Accuracy Implemented

app.py

@@ -5,6 +5,7 @@ import yfinance as yf
 from prophet import Prophet
 from prophet.plot import plot_plotly, plot_components_plotly
 import plotly.graph_objects as go
+from sklearn.metrics import mean_absolute_error, mean_squared_error
 
 
 import warnings
@@ -25,7 +26,7 @@ period_options = {
 period = gr.Radio(label="Training Period: ", choices=list(period_options.values()), value="1 Week")
 
 
-# Define the function to return the symbol corresponding to the selected company name
+
 def get_forecast(company_name):
     symbol_nse = stocks[stocks['Company Name'] == company_name]['Symbol'].values[0] + '.NS'
     #period_key = [key for key, value in period_options.items() if value == period][0]
@@ -45,19 +46,34 @@ def get_forecast(company_name):
     forecast = model.predict(future)
     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
+    #mae = mean_absolute_error(actual_prices, predicted_prices)
+    #mse = mean_squared_error(actual_prices, predicted_prices)
+    #rmse = np.sqrt(mse)
+    mape = np.mean(np.abs((actual_prices - predicted_prices) / actual_prices)) * 100
+    accuracy = 100 - mape
+    
+    
     fig = plot_plotly(model, forecast_df, xlabel = "Date", ylabel = "Price", figsize=(1400,800))
-    #fig.update_layout(xaxis_title="Date", yaxis_title="Price")
+    #fig.update_layout(autosize=True)
+    #fig.update_xaxes(automargin =True)
     
-    return fig
+    # Display accuracy metrics
+    # accuracy_text = f"MAE: {mae:.2f}, MSE: {mse:.2f}, RMSE: {rmse:.2f}, MAPE: {mape:.2f}%"
+    accuracy_text = f"{accuracy:.2f} %"  
+       
+    return fig , accuracy_text
 
 
 with gr.Blocks() as demo:
     
     gr.Markdown(
         """
-        #  NSE Stock Price Trend Prediction
-        Select the Stock from Dropdown Menu to get One Week Prediction.
-           
+        # Stock Price Trend Prediction - using PROPHET Model
+        Select the Stock from Dropdown Menu to get Next Week Prediction
+        
         """
         
     )
@@ -66,35 +82,31 @@ with gr.Blocks() as demo:
     
     with gr.Row():
         with gr.Column():
-            None
+            accuracy_textbox = gr.Textbox(label="Model Accuracy", visible=True, info = "Accuracy above 95% is considerate", interactive = False)
         with gr.Column():
             None
         with gr.Column():
             None
         with gr.Column():    
             submit_btn = gr.Button(value = "Predict")
-
+    
+    
+       
     gr.Markdown(
         """
-        ### Select the Plot-Area to check Prediction
+        ### Select the Plot-Area to check Prediction for Next Week
         
         """
+        
     )
-
     
+          
     with gr.Row():
-        forecast_plot = gr.Plot(label = 'Forecast Plot')
-    
+        forecast_plot = gr.Plot()
     
-    gr.Markdown(
-        """
-        Disclaimer : This model is for Informative Purpose Only, and doesn't support any particular Stock.       
-        """
         
-    )
-
-
-    submit_btn.click(get_forecast, inputs=dropdown, outputs=forecast_plot)            
+    
+    submit_btn.click(get_forecast, inputs=dropdown, outputs=[forecast_plot, accuracy_textbox])            
     
       
 demo.launch(share=True)