import os
import tempfile
from typing import List, Dict, Any, BinaryIO
import pdfplumber
from langchain_text_splitters import RecursiveCharacterTextSplitter

from config import CHUNK_SIZE, CHUNK_OVERLAP

"""
Methods:
extract_uploaded_pdf_pages -> Takes in a file_obj in the form of BinaryIO and a filename as string. Extracts text from PDF file, page by page.
extract_pdf_from_path -> Extract text from PDF file path directly for 
chunk_text -> Split text into chunks with metadata.
process_documents -> Process extracted pages into chunks.
chunks_to_store_format -> Convert chunks to vector store add_documents format.
"""

def extract_pdf_pages(file_obj: BinaryIO, filename: str = "document.pdf") -> List[Dict[str, Any]]:
    pages = []
    
    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
        tmp.write(file_obj.read())
        tmp_path = tmp.name
    
    try:
        with pdfplumber.open(tmp_path) as pdf:
            for i, page in enumerate(pdf.pages, 1):
                text = page.extract_text() or ""
                if text.strip():
                    pages.append({
                        "text": text,
                        "page_number": i,
                        "source": filename
                    })
    finally:
        os.unlink(tmp_path)
    
    return pages


def extract_pdf_from_path(pdf_path: str, filename: str = None) -> List[Dict[str, Any]]:
    pages = []
    if filename is None:
        filename = os.path.basename(pdf_path)
    
    try:
        with pdfplumber.open(pdf_path) as pdf:
            for i, page in enumerate(pdf.pages, 1):
                text = page.extract_text() or ""
                if text.strip():
                    pages.append({
                        "text": text,
                        "page_number": i,
                        "source": filename
                    })
    except Exception:
        pass
    
    return pages


def chunk_text(text: str, source: str, page_number: int, chunk_size: int = CHUNK_SIZE, chunk_overlap: int = CHUNK_OVERLAP) -> List[Dict[str, Any]]:
    
    splitter = RecursiveCharacterTextSplitter(
        chunk_size=chunk_size,
        chunk_overlap=chunk_overlap,
        separators=["\n\n", "\n", ".", " ", ""]
    )
    
    chunks = splitter.split_text(text)
    
    result = []
    for i, chunk in enumerate(chunks):
        result.append({
            "text": chunk,
            "metadata": {
                "source": source,
                "page_number": page_number,
                "chunk_index": i
            }
        })
    
    return result


def process_documents(pages: List[Dict[str, Any]], chunk_size: int = CHUNK_SIZE, chunk_overlap: int = CHUNK_OVERLAP) -> List[Dict[str, Any]]:
    all_chunks = []
    
    for page in pages:
        chunks = chunk_text(
            text=page["text"],
            source=page["source"],
            page_number=page["page_number"],
            chunk_size=chunk_size,
            chunk_overlap=chunk_overlap
        )
        all_chunks.extend(chunks)
    
    return all_chunks


def chunks_to_store_format(chunks: List[Dict[str, Any]]) -> tuple:
    texts = [c["text"] for c in chunks]
    metadatas = [c["metadata"] for c in chunks]
    return texts, metadatas


if __name__ == "__main__":
    test_text = """This is a test document.
    
    It has multiple paragraphs. Each paragraph contains some text.
    
    This is the third paragraph with more content."""
    
    chunks = chunk_text(test_text, "test.pdf", 1)
    print(f"Created {len(chunks)} chunks")
    for c in chunks:
        print(f"  - {c['metadata']}: {c['text'][:50]}...")