Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import pandas as pd
 from core.data import load_data
 from core.model_runner import get_model
-from core.plot import plot_forecast, plot_metrics_r2, plot_metrics_errors, plot_loss_curve, plot_future_forecast
+from core.plot import plot_forecast, plot_metrics_precision, plot_metrics_risk, plot_loss_curve, plot_future_forecast
 from config import AVAILABLE_MODELS, DEFAULT_TICKERS
 
 
@@ -39,8 +39,8 @@ def main_interface():
                 backtest_plot = gr.Plot(label="📊 Backtesting: Actual vs Forecast")
                 future_plot = gr.Plot(label="🔮 Future Forecast with Actuals")
                 future_table = gr.Dataframe(label="📋 Future Predictions")
-                r2_plot = gr.Plot(label="📉 R² and MAPE Metrics")
-                error_plot = gr.Plot(label="📉 RMSE and MAE Metrics")
+                precision_plot = gr.Plot(label="📉 Precision Metrics (Model Accuracy: R², Explained Variance, MDA)")
+                risk_plot = gr.Plot(label="📉 Risk Metrics (Error Magnitude: RMSE, MAE, MAPE, MASE)")
                 loss_plot = gr.Plot(label="📈 Training Loss Curve")
 
         def run_pipeline(data_src, ticker, file_upload, start_date, end_date, horizon, model,
@@ -58,7 +58,7 @@ def main_interface():
 
                 result = get_model(
                     df=main_df,
-                    future_df=future_df,  # Pass future_df
+                    future_df=future_df,
                     model_name=model,
                     horizon=horizon,
                     hidden_units=hidden_units,
@@ -73,9 +73,9 @@ def main_interface():
                     test_split=test_split
                 )
                 forecast_plot = plot_forecast(result)
-                future_plot = plot_future_forecast(main_df, result, future_df)  # Pass future_df
-                r2_plot = plot_metrics_r2(result)
-                error_plot = plot_metrics_errors(result)
+                future_plot = plot_future_forecast(main_df, result, future_df)
+                precision_plot = plot_metrics_precision(result)
+                risk_plot = plot_metrics_risk(result)
                 loss_plot = plot_loss_curve(result)
 
                 msg = "✅ Done."
@@ -90,7 +90,7 @@ def main_interface():
                 else:
                     future_df_out = pd.DataFrame()
 
-                return forecast_plot, future_plot, future_df_out, r2_plot, error_plot, loss_plot, msg
+                return forecast_plot, future_plot, future_df_out, precision_plot, risk_plot, loss_plot, msg
             except Exception as e:
                 return None, None, None, None, None, None, f"❌ Error: {str(e)}"
 
@@ -102,7 +102,7 @@ def main_interface():
                 hidden_units, n_layers, epochs, learning_rate,
                 beta1, beta2, weight_decay, dropout, window_size, test_split
             ],
-            outputs=[backtest_plot, future_plot, future_table, r2_plot, error_plot, loss_plot, status]
+            outputs=[backtest_plot, future_plot, future_table, precision_plot, risk_plot, loss_plot, status]
         )
 
         def toggle_file(src):