from . import nsepythonmodified as ns
import pandas as pd
from datetime import datetime
import os
# ==============================
# Simple daily cache helpers
# ==============================
CACHE_DIR = "./cache_eq"
os.makedirs(CACHE_DIR, exist_ok=True)
def _cache_path(key):
return os.path.join(CACHE_DIR, f"{key}.html")
def _is_today(path):
if not os.path.exists(path):
return False
mtime = datetime.fromtimestamp(os.path.getmtime(path)).date()
return mtime == datetime.now().date()
def cache_load(key):
path = _cache_path(key)
if _is_today(path):
try:
with open(path, "r", encoding="utf-8") as f:
return f.read()
except:
return False
return False
def cache_save(key, html):
with open(_cache_path(key), "w", encoding="utf-8") as f:
f.write(html)
# ==============================
# MAIN FUNCTION (CACHED)
# ==============================
def build_eq_html(symbol):
"""
Build full HTML page for eq(symbol) output
DAILY CACHED
"""
cache_key = f"eq_{symbol.upper()}"
# ---------- CACHE HIT ----------
cached = cache_load(cache_key)
if cached:
return cached
# -------------------------------------------------------
# CALL eq() function
# -------------------------------------------------------
out = ns.eq(symbol)
if not isinstance(out, dict):
return "
Error: EQ data not available
"
# -------------------------------------------------------
# Helper: DataFrame → HTML table
# -------------------------------------------------------
def df_to_table(df):
if df is None or df.empty:
return '
No data
'
return df.to_html(index=False, escape=False, border=0, classes="tbl")
# -------------------------------------------------------
# ORDER
# -------------------------------------------------------
section_order = [
"metadata",
"securityInfo",
"priceInfo",
"industryInfo",
"pdSectorIndAll",
"info",
"preOpen",
"preOpenMarket"
]
# Normalize to DataFrames
normalized = {}
for sec in section_order:
val = out.get(sec)
if isinstance(val, pd.DataFrame):
normalized[sec] = val
elif isinstance(val, list):
normalized[sec] = pd.DataFrame(val)
elif isinstance(val, dict):
normalized[sec] = pd.DataFrame([val])
else:
normalized[sec] = pd.DataFrame()
# -------------------------------------------------------
# Sections HTML
# -------------------------------------------------------
section_html = ""
for sec in section_order:
section_html += f"""
{sec}
{df_to_table(normalized[sec])}
"""
# -------------------------------------------------------
# FINAL HTML
# -------------------------------------------------------
html = f"""
Equity Report - {symbol}
Equity Report — {symbol}
{section_html}
"""
# ---------- SAVE CACHE ----------
cache_save(cache_key, html)
return html