Update pages/4_Model Creation and Evaluation.py

pages/4_Model Creation and Evaluation.py

@@ -12,7 +12,7 @@ import optuna
 st.markdown("<h1 style='text-align:center; color:purple;'>Model Creation and Evaluation</h1>", unsafe_allow_html=True)
 
 # # Background Styling
-background_image_url = "https://cdn-uploads.huggingface.co/production/uploads/675fab3a2d0851e23d23cad3/uuRffDOdqb_CQPlKm3_J5.jpeg"
+background_image_url = "https://cdn-uploads.huggingface.co/production/uploads/675fab3a2d0851e23d23cad3/MaLU-6gHwU70lZ-t_P94p.jpeg"
 # # Apply custom CSS for the background image and overlay
 st.markdown(
     f"""
@@ -145,13 +145,19 @@ st.code(code_3, language='python')
 st.subheader("Step 4: Model Training with Best Parameters")
 
 # Code for Model Training
-code_4 = """
-# Model Training with Best Parameters
-# Use the hyperparameters obtained from Optuna
+# Define hyperparameters first
 solver = 'newton-cg'  
 penalty = 'l2'        
 C = 999.8628541436512 
 
+# Code for Model Training
+code_4 = f"""
+# Model Training with Best Parameters
+# Use the hyperparameters obtained from Optuna
+solver = '{solver}'  
+penalty = '{penalty}'        
+C = {C} 
+
 # Initialize Logistic Regression model with the best hyperparameters
 model = LogisticRegression(C=C, solver=solver, penalty=penalty, multi_class="multinomial", max_iter=500)
 
@@ -165,6 +171,9 @@ st.code(code_4, language='python')
 model = LogisticRegression(C=C, solver=solver, penalty=penalty, max_iter=500)
 model.fit(x_train_std, y_train)
 
+st.write("Model has been trained successfully!")
+
+
 st.write("Model has been trained successfully!")
 
 # Code and Output 5: Model Evaluation