added model_details function

models/business_performance_forecasting/notebooks/Business_forecasting.py

@@ -2,7 +2,8 @@
 import pandas as pd 
 import pickle
 import os
-
+import matplotlib.pyplot as plt
+from sklearn.metrics import r2_score
 # Load the data
 df = pd.read_csv('50_Startups.csv')
 X = df.iloc[:, :-1].values
@@ -41,3 +42,38 @@
     pickle.dump(ct, scaler_file)
 
 print("Model and preprocessing objects saved successfully!")
+
+def save_evaluation_to_pickle(train_X, train_Y, test_X, test_Y, output_file="evaluation_results.pkl"):
+    # Calculate R^2 score
+    train_r2 = r2_score(train_Y, model.predict(train_X))
+    test_r2 = r2_score(test_Y, y_pred)
+
+    # Create plot
+    fig, ax = plt.subplots(figsize=(10, 6))
+    ax.scatter(test_Y, y_pred, alpha=0.6, color='blue', label='Predicted')
+    ax.plot([test_Y.min(), test_Y.max()], [test_Y.min(), test_Y.max()], 'r--', label='Perfect Prediction')
+    ax.set_xlabel("Actual")
+    ax.set_ylabel("Predicted")
+    ax.set_title("Actual vs Predicted Values (Test Set)")
+    ax.legend()
+    ax.grid(True)
+
+    # Save the plot as a PNG file
+    plot_file = "actual_vs_predicted.png"
+    fig.savefig(plot_file)
+
+    # Package results
+    results = {
+        "Train_R2": train_r2,
+        "Test_R2": test_r2,
+        "plot_file": plot_file  # Save the plot file path
+    }
+
+    # Save results to a pickle file
+    with open(output_file, "wb") as f:
+        pickle.dump(results, f)
+
+    print(f"Evaluation and plot data saved to {output_file}")
+    print(f"Plot saved as {plot_file}")
+# Run this function once to generate the evaluation file
+save_evaluation_to_pickle(X_train, y_train, X_test, y_test)

models/business_performance_forecasting/predict.py

@@ -1,10 +1,14 @@
-# import os
+import os
 import numpy as np
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import pickle
 from models.business_performance_forecasting.model import load_model_and_scaler  # Import the function from model.py
 
 # Define the prediction function
 def get_prediction(RnD_Spend, Administration, Marketing_Spend, State):
-    # Load the model and scaler
+    # Load the model and scalers
     model, scaler = load_model_and_scaler()
     # Prepare input features as a NumPy array
     input_data = np.array([[RnD_Spend, Administration, Marketing_Spend, State]])
@@ -18,3 +22,19 @@ def get_prediction(RnD_Spend, Administration, Marketing_Spend, State):
 
     return prediction[0]  # Return the predicted profit
 
+
+class ModelEvaluation:
+    def __init__(self):
+        metrics_file= os.path.join(os.path.dirname(__file__), 'saved_models', 'evaluation_results.pkl')
+        # Load evaluation metrics from a pickle file
+        with open(metrics_file, "rb") as f:
+            self.metrics = pickle.load(f)
+        print("Loaded metrics:", self.metrics)
+    def evaluate(self):
+        metrics = self.metrics       
+        return metrics, None, None, None
+
+def model_details():
+    evaluator = ModelEvaluation()
+    return evaluator
+

page_handler.py

@@ -85,11 +85,12 @@ def render_model_details(self, model_module,tab):
 
 			# Display the scatter plot for predicted vs actual values
 			#used clear_figure to clear the plot once displayed to avoid conflict 
-			st.subheader("Model Prediction Plot")
-			st.pyplot(prediction_plot, clear_figure=True)
-
-			st.subheader("Error Plot")
-			st.pyplot(error_plot, clear_figure=True)
-
-			st.subheader("Model Performance Plot")
-			st.pyplot(performance_plot, clear_figure=True)
+			if prediction_plot!=None:
+				st.subheader("Model Prediction Plot")
+				st.pyplot(prediction_plot, clear_figure=True)
+			if error_plot!=None:
+				st.subheader("Error Plot")
+				st.pyplot(error_plot, clear_figure=True)
+			if performance_plot!=None:
+				st.subheader("Model Performance Plot")
+				st.pyplot(performance_plot, clear_figure=True)