import pandas as pd
import datetime
from nsepython import *


def build_bhavcopy_html(date_str):
    # -------------------------------------------------------
    # 1) Validate Date
    # -------------------------------------------------------
    try:
        datetime.datetime.strptime(date_str, "%d-%m-%Y")
    except:
        return "<h3>Invalid date format. Use DD-MM-YYYY.</h3>"

    # -------------------------------------------------------
    # 2) Fetch Bhavcopy
    # -------------------------------------------------------
    try:
        df = nse_bhavcopy(date_str)
        df.columns = df.columns.str.strip()
    except:
        return f"<h3>No Bhavcopy found for {date_str}.</h3>"

    # -------------------------------------------------------
    # 3) Drop unwanted columns
    # -------------------------------------------------------
    remove = ["DATE1", "LAST_PRICE", "AVG_PRICE"]
    df.drop(columns=[c for c in remove if c in df.columns], inplace=True)

    # -------------------------------------------------------
    # 4) Convert numeric columns
    # -------------------------------------------------------
    numeric_cols = [
        "PREV_CLOSE", "OPEN_PRICE", "HIGH_PRICE", "LOW_PRICE",
        "CLOSE_PRICE", "TTL_TRD_QNTY", "TURNOVER_LACS",
        "NO_OF_TRADES", "DELIV_QTY", "DELIV_PER"
    ]

    for col in numeric_cols:
        if col in df.columns:
            df[col] = (
                df[col]
                .astype(str)
                .str.replace(",", "", regex=False)
                .str.strip()
            )
            df[col] = pd.to_numeric(df[col], errors="coerce").fillna(0)

    # -------------------------------------------------------
    # 5) Filter & sort
    # -------------------------------------------------------
    df = df[df["TURNOVER_LACS"] > 1000]
    df = df.sort_values("TURNOVER_LACS", ascending=False)

    # -------------------------------------------------------
    # 6) Computed columns
    # -------------------------------------------------------
    df["change"] = df["CLOSE_PRICE"] - df["PREV_CLOSE"]
    df["perchange"] = (df["change"] / df["PREV_CLOSE"].replace(0, 1)) * 100
    df["pergap"] = (
        (df["OPEN_PRICE"] - df["PREV_CLOSE"]) /
        df["PREV_CLOSE"].replace(0, 1)
    ) * 100


    # -------------------------------------------------------
    # 8) HTML Output
    # -------------------------------------------------------
    main_html = f"""
    <div class="main-table-container">
        {df.to_html(index=False, escape=False)}
    </div>
    """

    metrics = ["perchange", "pergap", "TURNOVER_LACS", "NO_OF_TRADES", "DELIV_PER"]
    col_html = []

    for m in metrics:
        if m in df.columns:
            temp = df[["SYMBOL", m]].sort_values(m, ascending=False)
            col_html.append(
                f"""
                <div class="col">
                    <h4>{m}</h4>
                    {temp.to_html(index=False, escape=False)}
                </div>
                """
            )

    grid_html = f"""
    <div class="grid">
        {''.join(col_html)}
    </div>
    """

    css = """
    <style>
        .grid { display: grid; grid-template-columns: repeat(5, 1fr); gap: 10px; }
        .col, .main-table-container {
            max-height: 480px; overflow-y: auto;
            border: 1px solid #ccc; padding: 4px;
        }
        table { font-size: 12px; width: 100%; border-collapse: collapse; }
        th, td { border: 1px solid #ddd; padding: 4px; }
        th {
            background: #2E7D32; color: white;
            position: sticky; top: 0;
        }
    </style>
    """

    return (
        css +
        "<h2>Main Bhavcopy Table</h2>" +
        main_html +
        "<h2>Matrix/Grid Table</h2>" +
        grid_html
    )