Delete bhavcopy_html2.py

bhavcopy_html2.py

@@ -1,118 +0,0 @@
-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
-    )