import re
import pdfplumber
import pandas as pd
from services.upload_file.utils.pdf_cleaner import row_ratio, has_mixed_text_and_numbers, is_short_text_row, parse_page_selection, filter_geo_admin_column, cleaning_column
from services.upload_file.upload_exceptions import PDFReadError
from utils.logger_config import setup_logger

logger = setup_logger(__name__)

def detect_header_rows(rows):
    if not rows:
        return []

    ratios = [row_ratio(r) for r in rows]
    body_start_index = None

    for i in range(1, len(rows)):
        row = rows[i]
        if has_mixed_text_and_numbers(row):
            body_start_index = i
            break
        if ratios[i] > 0.3:
            body_start_index = i
            break
        if any(isinstance(c, str) and re.match(r'^\d+$', c.strip()) for c in row):
            body_start_index = i
            break
        if ratios[i - 1] == 0 and ratios[i] > 0:
            body_start_index = i
            break

    if body_start_index is None:
        body_start_index = len(rows)

    potential_headers = rows[:body_start_index]
    body_filtered = rows[body_start_index:]
    header_filtered = []
    for idx, row in enumerate(potential_headers):
        if is_short_text_row(row):
            if idx + 1 < len(potential_headers) and ratios[idx + 1] == 0:
                header_filtered.append(row)
            else:
                continue
        else:
            header_filtered.append(row)

    return header_filtered, body_filtered


def merge_multiline_header(header_rows):
    final_header = []
    for col in zip(*header_rows):
        val = next((v for v in reversed(col) if v and str(v).strip()), '')
        val = str(val).replace('\n', ' ').strip()
        final_header.append(val)
    final_header = [v for v in final_header if v not in ['', None]]
    return final_header


def read_pdf(path: str, page: str):
    """
    Membaca tabel dari file PDF secara semi-otomatis menggunakan `pdfplumber`.

    Alur utama proses:
    1. **Buka file PDF** menggunakan pdfplumber.
    2. **Pilih halaman** berdasarkan input `page` (misalnya "1,3-5" untuk halaman 1 dan 3–5).
    3. **Deteksi tabel** di setiap halaman yang dipilih.
    4. **Ekstraksi tabel mentah** (list of list) dari setiap halaman.
    5. **Pisahkan baris header dan body** dengan fungsi `detect_header_rows()`.
    6. **Gabungkan header multi-baris** (misalnya tabel dengan dua baris judul kolom).
    7. **Bersihkan body tabel** menggunakan `cleaning_column()`:
       - Menghapus kolom nomor urut.
       - Menyesuaikan jumlah kolom dengan header.
    8. **Gabungkan hasil akhir** ke dalam format JSON dengan struktur:
       {
           "title": <nomor tabel>,
           "columns": [...],
           "rows": [...]
       }
    9. **Filter tambahan** dengan `filter_geo_admin_column()` (khusus metadata geospasial).
    10. **Kembalikan hasil** berupa list JSON siap dikirim ke frontend API.

    Args:
        path (str): Lokasi file PDF yang akan dibaca.
        page (str): Nomor halaman atau rentang halaman, contoh: "1", "2-4", "1,3-5".

    Returns:
        list[dict]: Daftar tabel hasil ekstraksi dengan struktur kolom dan baris.

    Raises:
        PDFReadError: Jika terjadi kesalahan saat membaca atau parsing PDF.
    """
    try:
        pdf_path = path
        selectedPage = page if page else "1"
        tables_data = []

        with pdfplumber.open(pdf_path) as pdf:
            total_pages = len(pdf.pages)
            selected_pages = parse_page_selection(selectedPage, total_pages)

            logger.info(f"[INFO] Total halaman PDF: {total_pages}")
            logger.info(f"[INFO] Halaman yang dipilih untuk dibaca: {selected_pages}")

            for page_num in selected_pages:
                pdf_page = pdf.pages[page_num - 1]
                tables = pdf_page.find_tables()
                logger.info(f"[INFO] Halaman {page_num}: {len(tables)} tabel terdeteksi")

                for t in tables:
                    table = t.extract()
                    if len(table) > 2:
                        tables_data.append(table)

        logger.info(f"\nTotal tabel valid: {len(tables_data)}\n")

        header_only, body_only = [], []
        for tbl in tables_data:
            head, body = detect_header_rows(tbl)
            header_only.append(head)
            body_only.append(body)

        clean_header = [merge_multiline_header(h) for h in header_only]
        clean_body = []

        for i, raw_body in enumerate(body_only):
            con_body = [[cell for cell in row if cell not in (None, '')] for row in raw_body]
            cleaned = cleaning_column(clean_header[i], [con_body])
            clean_body.append(cleaned[0])

        parsed = []
        for i, (cols, rows) in enumerate(zip(clean_header, clean_body), start=1):
            parsed.append({
                "title": str(i),
                "columns": cols,
                "rows": rows
            })

        clean_parsed = filter_geo_admin_column(parsed)
        return clean_parsed

    except Exception as e:
        raise PDFReadError(f"Gagal membaca PDF: {e}", code=422)


def convert_df(payload):
    try:
        if "columns" not in payload or "rows" not in payload:
            raise ValueError("Payload tidak memiliki key 'columns' atau 'rows'.")

        if not isinstance(payload["columns"], list):
            raise TypeError("'columns' harus berupa list.")
        if not isinstance(payload["rows"], list):
            raise TypeError("'rows' harus berupa list.")

        for i, row in enumerate(payload["rows"]):
            if len(row) != len(payload["columns"]):
                raise ValueError(f"Jumlah elemen di baris ke-{i} tidak sesuai jumlah kolom.")

        df = pd.DataFrame(payload["rows"], columns=payload["columns"])

        if "title" in payload:
            df.attrs["title"] = payload["title"]

        return df

    except Exception as e:
        raise PDFReadError(f"Gagal konversi payload ke DataFrame: {e}", code=400)