Delete preopen_html.py

preopen_html.py

@@ -1,175 +0,0 @@
-from nsepython import *
-import pandas as pd
-import re
-from datetime import datetime as dt
-
-# persist helpers (HF only)
-from persist import exists, load, save
-
-
-def build_preopen_html(key="NIFTY"):
-    """
-    Build full Pre-Open HTML
-    - Daily TTL (via persist.py)
-    - HTML only cache
-    """
-
-    # ================= CACHE (TTL via persist) =================
-    cache_name = f"DAILY_PREOPEN_{key.upper()}"
-
-    if exists(cache_name, "html"):
-        cached_html = load(cache_name, "html")
-        if isinstance(cached_html, str):
-            return cached_html
-
-    # ================= FETCH DATA =================
-    p = nsefetch(f"https://www.nseindia.com/api/market-data-pre-open?key={key}")
-
-    data_df = df_from_data(p.pop("data"))
-    rem_df  = df_from_data([p])
-
-    main_df  = data_df.iloc[[0]] if not data_df.empty else pd.DataFrame()
-    const_df = data_df.iloc[1:] if len(data_df) > 1 else pd.DataFrame()
-
-    # ================= REMOVE PATTERN COLUMNS =================
-    pattern_remove = re.compile(r"^(price_|buyQty_|sellQty_|iep_)\d+$")
-
-    def remove_pattern_cols(df):
-        if df is None or df.empty:
-            return df
-        return df[[c for c in df.columns if not pattern_remove.match(c)]]
-
-    main_df  = remove_pattern_cols(main_df)
-    const_df = remove_pattern_cols(const_df)
-    rem_df   = remove_pattern_cols(rem_df)
-
-    # ================= TABLE COLOR HELPER =================
-    def df_to_html_color(df, metric_col=None):
-        if df is None or df.empty:
-            return "<i>No data</i>"
-
-        df_html = df.copy()
-        top_up, top_down = [], []
-
-        if metric_col and metric_col in df_html.columns:
-            col_num = pd.to_numeric(df_html[metric_col], errors="coerce").dropna()
-            top_up = col_num.nlargest(3).index.tolist()
-            top_down = col_num.nsmallest(3).index.tolist()
-
-        for idx, row in df_html.iterrows():
-            for col in df_html.columns:
-                val = row[col]
-                cls = ""
-                if isinstance(val, (int, float)):
-                    val_fmt = f"{val:.2f}"
-                    if val > 0:
-                        cls = "numeric-positive"
-                    elif val < 0:
-                        cls = "numeric-negative"
-                    if metric_col and col == metric_col:
-                        if idx in top_up:
-                            cls += " top-up"
-                        elif idx in top_down:
-                            cls += " top-down"
-                    df_html.at[idx, col] = f'<span class="{cls.strip()}">{val_fmt}</span>'
-                else:
-                    df_html.at[idx, col] = str(val)
-
-        return df_html.to_html(index=False, escape=False, classes="compact-table")
-
-    # ================= MINI INFO CARDS =================
-    def build_info_cards(rem_df, main_df):
-        combined = pd.concat([rem_df, main_df], axis=1)
-        combined = combined.loc[:, ~combined.columns.duplicated()]
-        combined = remove_pattern_cols(combined)
-
-        html = '<div class="mini-card-container">'
-        for col in combined.columns:
-            val = combined.at[0, col] if not combined.empty else ""
-            html += f"""
-            <div class="mini-card">
-                <div class="card-key">{col}</div>
-                <div class="card-val">{val}</div>
-            </div>
-            """
-        html += '</div>'
-        return html
-
-    info_cards_html = build_info_cards(rem_df, main_df)
-
-    # ================= CONSTITUENTS TABLE =================
-    cons_html = df_to_html_color(const_df)
-
-    # ================= METRIC TABLES =================
-    metric_cols_allowed = [
-        "pChange",
-        "totalTurnover",
-        "marketCap",
-        "totalTradedVolume"
-    ]
-
-    metric_tables = ""
-    for col in metric_cols_allowed:
-        if col in const_df.columns:
-            df_m = const_df.copy()
-            df_m[col] = pd.to_numeric(df_m[col], errors="coerce")
-            df_m = df_m.sort_values(col, ascending=False)
-
-            show_cols = ["symbol", col] if "symbol" in df_m.columns else [col]
-            metric_tables += f"""
-            <div class="small-table">
-                <div class="st-title">{col}</div>
-                <div class="st-body">
-                    {df_to_html_color(df_m[show_cols], metric_col=col)}
-                </div>
-            </div>
-            """
-
-    # ================= FINAL HTML =================
-    html_out = f"""
-<!DOCTYPE html>
-<html>
-<head>
-<meta charset="UTF-8">
-<style>
-body {{ font-family: Arial; margin: 12px; background: #f5f5f5; font-size: 14px; }}
-h2, h3 {{ margin: 10px 0; }}
-table {{ border-collapse: collapse; width: 100%; }}
-th, td {{ border: 1px solid #bbb; padding: 6px; font-size: 13px; }}
-th {{ background: #333; color: #fff; }}
-.numeric-positive {{ color: green; font-weight: bold; }}
-.numeric-negative {{ color: red; font-weight: bold; }}
-.top-up {{ background: #b6f2b6; }}
-.top-down {{ background: #f2b6b6; }}
-.grid {{ display: grid; grid-template-columns: repeat(5, 1fr); gap: 12px; }}
-.small-table {{ background: #fff; padding: 8px; border-radius: 6px; border: 1px solid #ddd; }}
-.st-title {{ text-align: center; font-weight: bold; background: #222; color: #fff; padding: 6px; }}
-.st-body {{ max-height: 300px; overflow-y: auto; }}
-.mini-card-container {{ display: flex; flex-wrap: wrap; gap: 10px; }}
-.mini-card {{ background: #fff; padding: 8px 10px; border-radius: 6px; border: 1px solid #ddd; min-width: 120px; }}
-.card-key {{ font-weight: bold; }}
-</style>
-</head>
-<body>
-
-<h2>Pre-Open Market — {key}</h2>
-
-<h3>Info</h3>
-{info_cards_html}
-
-<h3>Constituents</h3>
-{cons_html}
-
-<h3>Key Metrics</h3>
-<div class="grid">
-{metric_tables}
-</div>
-
-</body>
-</html>
-"""
-
-    # ================= SAVE (HTML ONLY) =================
-    save(cache_name, html_out, "html")
-
-    return html_out