# backend/parser.py
from backend.metrics_extractor import extract_metrics, get_spacy_nlp
from backend.sector import detect_sector
import fitz  # PyMuPDF
import re


nlp = None


def get_parser_nlp():
    global nlp
    if nlp is not None:
        return nlp

    nlp = get_spacy_nlp()
    return nlp


def _clean(text: str) -> str:
    text = re.sub(r"\n{3,}", "\n\n", text)
    text = re.sub(r" {2,}", " ", text)
    return text.strip()


def pdf_to_pages(pdf_path: str) -> list:
    """Extract text per page (1-based page numbers preserved by index+1).
    HTML files count as a single 'page'. Each page is cleaned individually
    so char offsets computed AFTER joining stay valid for page mapping."""
    path = str(pdf_path)

    # HTML file
    if path.endswith(".html") or path.endswith(".htm"):
        with open(path, "r", encoding="utf-8", errors="ignore") as f:
            raw = f.read()
        # strip HTML tags
        text = re.sub(r"<[^>]+>", " ", raw)
        text = re.sub(r"&nbsp;", " ", text)
        text = re.sub(r"&amp;", "&", text)
        text = re.sub(r"&lt;", "<", text)
        text = re.sub(r"&gt;", ">", text)
        return [_clean(text)]

    # PDF file
    doc = fitz.open(path)
    pages = [_clean(page.get_text()) for page in doc]
    doc.close()
    return pages


def pdf_to_text(pdf_path: str) -> str:
    return "\n".join(pdf_to_pages(pdf_path))


def page_offsets(pages: list) -> list:
    """Start char offset of each page in the '\\n'.join(pages) text."""
    offsets = []
    pos = 0
    for p in pages:
        offsets.append(pos)
        pos += len(p) + 1  # +1 for the joining \n
    return offsets


def char_pos_to_page(char_pos: int, offsets: list) -> int | None:
    """Map a char position in the joined text to a 1-based page number."""
    if char_pos is None or not offsets:
        return None
    page = 1
    for i, start in enumerate(offsets):
        if char_pos >= start:
            page = i + 1
        else:
            break
    return page


def extract_entities(text: str) -> list:
    parser_nlp = get_parser_nlp()
    if parser_nlp is None:
        print("WARNING: spaCy model 'en_core_web_sm' not installed; extracted entities will be empty.")
        return []

    doc = parser_nlp(text[:50000])  # cap for speed
    entities = []
    seen = set()
    for ent in doc.ents:
        if ent.label_ in {"ORG", "GPE", "MONEY", "DATE", "PRODUCT", "PERSON"}:
            key = (ent.text.strip(), ent.label_)
            if key not in seen:
                seen.add(key)
                entities.append({"text": ent.text.strip(), "label": ent.label_})
    return entities


def chunk_text(text: str, size: int = 512, overlap: int = 50) -> list:
    words = text.split()
    chunks = []
    start = 0
    while start < len(words):
        end = min(start + size, len(words))
        chunks.append(" ".join(words[start:end]))
        if end == len(words):
            break
        start += size - overlap
    return chunks


def chunk_pages(pages: list, size: int = 512, overlap: int = 50) -> list:
    """Chunk page-by-page so every chunk carries the page it came from.
    Chunks never span pages — a slight retrieval-quality trade for exact
    provenance, which is the point. Most report pages are under 512 words
    so this usually means one chunk per page anyway.

    Returns list of {"text": str, "page": int} (page is 1-based)."""
    out = []
    for page_no, page_text in enumerate(pages, start=1):
        if not page_text.strip():
            continue
        for piece in chunk_text(page_text, size=size, overlap=overlap):
            out.append({"text": piece, "page": page_no})
    return out


def _attach_pages(metrics: dict, offsets: list) -> dict:
    """Convert each money-metric's char_pos (and its alternatives') into a
    1-based PDF page number. Ratio metrics are bare floats — passed through."""
    for value in metrics.values():
        if not isinstance(value, dict):
            continue
        value["page"] = char_pos_to_page(value.pop("char_pos", None), offsets)
        for alt in value.get("alternatives", []):
            if isinstance(alt, dict):
                alt["page"] = char_pos_to_page(alt.pop("char_pos", None), offsets)
    return metrics


def parse_document(pdf_path: str, company: str, year: str) -> dict:
    print(f"Parsing {pdf_path}...")
    pages = pdf_to_pages(pdf_path)
    offsets = page_offsets(pages)
    text = "\n".join(pages)
    print(f"Text length: {len(text)} chars across {len(pages)} pages")
    # ascii-safe: PDF text often contains ligatures/₹ glyphs that crash
    # print() on Windows cp1252 consoles
    print("Sample:", text[:200].encode("ascii", "replace").decode("ascii"))

    # NOTE: company is now passed through to extract_metrics so the
    # entity-boundary detection in metrics_extractor.py can demote
    # numbers found inside a bundled subsidiary's section instead of
    # treating every match in the document as equally valid for `company`.
    metrics = _attach_pages(extract_metrics(text, company=company), offsets)
    # metric dicts carry raw PDF context strings — ascii-safe for the same
    # cp1252 console reason as the sample print above
    print("Metrics found:", str(metrics).encode("ascii", "replace").decode("ascii"))

    sector = detect_sector(text)
    print(f"Sector detected: {sector}")

    entities = extract_entities(text)
    print(f"Entities found: {len(entities)}")
    chunks = chunk_pages(pages)

    return {
        "company": company,
        "year": year,
        "file": pdf_path,
        "sector": sector,
        "char_count": len(text),
        "chunk_count": len(chunks),
        "metrics": metrics,
        "entities": entities,
        "chunks": [
            {
                "chunk_id": f"{company}_{year}_{i:04d}",
                "company": company,
                "year": year,
                "page": c["page"],
                "text": c["text"]
            }
            for i, c in enumerate(chunks)
        ]
    }


if __name__ == "__main__":
    sample = """
    Apple Inc. reported total revenues of $394.3 billion for fiscal year 2022.
    Net income was $99.8 billion. Earnings per share reached $6.15.
    Total assets stood at $352.6 billion. Cash and cash equivalents were $23.6 billion.
    """
    print("Metrics:", extract_metrics(sample, company="Apple"))
    print("Entities:", extract_entities(sample))
    print("Chunks:", len(chunk_text(sample)))