Update pages/4_Model Creation and Evaluation.py

pages/4_Model Creation and Evaluation.py

@@ -12,14 +12,14 @@ 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/zK1vosRLYAQ4EFqG8JyEs.png"
+background_image_url = "https://cdn-uploads.huggingface.co/production/uploads/675fab3a2d0851e23d23cad3/uuRffDOdqb_CQPlKm3_J5.jpeg"
 # Apply custom CSS for the background image and overlay
 st.markdown(
     f"""
     <style>
         .stApp {{
             background-image: url("{background_image_url}");
-            background-size: 100% auto;  /* Ensure the image width is 100% of the screen, and the height scales proportionally */
+            background-size: auto;  /* Ensure the image width is 100% of the screen, and the height scales proportionally */
             background-repeat: repeat-y;  /* Repeat only vertically */
             background-position: top center;  /* Start repeating from the top center */
             background-attachment: fixed;  /* Keeps the background fixed as you scroll */
@@ -45,7 +45,7 @@ st.markdown(
         }}
         /* Styling the markdown content */
         .stMarkdown {{
-            color: white;  /* White text to ensure visibility */
+            color: black;  /* White text to ensure visibility */
             font-size: 100px;  /* Adjust font size for readability */
         }}
     </style>
@@ -140,13 +140,6 @@ study.optimize(objective, n_trials=200)
 """
 st.code(code_3, language='python')
 
-# Output for Optuna Optimization
-study = optuna.create_study(direction="maximize", sampler=optuna.samplers.TPESampler())
-study.optimize(objective, n_trials=200)
-
-st.write("Best Parameters found by Optuna:", study.best_params)
-st.write("All Trials Dataframe:")
-st.write(study.trials_dataframe())
 
 # Code and Output 4: Model Training with Best Parameters
 st.subheader("Step 4: Model Training with Best Parameters")
@@ -154,18 +147,21 @@ st.subheader("Step 4: Model Training with Best Parameters")
 # Code for Model Training
 code_4 = """
 # Model Training with Best Parameters
-solver = study.best_params.get('solver', 'lbfgs')
-penalty = study.best_params.get('penalty', 'l2')
-C = study.best_params.get('lambda', 1.0)
-model = LogisticRegression(C=C, solver=solver, penalty=penalty, max_iter=500)
+# Use the hyperparameters obtained from Optuna
+solver = 'newton-cg'  
+penalty = 'l2'        
+C = 999.8628541436512 
+
+# Initialize Logistic Regression model with the best hyperparameters
+model = LogisticRegression(C=C, solver=solver, penalty=penalty, multi_class="multinomial", max_iter=500)
+
+# Train the model
 model.fit(x_train_std, y_train)
+
 """
 st.code(code_4, language='python')
 
 # Output for Model Training
-solver = study.best_params.get('solver', 'lbfgs')
-penalty = study.best_params.get('penalty', 'l2')
-C = study.best_params.get('lambda', 1.0)
 model = LogisticRegression(C=C, solver=solver, penalty=penalty, max_iter=500)
 model.fit(x_train_std, y_train)