Delete bhavcopy_html.py

bhavcopy_html.py

@@ -1,147 +0,0 @@
-import pandas as pd
-import nsepython as nse
-import persist
-from datetime import datetime as dt
-
-
-def build_bhavcopy_html(date_str):
-    key = f"bhavcopy_{date_str}"
-
-    # -------------------------------------------------------
-    # 0) Use cached HTML if present
-    # -------------------------------------------------------
-    if persist.exists(key, "html"):
-        cached = persist.load(key, "html")
-        if cached is not False:
-            print(
-                f"[{dt.now().strftime('%Y-%m-%d %H:%M:%S')}] "
-                f"Using cached bhavcopy for {date_str}"
-            )
-            return cached
-
-    try:
-        # -------------------------------------------------------
-        # 1) Validate Date (DD-MM-YYYY)
-        # -------------------------------------------------------
-        try:
-            dt.strptime(date_str, "%d-%m-%Y")
-        except ValueError:
-            html = "<h3>Invalid date format. Use DD-MM-YYYY.</h3>"
-            persist.save(key, html, "html")
-            return html
-
-        # -------------------------------------------------------
-        # 2) Fetch Bhavcopy (nsepython expects DD-MM-YYYY)
-        # -------------------------------------------------------
-        try:
-            df = nse.nse_bhavcopy(date_str)
-            df.columns = df.columns.str.strip()
-        except Exception:
-            html = f"<h3>No Bhavcopy found for {date_str}.</h3>"
-            persist.save(key, html, "html")
-            return html
-
-        # -------------------------------------------------------
-        # 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
-
-        # -------------------------------------------------------
-        # 7) 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>
-        """
-
-        html = (
-            css +
-            "<h2>Main Bhavcopy Table</h2>" +
-            main_html +
-            "<h2>Matrix/Grid Table</h2>" +
-            grid_html
-        )
-
-        persist.save(key, html, "html")
-        return html
-
-    except Exception as e:
-        print(
-            f"[{dt.now().strftime('%Y-%m-%d %H:%M:%S')}] "
-            f"Error build_bhavcopy_html: {e}"
-        )
-        return f"<h3>Error: {e}</h3>"