Accuracy calculation

app.py

@@ -47,8 +47,8 @@ def get_forecast(company_name):
     forecast_df = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]
     
     # Calculate accuracy metrics
-    actual_prices = stock_df['y'].values[-1000:]  # Last 7 actual prices
-    predicted_prices = forecast_df['yhat'].values[-1000:]  # Forecasted prices for the last 7 days
+    actual_prices = stock_df['y'].values[-1000:]  # Last 1000 actual prices
+    predicted_prices = forecast_df['yhat'].values[-1000:]  # Forecasted prices for the last 1000 days
     #mae = mean_absolute_error(actual_prices, predicted_prices)
     #mse = mean_squared_error(actual_prices, predicted_prices)
     #rmse = np.sqrt(mse)
@@ -62,7 +62,7 @@ def get_forecast(company_name):
     
     # Display accuracy metrics
     # accuracy_text = f"MAE: {mae:.2f}, MSE: {mse:.2f}, RMSE: {rmse:.2f}, MAPE: {mape:.2f}%"
-    accuracy_text = f"{accuracy:.2f} %"  
+    accuracy_text = f"{accuracy:.2f} % based on past 03 Years Prediction vs Actual Stock Price."  
        
     return fig , accuracy_text