTK_PATH, quiet=True)


MODEL_DIR = os.path.join("/tmp", "e5-large-v2")

chunk_dict= {}
    
def load_excel(path: str) -> list[Document]:
    dfs = pd.read_excel(path, sheet_name=None)
    docs = []
    for sheet_name, df in dfs.items():
        text = df.to_csv(index=False)
        docs.append(Document(page_content=text, metadata={"sheet": sheet_name}))
    return docs



def load_zip(path: str, depth: int = 0, base_dir="/tmp/unzipped") -> list[Document]:
    extracted_docs = []
    extract_dir = os.path.join(base_dir, f"level_{depth}")
    os.makedirs(extract_dir, exist_ok=True)

    with zipfile.ZipFile(path, 'r') as archive:
        archive.extractall(extract_dir)

    for name in os.listdir(extract_dir):
        file_path = os.path.join(extract_dir, name)
        
        if name.endswith(".zip"):
            extracted_docs.extend(load_zip(file_path, depth + 1, base_dir))  # Recursive call
        elif name.endswith(".pdf"):
            loader = PyMuPDFLoader(file_path)
            extracted_docs += loader.load()
        elif name.endswith(".docx"):
            loader = Docx2txtLoader(file_path)
            extracted_docs += loader.load()
        elif name.endswith(".txt"):
            with open(file_path, "r", encoding="utf-8", errors="ignore") as f:
                extracted_docs.append(Document(page_content=f.read()))
        elif name.endswith((".png", ".jpg", ".jpeg")):
            image = Image.open(file_path)
            text = pytesseract.image_to_string(image)
            extracted_docs.append(Document(page_content=text))

    return extracted_docs


def load_image(path: str) -> list[Document]:
    image = Image.open(path)
    text = pytesseract.image_to_string(image)
    return [Document(page_content=text)]

def load_pptx(path: str) -> list[Document]:
    prs = Presentation(path)
    full_text = []
    for slide in prs.slides:
        for shape in slide.shapes:
            if hasattr(shape, "text"):
                full_text.append(shape.text)
            elif shape.shape_type == 13 and shape.image:  # PICTURE shape
                image = shape.image.blob
                with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as img_tmp:
                    img_tmp.write(image)
                    img_path = img_tmp.name
                try:
                    img_text = pytesseract.image_to_string(Image.open(img_path))
                    if img_text.strip():
                        full_text.append(img_text.strip())
                finally:
                    os.remove(img_path)
    return [Document(page_content="\n".join(full_text))]

def load_and_chunk(url: str) -> list[Document]:
    print(url)
    if url not in chunk_dict:
        print("processing new url")
        resp = requests.get(url)
        if resp.status_code != 200:
            raise HTTPException(400, "Could not download document")

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

        try:
            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
                loader = PyMuPDFLoader(tmp_path)
                docs = loader.load_and_split()

            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
                loader = Docx2txtLoader(tmp_path)
                docs = loader.load_and_split()

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

            elif ".xlsx" in url_lower:
                with tempfile.NamedTemporaryFile(delete=False, suffix=".xlsx") as tmp:
                    tmp.write(resp.content)
                    tmp_path = tmp.name
                docs = load_excel(tmp_path)

            elif ".zip" in url_lower:
                with tempfile.NamedTemporaryFile(delete=False, suffix=".zip") as tmp:
                    tmp.write(resp.content)
                    tmp_path = tmp.name
                text = "empty file"    
                docs = [Document(page_content=text)]

            elif ".png" in url_lower or ".jpg" in url_lower or ".jpeg" in url_lower:
                with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp:
                    tmp.write(resp.content)
                    tmp_path = tmp.name
                docs = load_image(tmp_path)

            elif ".pptx" in url_lower:
                with tempfile.NamedTemporaryFile(delete=False, suffix=".pptx") as tmp:
                    tmp.write(resp.content)
                    tmp_path = tmp.name
                docs = load_pptx(tmp_path)

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

        finally:
            if 'tmp_path' in locals() and os.path.exists(tmp_path):
                os.remove(tmp_path)

        full_text = "\n".join([doc.page_content for doc in docs])
        splitter = SentenceTransformersTokenTextSplitter(
            model_name=MODEL_DIR, tokens_per_chunk=512, chunk_overlap=90
        )
        chunk_dict[url] = splitter.create_documents([full_text])
        return chunk_dict[url]
    else:
        print("stored chunk")
        return chunk_dict[url]