app.py

import streamlit as st
import plotly.graph_objs as go
import plotly.express as px
from plotly.subplots import make_subplots
from main import CryptoCrew
import time
import os
import pandas as pd

st.set_page_config(page_title="Advanced Crypto Analyst", page_icon="📈", layout="wide")

# Custom CSS for better styling
st.markdown("""
<style>
.metric-card {
    background-color: #f0f2f6;
    padding: 1rem;
    border-radius: 0.5rem;
    margin: 0.5rem 0;
}
.positive { color: #00ff00; }
.negative { color: #ff0000; }
.neutral { color: #808080; }
</style>
""", unsafe_allow_html=True)

st.title("⚡ Advanced Crypto Analyst")
st.markdown("*Powered by Together AI with Enhanced Multi-Agent Analysis*")

# Enhanced caching with longer TTL for detailed analysis
@st.cache_data(ttl=600)  # Cache for 10 minutes
def analyze_crypto(crypto_name):
    crypto_crew = CryptoCrew(crypto_name.lower())
    return crypto_crew.run()

# Input section
col1, col2 = st.columns([3, 1])
with col1:
    crypto = st.text_input("Enter cryptocurrency name:", placeholder="bitcoin, ethereum, solana, cardano...")

with col2:
    st.markdown("<br>", unsafe_allow_html=True)
    analyze_btn = st.button("🚀 Analyze", type="primary", use_container_width=True)

if analyze_btn and crypto:
    start_time = time.time()
    
    with st.spinner("🔍 Performing comprehensive analysis... This may take 30-60 seconds for detailed results!"):
        try:
            result = analyze_crypto(crypto)
            end_time = time.time()
            
            # Enhanced header metrics
            st.markdown("## 📊 Analysis Dashboard")
            
            col1, col2, col3, col4 = st.columns(4)
            with col1:
                st.metric("Analysis Time", f"{end_time - start_time:.1f}s", "✅ Complete")
            with col2:
                rec = result.get("recommendation", {}).get("action", "HOLD")
                confidence = result.get("recommendation", {}).get("confidence", "Medium")
                st.metric("Recommendation", rec, f"Confidence: {confidence}")
            with col3:
                risk = result.get("risk_assessment", "Moderate Risk")
                st.metric("Risk Level", risk)
            with col4:
                last_updated = result.get("last_updated", "Unknown")
                st.metric("Last Updated", last_updated.split()[1] if " " in last_updated else "N/A")
            
            # Market Data Section
            st.markdown("## 💰 Market Metrics")
            market_data = result.get("market_data", {})
            
            col1, col2, col3 = st.columns(3)
            with col1:
                price = market_data.get("current_price", "N/A")
                price_change_24h = market_data.get("price_change_24h", "N/A")
                st.metric("Current Price", price, price_change_24h)
                
            with col2:
                market_cap = market_data.get("market_cap", "N/A")
                st.metric("Market Cap", market_cap)
                
            with col3:
                volume_24h = market_data.get("volume_24h", "N/A")
                st.metric("24h Volume", volume_24h)
            
            col4, col5, col6 = st.columns(3)
            with col4:
                price_change_7d = market_data.get("price_change_7d", "N/A")
                st.metric("7-Day Change", price_change_7d)
            with col5:
                dominance = market_data.get("market_dominance", "N/A")
                st.metric("Market Dominance", dominance)
            with col6:
                st.metric("Analysis Depth", "Advanced", "🎯 Multi-Agent")

            # Technical Analysis Section
            st.markdown("## 📈 Technical Analysis")
            technical_data = result.get("technical_data", {})
            
            col1, col2 = st.columns(2)
            with col1:
                rsi = technical_data.get("rsi", "N/A")
                rsi_signal = technical_data.get("rsi_signal", "Neutral")
                st.metric("RSI (14)", rsi, rsi_signal)
                
                trend = technical_data.get("trend", "Neutral")
                st.metric("Current Trend", trend)
                
            with col2:
                ma_7d = technical_data.get("moving_average_7d", "N/A")
                st.metric("7-Day MA", ma_7d)
                
                support = technical_data.get("support_level", "N/A")
                resistance = technical_data.get("resistance_level", "N/A")
                st.metric("Support | Resistance", f"{support} | {resistance}")

            # Enhanced Sentiment Analysis with Fixed Chart
            st.markdown("## 💭 Multi-Source Sentiment Analysis")
            sentiment_data = result.get("sentiment", {})
            
            # Create properly differentiated sentiment chart
            categories = list(sentiment_data.keys())
            values = []
            colors = []
            sentiment_texts = []
            
            for category, sentiment in sentiment_data.items():
                sentiment_texts.append(sentiment)
                if sentiment == "Positive":
                    values.append(1)
                    colors.append('#00C851')  # Green
                elif sentiment == "Negative":
                    values.append(-1)
                    colors.append('#FF4444')  # Red
                else:
                    values.append(0)
                    colors.append('#FFBB33')  # Orange for neutral
            
            # Create sentiment visualization
            fig = go.Figure(data=[go.Bar(
                x=categories,
                y=values,
                marker_color=colors,
                text=sentiment_texts,
                textposition='auto',
                hovertemplate='<b>%{x}</b><br>Sentiment: %{text}<br>Score: %{y}<extra></extra>'
            )])
            
            fig.update_layout(
                title="Sentiment Distribution Across Sources",
                xaxis_title="Analysis Source",
                yaxis_title="Sentiment Score",
                yaxis=dict(
                    tickvals=[-1, 0, 1], 
                    ticktext=["Negative", "Neutral", "Positive"],
                    range=[-1.2, 1.2]
                ),
                height=500,
                showlegend=False,
                plot_bgcolor='rgba(0,0,0,0)',
                paper_bgcolor='rgba(0,0,0,0)'
            )
            
            st.plotly_chart(fig, use_container_width=True)
            
            # Sentiment Details
            col1, col2, col3, col4 = st.columns(4)
            sentiments = ["overall", "social_media", "news", "community"]
            columns = [col1, col2, col3, col4]
            
            for sentiment_type, col in zip(sentiments, columns):
                sentiment_val = sentiment_data.get(sentiment_type, "Neutral")
                color_class = "positive" if sentiment_val == "Positive" else "negative" if sentiment_val == "Negative" else "neutral"
                col.markdown(f"**{sentiment_type.replace('_', ' ').title()}**")
                col.markdown(f'<span class="{color_class}">{sentiment_val}</span>', unsafe_allow_html=True)

            # Investment Recommendation Section
            st.markdown("## 🎯 Investment Recommendation")
            recommendation = result.get("recommendation", {})
            
            action = recommendation.get("action", "HOLD")
            confidence = recommendation.get("confidence", "Medium")
            reasoning = recommendation.get("reasoning", "Standard analysis completed")
            
            # Color-coded recommendation
            rec_colors = {"BUY": "🟢", "SELL": "🔴", "HOLD": "🟡"}
            rec_bg_colors = {"BUY": "#d4edda", "SELL": "#f8d7da", "HOLD": "#fff3cd"}
            
            st.markdown(f"""
            <div style="background-color: {rec_bg_colors.get(action, '#f8f9fa')}; 
                        padding: 1rem; border-radius: 0.5rem; margin: 1rem 0;">
                <h3>{rec_colors.get(action, '🟡')} Investment Recommendation: {action}</h3>
                <p><strong>Confidence Level:</strong> {confidence}</p>
                <p><strong>Reasoning:</strong> {reasoning}</p>
            </div>
            """, unsafe_allow_html=True)
            
            # Additional recommendation details
            col1, col2, col3 = st.columns(3)
            with col1:
                time_horizon = recommendation.get("time_horizon", "Medium-term")
                st.info(f"**Time Horizon:** {time_horizon}")
            with col2:
                risk_level = recommendation.get("risk_level", "Moderate")
                st.info(f"**Risk Level:** {risk_level}")
            with col3:
                st.info(f"**Analysis Type:** Multi-Agent AI")

            # Detailed Analysis Summary
            st.markdown("## 📋 Detailed Analysis Summary")
            with st.expander("View Full Analysis Report", expanded=False):
                st.write(result.get("summary", "No detailed summary available"))
                
            # Risk Assessment
            st.markdown("## ⚠️ Risk Assessment")
            st.warning(f"**Risk Level:** {result.get('risk_assessment', 'Moderate Risk')}")
            
        except Exception as e:
            st.error(f"Analysis failed: {str(e)}")
            st.info("""
            💡 **Troubleshooting Tips:**
            - Use full cryptocurrency names (e.g., 'bitcoin' not 'btc')
            - Ensure your API key is properly configured
            - Try again if the analysis times out
            - Check network connectivity
            """)

# Enhanced Sidebar
with st.sidebar:
    st.header("⚙️ System Status")
    
    # API Status Check
    api_key_status = "✅ Connected" if os.getenv("TOGETHER_API_KEY") else "❌ Missing API Key"
    st.write(f"**Together AI:** {api_key_status}")
    
    if not os.getenv("TOGETHER_API_KEY"):
        st.error("Add TOGETHER_API_KEY to your environment variables")
    else:
        st.success("API Configuration Valid")
        
    st.markdown("---")
    st.markdown("### 📊 Analysis Features")
    st.markdown("""
    ✅ **Market Data Analysis**
    - Real-time price & volume
    - Market cap & dominance
    - Price change tracking
    
    ✅ **Technical Analysis** 
    - RSI & Moving Averages
    - Support/Resistance levels
    - Trend identification
    
    ✅ **Sentiment Analysis**
    - Social media monitoring
    - News sentiment tracking
    - Community analysis
    
    ✅ **AI Recommendations**
    - Multi-agent analysis
    - Risk assessment
    - Entry/exit strategies
    """)
    
    st.markdown("---")
    st.markdown("### ⚡ Performance")
    st.markdown("""
    - **Analysis Time:** 30-60s
    - **Model:** Llama 3.1 8B Turbo
    - **Agents:** 3 Specialized AI Agents
    - **Data Sources:** Multiple APIs
    """)

# Footer
st.markdown("---")
st.markdown("### 🚀 Advanced Analytics Dashboard")
cols = st.columns(4)
with cols[0]:
    st.metric("AI Agents", "3", "🤖 Specialized")
with cols[1]:
    st.metric("Data Sources", "Multiple", "🔄 Real-time")
with cols[2]:
    st.metric("Analysis Depth", "Professional", "⭐ Institutional Grade")
with cols[3]:
    st.metric("Update Frequency", "Real-time", "🕐 Live Data")