Enhance stock prediction and visualization features in app.py:

- Add Plotly for interactive charts and pandas for data manipulation.
- Update predict_stock_price to return historical data, forecast dates, and values.
- Implement tabs for displaying price history, predictions, and technical indicators (moving averages).
- Improve error handling and user feedback during stock analysis.

app.py

@@ -15,6 +15,9 @@ from statsmodels.tsa.arima.model import ARIMA
 import torch
 import re
 from typing import List, Union
+import plotly.graph_objects as go
+import pandas as pd
+from datetime import datetime, timedelta
 
 # Load environment variables from .env
 load_dotenv()
@@ -77,12 +80,20 @@ def predict_stock_price(ticker, days=5):
     hist = stock.history(period="6mo")
     if hist.empty:
         return {"error": f"No data found for ticker {ticker}"}
-
     
     model = ARIMA(hist["Close"], order=(5, 1, 0))
     model_fit = model.fit()
     forecast = model_fit.forecast(steps=days)
-    return forecast.tolist()
+    
+    # Create future dates for the forecast
+    last_date = hist.index[-1]
+    future_dates = pd.date_range(start=last_date + timedelta(days=1), periods=days, freq='B')
+    
+    return {
+        "historical_data": hist[["Close"]].to_dict(),
+        "forecast_dates": future_dates.strftime('%Y-%m-%d').tolist(),
+        "forecast_values": forecast.tolist()
+    }
 
 def compare_stocks(ticker1, ticker2):
     data1 = fetch_stock_data(ticker1)
@@ -234,13 +245,77 @@ if st.button("Submit"):
         st.write("Debug: User Query ->", query)
         with st.spinner("Processing..."):
             try:
-                # Run the agent and get the response
-                response = agent_executor.run(query)  # Correct method is `run()`
+                response = agent_executor.run(query)
                 st.success("Response:")
                 st.write(response)
+                
+                # Extract ticker from query (basic extraction, you might want to make this more robust)
+                possible_tickers = re.findall(r'[A-Z]{1,5}', query.upper())
+                if possible_tickers:
+                    ticker = possible_tickers[0]
+                    
+                    # Create tabs for different visualizations
+                    tab1, tab2, tab3 = st.tabs(["Price History", "Price Prediction", "Technical Indicators"])
+                    
+                    with tab1:
+                        st.subheader(f"{ticker} Price History")
+                        stock = yf.Ticker(ticker)
+                        hist = stock.history(period="1y")
+                        
+                        fig = go.Figure()
+                        fig.add_trace(go.Candlestick(
+                            x=hist.index,
+                            open=hist['Open'],
+                            high=hist['High'],
+                            low=hist['Low'],
+                            close=hist['Close'],
+                            name='OHLC'
+                        ))
+                        fig.update_layout(title=f"{ticker} Stock Price", xaxis_title="Date", yaxis_title="Price")
+                        st.plotly_chart(fig)
+                    
+                    with tab2:
+                        st.subheader(f"{ticker} Price Prediction")
+                        prediction_data = predict_stock_price(ticker)
+                        
+                        if "error" not in prediction_data:
+                            hist_df = pd.DataFrame(prediction_data["historical_data"])
+                            
+                            fig = go.Figure()
+                            # Plot historical data
+                            fig.add_trace(go.Scatter(
+                                x=hist_df.index,
+                                y=hist_df['Close'],
+                                name='Historical',
+                                line=dict(color='blue')
+                            ))
+                            # Plot predicted data
+                            fig.add_trace(go.Scatter(
+                                x=prediction_data["forecast_dates"],
+                                y=prediction_data["forecast_values"],
+                                name='Predicted',
+                                line=dict(color='red', dash='dash')
+                            ))
+                            fig.update_layout(title=f"{ticker} Price Prediction", xaxis_title="Date", yaxis_title="Price")
+                            st.plotly_chart(fig)
+                    
+                    with tab3:
+                        st.subheader(f"{ticker} Technical Indicators")
+                        # Calculate and plot moving averages
+                        hist['MA5'] = hist['Close'].rolling(window=5).mean()
+                        hist['MA20'] = hist['Close'].rolling(window=20).mean()
+                        hist['MA50'] = hist['Close'].rolling(window=50).mean()
+                        
+                        fig = go.Figure()
+                        fig.add_trace(go.Scatter(x=hist.index, y=hist['Close'], name='Price'))
+                        fig.add_trace(go.Scatter(x=hist.index, y=hist['MA5'], name='5-day MA'))
+                        fig.add_trace(go.Scatter(x=hist.index, y=hist['MA20'], name='20-day MA'))
+                        fig.add_trace(go.Scatter(x=hist.index, y=hist['MA50'], name='50-day MA'))
+                        fig.update_layout(title=f"{ticker} Technical Indicators", xaxis_title="Date", yaxis_title="Price")
+                        st.plotly_chart(fig)
+                
             except Exception as e:
                 st.error(f"An error occurred: {e}")
-                # Log the full LLM output for debugging
                 if hasattr(e, "output"):
                     st.write("Raw Output:", e.output)
     else: