Improved Code Scalability

app.py

@@ -601,26 +601,21 @@ classifier.fit(
 )
 """)
                 history = model.text_based_recommender.history
+                n_estimator = len(history['validation_0']['merror'])
                 
-                
-                h2('Result Training Loss and Error')
+                h2('Training vs Validation log loss')
                 results = {
                     "merror": history['validation_0']['merror'],
                     "mlogloss": history['validation_0']['mlogloss']
                 }
                 plot_output = gr.Plot(format='png')
                 btn = gr.Button("Generate Plot")
-                btn.click(fn=lambda:plot_training_results(results), inputs=[], outputs=plot_output, preprocess=False)
+                btn.click(fn=lambda:plot_training_results(n_estimator,history['validation_0']['mlogloss'],history['validation_1']['mlogloss'],'Training Log Loss','Validation Log Loss','Log Loss','N Estimator'), inputs=[], outputs=plot_output, preprocess=False)
                 
-                h2('Result Validation Loss and Error')
-
-                resultsval = {
-                    "merror": history['validation_1']['merror'],
-                    "mlogloss": history['validation_1']['mlogloss']
-                }
+                h2('Training vs Validation error')
                 plot_outputval = gr.Plot(format='png')
                 btnval = gr.Button("Generate Plot")
-                btnval.click(fn=lambda:plot_training_results(resultsval), inputs=[], outputs=plot_outputval, preprocess=False)
+                btnval.click(fn=lambda:plot_training_results(n_estimator,history['validation_0']['merror'],history['validation_1']['merror'],'Training error','Validation error','merror','N Estimator'), inputs=[], outputs=plot_outputval, preprocess=False)
                 y_pred = model.text_based_recommender.classifier.predict(vectorizer.transform(test_df['cleaned_review']))
                 y_test = model.text_based_recommender.app_id_encoder.transform(test_df['app_id'])
                 class_report = classification_report(y_test,y_pred)

component.py

@@ -144,51 +144,18 @@ def code_cell(code):
     gr.Code(inspect.cleandoc(code), language='python')
 
 ## This for EDA, Preprocess, and training
-def plot_training_results(results: dict):
-    """
-    Plots the training metrics: merror and mlogloss from the result dictionary.
-
-    This function generates a line plot that visualizes the model's training 
-    performance over time (e.g., across epochs or folds), using the merror 
-    (training error) and mlogloss (log loss) values.
-
-    Args:
-        results (dict): A dictionary containing two keys:
-            - 'merror': list of training error values.
-            - 'mlogloss': list of log loss values.
-            Example:
-                {
-                    "merror": [0.12, 0.10, 0.08],
-                    "mlogloss": [0.35, 0.32, 0.30]
-                }
-
-    Returns:
-        matplotlib.figure.Figure: A Matplotlib figure showing the trends of 
-        training error and log loss as line plots.
-
-    Example:
-        results = {
-            "merror": [0.12, 0.10, 0.08],
-            "mlogloss": [0.35, 0.32, 0.30]
-        }
-        plot_output = gr.Plot()
-        btn = gr.Button("Generate Plot")
-        btn.click(fn=lambda:plot_training_results(results), inputs=[], outputs=plot_output, preprocess=False)
-    """
-    epochs = list(range(1, len(results["merror"]) + 1))
+def plot_training_results(x,y1,y2,y1label,y2label,ylabel,xlabel,title=""):
+    fig,ax=plt.subplots(figsize=(8,5))
+    ax.plot(x, y1, marker='o', label=y1label, color='blue')
+    ax.plot(x, y2, marker='s', label=y2label, color='orange')
 
-    plt.figure(figsize=(8, 5))
-    plt.plot(epochs, results["merror"], marker='o', label='Training Error (merror)', color='blue')
-    plt.plot(epochs, results["mlogloss"], marker='s', label='Log Loss (mlogloss)', color='orange')
+    ax.set_title(title)
+    ax.set_xlabel(xlabel=xlabel)
+    ax.set_ylabel(ylabel=ylabel)
+    ax.legend()
+    ax.grid(True)
 
-    plt.title('Training Metrics Over Time')
-    plt.xlabel('Epoch / Fold')
-    plt.ylabel('Value')
-    plt.legend()
-    plt.grid(True)
-    plt.tight_layout()
-
-    return plt.gcf()
+    return fig
 
 # for Recommendation section
 def input_name_textbox(Label:str, Placeholder:str):