core/crypto_dashboard.py

"""
Crypto Dashboard — Plotly Edition (clean layout)
• убраны colorbar заголовки (percent_change_*)
• уменьшены отступы KPI
• без глобального Markdown-заголовка
"""
import requests
import pandas as pd
import plotly.express as px
from services.llm_client import llm_service


def fetch_coinlore_data(limit=100):
    url = "https://api.coinlore.net/api/tickers/"
    data = requests.get(url).json()["data"]
    df = pd.DataFrame(data)
    for col in ["price_usd", "market_cap_usd", "volume24",
                "percent_change_1h", "percent_change_24h", "percent_change_7d"]:
        df[col] = pd.to_numeric(df[col], errors="coerce")
    return df.head(limit)


def _kpi_line(df) -> str:
    """Формирует компактную KPI-строку без лишних пробелов"""
    tracked = ["BTC", "ETH", "SOL", "DOGE"]
    parts = []
    for sym in tracked:
        row = df[df["symbol"] == sym]
        if row.empty:
            continue
        price = float(row["price_usd"])
        ch = float(row["percent_change_24h"])
        arrow = "↑" if ch > 0 else "↓"
        color = "#4ade80" if ch > 0 else "#f87171"
        parts.append(
            f"<b>{sym}</b> ${price:,.0f} "
            f"<span style='color:{color}'>{arrow} {abs(ch):.2f}%</span>"
        )
    return " , ".join(parts)


def build_crypto_dashboard(top_n=50):
    df = fetch_coinlore_data(top_n)

    # === Treemap ===
    fig_treemap = px.treemap(
        df,
        path=["symbol"],
        values="market_cap_usd",
        color="percent_change_24h",
        color_continuous_scale="RdYlGn",
        height=420,
    )
    fig_treemap.update_layout(
        title=None,
        template="plotly_dark",
        coloraxis_colorbar=dict(title=None),  # 🔹 убираем надпись percent_change_24h
        margin=dict(l=5, r=5, t=5, b=5),
        paper_bgcolor="rgba(0,0,0,0)",
        plot_bgcolor="rgba(0,0,0,0)",
    )

    # === Bar chart (Top gainers) ===
    top = df.sort_values("percent_change_24h", ascending=False).head(12)
    fig_bar = px.bar(
        top,
        x="percent_change_24h",
        y="symbol",
        orientation="h",
        color="percent_change_24h",
        color_continuous_scale="Blues",
        height=320,
    )
    fig_bar.update_layout(
        title=None,
        template="plotly_dark",
        coloraxis_colorbar=dict(title=None),  # 🔹 убираем надпись percent_change_24h
        margin=dict(l=40, r=10, t=5, b=18),
        paper_bgcolor="rgba(0,0,0,0)",
        plot_bgcolor="rgba(0,0,0,0)",
    )

    # === Scatter (Market Cap vs Volume) ===
    fig_bubble = px.scatter(
        df.head(60),
        x="market_cap_usd",
        y="volume24",
        size="price_usd",
        color="percent_change_7d",
        hover_name="symbol",
        log_x=True,
        log_y=True,
        color_continuous_scale="RdYlGn",
        height=320,
    )
    fig_bubble.update_layout(
        title=None,
        template="plotly_dark",
        coloraxis_colorbar=dict(title=None),  # 🔹 убираем надпись percent_change_7d
        margin=dict(l=36, r=10, t=5, b=18),
        paper_bgcolor="rgba(0,0,0,0)",
        plot_bgcolor="rgba(0,0,0,0)",
    )

    # === LLM summary ===
    summary = _ai_summary(df)
    kpi_text = _kpi_line(df)
    return fig_treemap, fig_bar, fig_bubble, summary, kpi_text


def _ai_summary(df):
    leaders = df.sort_values("percent_change_24h", ascending=False).head(3)["symbol"].tolist()
    laggards = df.sort_values("percent_change_24h").head(3)["symbol"].tolist()
    prompt = f"""
Summarize today's crypto market based on Coinlore data.
Top gainers: {', '.join(leaders)}.
Top losers: {', '.join(laggards)}.
Include: overall sentiment, volatility/liquidity, short-term outlook.
"""
    text = ""
    for delta in llm_service.stream_chat(
        messages=[{"role": "user", "content": prompt}],
        model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    ):
        text += delta
    return text