Update app.py

app.py

@@ -417,27 +417,83 @@ def predict_stock_prices():
                     predictions = predictor.predict(days=days_to_predict)
 
                     if predictions is not None:
-                        plot = create_prediction_plot(data, predictions, company_name)
-                        st.plotly_chart(plot, use_container_width=True)
-
-                        candlestick_plot = create_candlestick_plot(data, company_name)
-                        st.plotly_chart(candlestick_plot, use_container_width=True)
+                        # Create prediction plot
+                        fig = go.Figure()
+
+                        fig.add_trace(go.Scatter(
+                            x=data.index,
+                            y=data['Close'],
+                            mode='lines',
+                            name='Historical Data',
+                            line=dict(color='cyan')
+                        ))
+
+                        future_dates = pd.date_range(start=data.index[-1] + pd.Timedelta(days=1), periods=days_to_predict)
+                        fig.add_trace(go.Scatter(
+                            x=future_dates,
+                            y=predictions['yhat'].tail(days_to_predict),
+                            mode='lines',
+                            name='Predicted Data',
+                            line=dict(color='yellow')
+                        ))
+
+                        fig.add_trace(go.Scatter(
+                            x=future_dates,
+                            y=predictions['yhat_upper'].tail(days_to_predict),
+                            mode='lines',
+                            line=dict(width=0),
+                            showlegend=False
+                        ))
+
+                        fig.add_trace(go.Scatter(
+                            x=future_dates,
+                            y=predictions['yhat_lower'].tail(days_to_predict),
+                            mode='lines',
+                            line=dict(width=0),
+                            fillcolor='rgba(255, 255, 0, 0.3)',
+                            fill='tonexty',
+                            name='Prediction Interval'
+                        ))
+
+                        fig.update_layout(
+                            title=f'{company_name} Stock Price Prediction',
+                            xaxis_title='Date',
+                            yaxis_title='Close Price',
+                            template='plotly_dark',
+                            hovermode='x unified',
+                            xaxis_rangeslider_visible=True,
+                            legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
+                        )
+
+                        st.plotly_chart(fig, use_container_width=True)
+
+                        # Create forecast components plot using Plotly
+                        components = predictor.model.predict_components(predictions)
+                        n_components = len(components.columns)
+                        
+                        fig_components = make_subplots(rows=n_components, cols=1, 
+                                                       subplot_titles=components.columns)
+                        
+                        for i, component in enumerate(components.columns, start=1):
+                            fig_components.add_trace(
+                                go.Scatter(x=predictions['ds'], y=components[component], 
+                                           mode='lines', name=component),
+                                row=i, col=1
+                            )
+                        
+                        fig_components.update_layout(height=300*n_components, 
+                                                     title_text="Forecast Components",
+                                                     showlegend=False)
+                        
+                        st.plotly_chart(fig_components, use_container_width=True)
 
                         st.subheader("Predicted Prices")
                         pred_df = predictions[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(days_to_predict)
                         pred_df.columns = ['Date', 'Predicted Price', 'Lower Bound', 'Upper Bound']
                         st.dataframe(pred_df)
 
-                        # Component-wise forecast
-                        st.subheader("Forecast Components")
-                        fig = predictor.model.plot_components(predictions)
-                        st.pyplot(fig)
-
-                        # Changepoints
-                        st.subheader("Detected Changepoints")
-                        fig = predictor.model.plot(predictions)
-                        a = add_changepoints_to_plot(fig.gca(), predictor.model, predictions)
-                        st.pyplot(fig)
+                        # Provide download link for predictions
+                        st.markdown(get_table_download_link(pred_df, f"{ticker}_predictions.csv", "Download predictions CSV"), unsafe_allow_html=True)
 
                         news = fetch_news(company_name)
                         st.subheader("Latest News")
@@ -447,6 +503,8 @@ def predict_stock_prices():
                         st.error("Failed to generate predictions. The predicted data is None.")
                 else:
                     st.error("Failed to train the Prophet model. Please try a different dataset.")
+            else:
+                st.error("Failed to fetch stock data. Please try again.")
 
 def explore_data():
     st.header("Explore Stock Data")