ai_dashboard.py

import streamlit as st
import plotly.graph_objects as go
import yfinance as yf
from transformers import pipeline

class AIDashboard:
    def __init__(self):
        self.sentiment_model = pipeline(
            "sentiment-analysis",
            model="yiyanghkust/finbert-tone"
        )
    
    def render(self):
        st.title("🧠 AI Stock Research Lab")
        
        tab1, tab2, tab3 = st.tabs([
            "📰 News Sentiment",
            "📈 Technical Analysis",
            "💬 AI Chat Analyst"
        ])
        
        with tab1:
            self.news_sentiment_tab()
        
        with tab2:
            self.technical_analysis_tab()
        
        with tab3:
            self.ai_chat_tab()
    
    def news_sentiment_tab(self):
        st.subheader("Financial News Sentiment Analysis")
        
        # Input for multiple stocks
        symbols = st.text_input("Enter stock symbols (comma-separated):", 
                               "AAPL, MSFT, NVDA, TSLA")
        
        if st.button("Analyze News Sentiment"):
            symbol_list = [s.strip() for s in symbols.split(',')]
            
            for symbol in symbol_list[:5]:  # Limit to 5
                with st.expander(f"📊 {symbol} News Analysis"):
                    try:
                        ticker = yf.Ticker(symbol)
                        news = ticker.news[:3]  # Get 3 latest news
                        
                        if news:
                            total_score = 0
                            for item in news:
                                title = item.get('title', 'No title')
                                st.write(f"**Headline**: {title}")
                                
                                # Analyze sentiment
                                result = self.sentiment_model(title[:512])
                                sentiment = result[0]['label']
                                score = result[0]['score']
                                total_score += score if sentiment == 'Positive' else -score
                                
                                # Display sentiment
                                if sentiment == 'Positive':
                                    st.success(f"✅ Positive ({score:.2%})")
                                elif sentiment == 'Negative':
                                    st.error(f"❌ Negative ({score:.2%})")
                                else:
                                    st.info(f"📊 Neutral ({score:.2%})")
                            
                            # Overall sentiment
                            avg_sentiment = total_score / len(news)
                            st.metric("Overall Sentiment Score", f"{avg_sentiment:.2%}")
                        else:
                            st.warning("No recent news available")
                    except Exception as e:
                        st.error(f"Error analyzing {symbol}: {str(e)}")
    
    def technical_analysis_tab(self):
        st.subheader("AI-Powered Technical Analysis")
        
        symbol = st.text_input("Stock Symbol:", "AAPL")
        period = st.selectbox("Time Period", ["1mo", "3mo", "6mo", "1y"])
        
        if st.button("Generate AI Analysis"):
            try:
                # Get data
                ticker = yf.Ticker(symbol)
                hist = ticker.history(period=period)
                
                if len(hist) > 0:
                    # Create interactive chart
                    fig = go.Figure(data=[go.Candlestick(
                        x=hist.index,
                        open=hist['Open'],
                        high=hist['High'],
                        low=hist['Low'],
                        close=hist['Close'],
                        name='Price'
                    )])
                    
                    # Add moving averages
                    hist['SMA_20'] = hist['Close'].rolling(window=20).mean()
                    hist['SMA_50'] = hist['Close'].rolling(window=50).mean()
                    
                    fig.add_trace(go.Scatter(
                        x=hist.index,
                        y=hist['SMA_20'],
                        name='20-Day MA',
                        line=dict(color='orange', width=2)
                    ))
                    
                    fig.add_trace(go.Scatter(
                        x=hist.index,
                        y=hist['SMA_50'],
                        name='50-Day MA',
                        line=dict(color='blue', width=2)
                    ))
                    
                    fig.update_layout(
                        title=f"{symbol} Technical Analysis",
                        yaxis_title="Price ($)",
                        xaxis_title="Date",
                        template="plotly_dark"
                    )
                    
                    st.plotly_chart(fig, use_container_width=True)
                    
                    # AI-generated insights
                    current_price = hist['Close'].iloc[-1]
                    sma_20 = hist['SMA_20'].iloc[-1]
                    sma_50 = hist['SMA_50'].iloc[-1]
                    
                    st.subheader("🤖 AI Technical Insights")
                    
                    if current_price > sma_20 and current_price > sma_50:
                        st.success("**BULLISH SIGNAL**: Price above both moving averages")
                        st.write("AI Recommendation: Consider buying on pullbacks")
                    elif current_price < sma_20 and current_price < sma_50:
                        st.error("**BEARISH SIGNAL**: Price below both moving averages")
                        st.write("AI Recommendation: Consider selling or waiting")
                    else:
                        st.warning("**NEUTRAL/MIXED SIGNALS**")
                        st.write("AI Recommendation: Hold and monitor")
                        
            except Exception as e:
                st.error(f"Error: {str(e)}")
    
    def ai_chat_tab(self):
        st.subheader("💬 AI Stock Analyst Chat")
        
        # Simple chat interface
        user_question = st.text_input("Ask about any stock or trading strategy:")
        
        if user_question:
            # Simple response logic (enhance with actual LLM)
            responses = {
                "buy": "Based on technical analysis, consider buying when price is above 50-day moving average with increasing volume.",
                "sell": "Consider selling if stock breaks below key support levels or shows bearish divergence.",
                "hold": "Hold if fundamentals remain strong despite short-term volatility.",
                "portfolio": "For your portfolio, focus on diversification and risk management."
            }
            
            question_lower = user_question.lower()
            
            if "buy" in question_lower:
                st.info(responses["buy"])
            elif "sell" in question_lower:
                st.info(responses["sell"])
            elif "hold" in question_lower:
                st.info(responses["hold"])
            elif "portfolio" in question_lower:
                st.info(responses["portfolio"])
            else:
                st.info("AI Analysis: Consider both technical and fundamental factors before making investment decisions.")