import pandas as pd
import pdfplumber



def classify_pdf(pdf_path: str) -> str:
    """
    Peeks at the first page of the PDF to determine its origin.
    Returns 'MARG', 'TALLY', or 'UNKNOWN'.
    """
    with pdfplumber.open(pdf_path) as pdf:
        if len(pdf.pages) == 0:
            return "UNKNOWN"

        first_page = pdf.pages[0]
        words = first_page.extract_words(keep_blank_chars=False)

        if not words:
            return "UNKNOWN"

        text_stream = " ".join([w["text"].lower() for w in words])

        # --- 1. Check for Explicit Metadata/Branding ---
        if "marg erp" in text_stream or "margcompusoft" in text_stream:
            return "MARG"
        if "tally prime" in text_stream or "tally solutions" in text_stream:
            return "TALLY"

        # --- 2. Check Structural Vocabulary (Heuristics) ---
        marg_score = 0
        tally_score = 0

        # Marg Keywords (Strictly Marg-specific financial headers)
        # Removed "batch", "exp", and "hsn" as they are shared with Tally
        marg_keywords = ["s.no.", "mrp", "p.rate", "total stock", "op.stock", "cl.stock"]
        for kw in marg_keywords:
            if kw in text_stream:
                marg_score += 1

        # Tally Keywords (Hierarchical accounting)
        # Added "godown" which is highly specific to Tally
        tally_keywords = ["particulars", "opening balance", "inwards", "outwards", "closing balance", "grand total",
                          "godown"]
        for kw in tally_keywords:
            if kw in text_stream:
                tally_score += 1

        # Look for Marg's distinct dashed lines
        dashed_lines = sum(1 for w in words if "----" in w["text"] or "====" in w["text"])
        if dashed_lines > 3:
            marg_score += 2

        # Check for explicit Tally report titles combined with Particulars
        if "stock summary" in text_stream and "particulars" in text_stream:
            tally_score += 2

        # --- 3. Final Decision ---
        if marg_score > tally_score and marg_score >= 1:
            return "MARG"
        elif tally_score > marg_score and tally_score >= 1:
            return "TALLY"

        # Check for explicit drawn tables (Vanilla Grids)
        if len(first_page.find_tables()) > 0:
            return "GENERIC"

        return "UNKNOWN"


def classify_dataframe(df: pd.DataFrame) -> str:
    """Scans the first 15 rows of a dataframe for Tally/Marg signatures."""
    # Convert the top portion to a single lowercase string for easy keyword matching
    top_rows_text = df.head(15).to_string().lower()

    if "marg erp" in top_rows_text or "margcompusoft" in top_rows_text:
        return "MARG"
    if "tally prime" in top_rows_text or "tally solutions" in top_rows_text:
        return "TALLY"

    # Heuristics
    if "particulars" in top_rows_text and "godown" in top_rows_text:
        return "TALLY"
    if "s.no" in top_rows_text and "mrp" in top_rows_text and "p.rate" in top_rows_text:
        return "MARG"

    return "GENERIC"