Delete app/yahooinfo2.py

app/yahooinfo2.py

@@ -1,382 +0,0 @@
-# ==============================
-# Imports (kept same style)
-# ==============================
-import yfinance as yf
-import pandas as pd
-import traceback
-import time
-
-# persist helpers
-from .persist import exists, load, save
-
-
-# ==============================
-# Yahoo Finance info fetch (RAW)
-# ==============================
-def yfinfo(symbol):
-    """
-    Low-level Yahoo Finance info fetch.
-    Returns raw dict or {"__error__": "..."}
-    """
-    try:
-        t = yf.Ticker(symbol + ".NS")
-        info = t.info
-        if not info or not isinstance(info, dict):
-            return {}
-        return info
-    except Exception as e:
-        return {"__error__": str(e)}
-
-
-# ==============================
-# Icons
-# ==============================
-SUBGROUP_ICONS = {
-    "Live Price": "💹",
-    "Volume": "📊",
-    "Moving Avg": "📈",
-    "Range / Vol": "📉",
-    "Bid / Analyst": "📝",
-    "Other": "ℹ️"
-}
-
-MAIN_ICONS = {
-    "Price / Volume": "📈",
-    "Fundamentals": "📊",
-    "Company Profile": "🏢"
-}
-
-
-# ==============================
-# Responsive column layout
-# ==============================
-def column_layout(html, min_width=320):
-    return f"""
-    <div style="
-        display:grid;
-        grid-template-columns:repeat(auto-fit,minmax({min_width}px,1fr));
-        gap:10px;
-        align-items:start;
-    ">
-        {html}
-    </div>
-    """
-
-
-# ==============================
-# Card renderer
-# ==============================
-def html_card(title, body, mini=False, shade=0):
-    font = "12px" if mini else "14px"
-    pad  = "6px" if mini else "10px"
-
-    shades = ["#e6f0fa", "#d7e3f5", "#c8d6f0"]
-    grads = [
-        "linear-gradient(to right,#1a4f8a,#4a7ac7)",
-        "linear-gradient(to right,#1f5595,#5584d6)",
-        "linear-gradient(to right,#205ca0,#6192e0)"
-    ]
-
-    return f"""
-    <div style="
-        background:{shades[shade%3]};
-        border:1px solid #a3c0e0;
-        border-radius:8px;
-        padding:{pad};
-        font-size:{font};
-        box-shadow:0 2px 6px rgba(0,0,0,.08);
-    ">
-        <div style="
-            background:{grads[shade%3]};
-            color:white;
-            padding:4px 8px;
-            border-radius:6px;
-            font-weight:600;
-            margin-bottom:6px;
-        ">
-            {title}
-        </div>
-        {body}
-    </div>
-    """
-
-
-# ==============================
-# Formatting helpers
-# ==============================
-def format_number(x):
-    try:
-        x = float(x)
-        if abs(x) >= 100:
-            return f"{x:,.0f}"
-        if abs(x) >= 1:
-            return f"{x:,.2f}"
-        return f"{x:.4f}"
-    except:
-        return str(x)
-
-
-# ==============================
-# Compact inline key:value view
-# ==============================
-def make_table(df):
-    rows = ""
-    for _, r in df.iterrows():
-        color = "#0d1f3c"
-        if any(x in r[0].lower() for x in ["chg", "%"]):
-            try:
-                color = "#0a7d32" if float(r[1]) >= 0 else "#b00020"
-            except:
-                pass
-
-        rows += f"""
-        <div style="
-            display:flex;
-            justify-content:space-between;
-            gap:6px;
-            padding:2px 0;
-            border-bottom:1px dashed #bcd0ea;
-        ">
-            <span style="color:#1a4f8a;font-weight:500;">
-                {r[0]}
-            </span>
-            <span style="
-                color:{color};
-                font-weight:600;
-                background:#f1f6ff;
-                padding:1px 6px;
-                border-radius:4px;
-            ">
-                {r[1]}
-            </span>
-        </div>
-        """
-    return f"<div>{rows}</div>"
-
-
-# ==============================
-# Noise filtering
-# ==============================
-NOISE_KEYS = {
-    "maxAge","priceHint","triggerable",
-    "customPriceAlertConfidence",
-    "sourceInterval","exchangeDataDelayedBy",
-    "esgPopulated"
-}
-
-def is_noise(k):
-    return k in NOISE_KEYS
-
-
-# ==============================
-# Duplicate resolver
-# ==============================
-DUPLICATE_PRIORITY = {
-    "price": ["regularMarketPrice","currentPrice"],
-    "prev": ["regularMarketPreviousClose","previousClose"],
-    "open": ["regularMarketOpen","open"],
-    "high": ["regularMarketDayHigh","dayHigh"],
-    "low": ["regularMarketDayLow","dayLow"],
-    "volume": ["regularMarketVolume","volume"]
-}
-
-def resolve_duplicates(data):
-    resolved, used = {}, set()
-    for keys in DUPLICATE_PRIORITY.values():
-        for k in keys:
-            if k in data:
-                resolved[k] = data[k]
-                used.update(keys)
-                break
-    for k,v in data.items():
-        if k not in used:
-            resolved[k] = v
-    return resolved
-
-
-# ==============================
-# Short key names
-# ==============================
-SHORT_NAMES = {
-    "regularMarketPrice":"Price",
-    "regularMarketChange":"Chg",
-    "regularMarketChangePercent":"Chg%",
-    "regularMarketPreviousClose":"Prev",
-    "regularMarketOpen":"Open",
-    "regularMarketDayHigh":"High",
-    "regularMarketDayLow":"Low",
-    "regularMarketVolume":"Vol",
-    "averageDailyVolume10Day":"AvgV10",
-    "averageDailyVolume3Month":"AvgV3M",
-    "fiftyDayAverage":"50DMA",
-    "twoHundredDayAverage":"200DMA",
-    "fiftyTwoWeekLow":"52WL",
-    "fiftyTwoWeekHigh":"52WH",
-    "beta":"Beta",
-    "targetMeanPrice":"Target"
-}
-
-def pretty_key(k):
-    return SHORT_NAMES.get(k, k[:12])
-
-
-# ==============================
-# Classifiers
-# ==============================
-def classify_price_volume_subgroup(key):
-    k = key.lower()
-    if "volume" in k: return "Volume"
-    if "average" in k or "dma" in k: return "Moving Avg"
-    if "week" in k or "beta" in k: return "Range / Vol"
-    if "target" in k or "recommend" in k: return "Bid / Analyst"
-    return "Live Price"
-
-
-def classify_key(key, value):
-    k = key.lower()
-    if isinstance(value,(int,float)) and any(x in k for x in [
-        "price","volume","avg","change","percent","market","week","beta","target"
-    ]):
-        return "price_volume"
-    if any(x in k for x in [
-        "revenue","income","profit","margin","pe","pb","roe","roa","debt","equity"
-    ]):
-        return "fundamental"
-    if isinstance(value,str) and len(value) > 80:
-        return "long_text"
-    return "profile"
-
-
-# ==============================
-# Group builder
-# ==============================
-def build_grouped_info(info):
-    groups = {
-        "price_volume":{},
-        "fundamental":{},
-        "profile":{},
-        "long_text":{}
-    }
-    for k,v in info.items():
-        if v in [None,"",[],{}]:
-            continue
-        groups[classify_key(k,v)][k] = v
-    return groups
-
-
-# ==============================
-# Column splitter
-# ==============================
-def split_df_evenly(df):
-    if df is None or df.empty:
-        return []
-
-    n = len(df)
-    cols = 1 if n <= 6 else 2 if n <= 14 else 3
-    chunk = (n + cols - 1) // cols
-    return [df.iloc[i:i+chunk] for i in range(0, n, chunk)]
-
-
-# ==============================
-# DataFrame builder
-# ==============================
-def build_df_from_dict(data):
-    rows = []
-    for k,v in data.items():
-        if is_noise(k):
-            continue
-        if isinstance(v,(int,float)):
-            v = format_number(v)
-        rows.append([pretty_key(k), v])
-    return pd.DataFrame(rows, columns=["Field","Value"])
-
-
-# ==============================
-# MAIN FUNCTION (CACHED)
-# ==============================
-def fetch_info(symbol):
-    """
-    Cached Yahoo Finance info renderer
-    Cache validity: 1 hour
-    """
-    key = f"info_{symbol}"
-
-    # ---------- CACHE CHECK ----------
-    if exists(key, "html"):
-        cached = load(key, "html")
-        if cached:
-            return cached
-
-    try:
-        info = yfinfo(symbol)
-
-        # ---------- VALIDATION ----------
-        if not info or "__error__" in info:
-            return "No data"
-
-        groups = build_grouped_info(info)
-        html = ""
-
-        # ---------- PRICE / VOLUME ----------
-        pv = resolve_duplicates(groups["price_volume"])
-        sub = {}
-        for k,v in pv.items():
-            sg = classify_price_volume_subgroup(k)
-            sub.setdefault(sg,{})[k] = v
-
-        cards = ""
-        for i,(t,d) in enumerate(sub.items()):
-            df = build_df_from_dict(d)
-            if not df.empty:
-                cards += html_card(
-                    f"{SUBGROUP_ICONS.get(t,'ℹ️')} {t}",
-                    make_table(df),
-                    mini=True,
-                    shade=i
-                )
-
-        if cards:
-            html += html_card(
-                f"{MAIN_ICONS['Price / Volume']} Price / Volume",
-                column_layout(cards),
-                shade=0
-            )
-
-        # ---------- FUNDAMENTALS ----------
-        if groups["fundamental"]:
-            chunks = split_df_evenly(build_df_from_dict(groups["fundamental"]))
-            cols = "".join(
-                html_card("📊 Fundamentals", make_table(c), mini=True, shade=i)
-                for i,c in enumerate(chunks)
-            )
-            html += html_card(
-                f"{MAIN_ICONS['Fundamentals']} Fundamentals",
-                column_layout(cols),
-                shade=1
-            )
-
-        # ---------- COMPANY PROFILE ----------
-        if groups["profile"]:
-            chunks = split_df_evenly(build_df_from_dict(groups["profile"]))
-            cols = "".join(
-                html_card("🏢 Profile", make_table(c), mini=True, shade=i)
-                for i,c in enumerate(chunks)
-            )
-            html += html_card(
-                f"{MAIN_ICONS['Company Profile']} Company Profile",
-                column_layout(cols),
-                shade=2
-            )
-
-        # ---------- LONG TEXT ----------
-        for k,v in groups["long_text"].items():
-            html += html_card(pretty_key(k), v, shade=2)
-
-        # ---------- SAVE CACHE ----------
-        if html.strip():
-            save(key, html, "html")
-
-        return html
-
-    except Exception:
-        return f"<pre>{traceback.format_exc()}</pre>"