import streamlit as st
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import ta

def render_detail(symbol: str, coin_data: dict, ohlcv_df: pd.DataFrame,
                  news_articles: list, social_sentiment: dict, ai_summary: str):
    symbol_short = symbol.replace("USDT", "")
    signal = coin_data.get("signal", "HOLD")

    st.header(f"{symbol_short}/USDT")
    cols = st.columns(5)
    cols[0].metric("Signal", signal)
    cols[1].metric("Score", f"{coin_data.get('composite', 0):.0f}/100")
    cols[2].metric("Technical", f"{coin_data.get('technical', 0):.0f}")
    cols[3].metric("News", f"{coin_data.get('news', 0):.0f}")
    cols[4].metric("Social / AI", f"{coin_data.get('social', 0):.0f} / {coin_data.get('ai', 0):.0f}")

    tab_chart, tab_news, tab_social, tab_ai = st.tabs(["Chart", "News", "Social", "AI Analysis"])

    with tab_chart:
        _render_chart(ohlcv_df, symbol_short)
    with tab_news:
        _render_news(news_articles)
    with tab_social:
        _render_social(social_sentiment)
    with tab_ai:
        _render_ai(ai_summary)

def _render_chart(df: pd.DataFrame, symbol: str):
    if df is None or df.empty:
        st.info("No chart data available")
        return
    df = df.copy()
    df["rsi"] = ta.momentum.RSIIndicator(df["close"]).rsi()
    macd = ta.trend.MACD(df["close"])
    df["macd"] = macd.macd()
    df["macd_signal"] = macd.macd_signal()
    bb = ta.volatility.BollingerBands(df["close"])
    df["bb_high"] = bb.bollinger_hband()
    df["bb_low"] = bb.bollinger_lband()
    df["sma20"] = df["close"].rolling(20).mean()

    fig = make_subplots(rows=3, cols=1, shared_xaxes=True, vertical_spacing=0.05,
                        row_heights=[0.6, 0.2, 0.2],
                        subplot_titles=(f"{symbol} Price", "RSI", "MACD"))
    fig.add_trace(go.Candlestick(x=df["timestamp"], open=df["open"], high=df["high"],
                                  low=df["low"], close=df["close"], name="Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=df["timestamp"], y=df["bb_high"], line=dict(color="rgba(173,216,230,0.3)"), name="BB Upper"), row=1, col=1)
    fig.add_trace(go.Scatter(x=df["timestamp"], y=df["bb_low"], line=dict(color="rgba(173,216,230,0.3)"), fill="tonexty", name="BB Lower"), row=1, col=1)
    fig.add_trace(go.Scatter(x=df["timestamp"], y=df["sma20"], line=dict(color="orange", width=1), name="SMA20"), row=1, col=1)
    fig.add_trace(go.Scatter(x=df["timestamp"], y=df["rsi"], line=dict(color="purple"), name="RSI"), row=2, col=1)
    fig.add_hline(y=70, line_dash="dash", line_color="red", row=2, col=1)
    fig.add_hline(y=30, line_dash="dash", line_color="green", row=2, col=1)
    fig.add_trace(go.Scatter(x=df["timestamp"], y=df["macd"], line=dict(color="blue"), name="MACD"), row=3, col=1)
    fig.add_trace(go.Scatter(x=df["timestamp"], y=df["macd_signal"], line=dict(color="red"), name="Signal"), row=3, col=1)
    fig.update_layout(template="plotly_dark", height=700, showlegend=False, xaxis_rangeslider_visible=False)
    st.plotly_chart(fig, use_container_width=True)

def _render_news(articles: list):
    if not articles:
        st.info("No recent news for this coin")
        return
    for article in articles[:10]:
        votes = article.get("sentiment_votes", {})
        pos = votes.get("positive", 0)
        neg = votes.get("negative", 0)
        if pos > neg:
            icon = ":green_circle:"
        elif neg > pos:
            icon = ":red_circle:"
        else:
            icon = ":white_circle:"
        st.markdown(f"{icon} **{article['title']}**")
        st.caption(f"Published: {article.get('published_at', 'N/A')} | +{pos} -{neg}")
        st.divider()

def _render_social(sentiment: dict):
    if not sentiment or sentiment.get("total", 0) == 0:
        st.info("No social data available")
        return
    total = sentiment["total"]
    cols = st.columns(3)
    cols[0].metric("Positive", f"{sentiment['positive']}/{total}", f"{sentiment['positive']/total*100:.0f}%")
    cols[1].metric("Negative", f"{sentiment['negative']}/{total}", f"-{sentiment['negative']/total*100:.0f}%")
    cols[2].metric("Neutral", f"{sentiment['neutral']}/{total}")

def _render_ai(summary: str):
    if not summary:
        st.info("AI analysis not available")
        return
    st.markdown(summary)