Windsurf + Gigabrain

import os
import streamlit as st
import requests
import json
from datetime import datetime
from dotenv import load_dotenv

load_dotenv()

API_KEY = os.getenv("GIGABRAIN_API_KEY")
BASE_URL = "https://api.gigabrain.gg"

st.set_page_config(
    page_title="Gigabrain Dashboard",
    page_icon="🧠",
    layout="wide"
)

@st.cache_data(ttl=300)  # Cache for 5 minutes
def query_gigabrain(message):
    """Query Gigabrain API with caching"""
    try:
        response = requests.post(
            f"{BASE_URL}/v1/chat",
            headers={
                "Authorization": f"Bearer {API_KEY}",
                "Content-Type": "application/json"
            },
            json={"message": message},
            timeout=600
        )

        if response.status_code == 200:
            data = response.json()
            return json.loads(data["content"])
        else:
            st.error(f"API Error: {response.status_code}")
            return None
    except Exception as e:
        st.error(f"Error: {e}")
        return None

def main():
    st.title("🧠 Gigabrain Intelligence Dashboard")
    st.caption(f"Last updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")

    # Refresh button
    if st.button("🔄 Refresh Data"):
        st.cache_data.clear()
        st.rerun()

    # Create columns for different agents
    col1, col2 = st.columns(2)

    with col1:
        st.header("📈 Market Sentiment")
        with st.spinner("Fetching Fear & Greed..."):
            sentiment = query_gigabrain("""
                Get BTC fear and greed index. Respond as JSON with:
                fear_greed_index, fear_greed_label, btc_dominance,
                altcoin_season_index, market_cap_total
            """)

        if sentiment:
            # Fear & Greed gauge
            fg_index = sentiment["fear_greed_index"]
            fg_label = sentiment["fear_greed_label"]

            st.metric("Fear & Greed Index", f"{fg_index}/100", fg_label)
            st.progress(fg_index / 100)

            # Other metrics
            st.metric("BTC Dominance", f"{sentiment['btc_dominance']}%")
            st.metric("Altcoin Season Index", sentiment['altcoin_season_index'])

    with col2:
        st.header("💰 Funding Rates")
        with st.spinner("Fetching funding rates..."):
            funding = query_gigabrain("""
                Get funding rates for BTC, ETH, SOL. Respond as JSON array with:
                symbol, funding_rate, open_interest, long_short_ratio
            """)

        if funding:
            for token in funding:
                rate_pct = token["funding_rate"] * 100
                direction = "🟢 LONG" if rate_pct > 0 else "🔴 SHORT"

                st.metric(
                    f"{token['symbol']} Funding",
                    f"{rate_pct:.4f}%",
                    f"{direction} paying"
                )
                st.caption(f"OI: ${token['open_interest']:,.0f}")

    # Full width section for narratives
    st.header("🔥 Trending Narratives")
    with st.spinner("Fetching narratives..."):
        narratives = query_gigabrain("""
            Get current crypto narratives ranked by momentum. Respond as JSON array with:
            narrative, momentum_score, top_tokens, sentiment
        """)

    if narratives:
        for i, narrative in enumerate(narratives[:5], 1):
            with st.expander(f"{i}. {narrative['narrative']} (Momentum: {narrative['momentum_score']}/100)"):
                st.write(f"**Sentiment**: {narrative['sentiment']}")
                st.write(f"**Top Tokens**: {', '.join(narrative['top_tokens'])}")
                st.progress(narrative['momentum_score'] / 100)

    # Liquidations section
    st.header("⚡ Liquidations (24h)")
    with st.spinner("Fetching liquidation data..."):
        liquidations = query_gigabrain("""
            Get liquidation data for past 24 hours. Respond as JSON with:
            total_liquidations_usd, long_liquidations, short_liquidations,
            top_liquidated_tokens
        """)

    if liquidations:
        col1, col2, col3 = st.columns(3)

        with col1:
            st.metric("Total Liquidations", f"${liquidations['total_liquidations_usd']:,.0f}")
        with col2:
            st.metric("Long Liquidations", f"${liquidations['long_liquidations']:,.0f}")
        with col3:
            st.metric("Short Liquidations", f"${liquidations['short_liquidations']:,.0f}")

        if "top_liquidated_tokens" in liquidations:
            st.subheader("Top Liquidated Tokens")
            for token in liquidations["top_liquidated_tokens"][:5]:
                st.write(f"**{token['symbol']}**: ${token['amount']:,.0f}")

if __name__ == "__main__":
    main()