Update app.py

app.py

@@ -22,10 +22,13 @@ def draw_scatter_plot_similarity(methods_selected, x_metric, y_metric, title):
     df = pd.read_csv(CSV_RESULT_PATH)
     # Filter the dataframe based on selected methods
     filtered_df = df[df['method_name'].isin(methods_selected)]
-    
+
+    def get_method_color(method):
+        return color_dict.get(method, 'black')
+
     # Add a new column to the dataframe for the color
     filtered_df['color'] = filtered_df['method_name'].apply(get_method_color)
-    
+
     adjust_text_dict = {
         'expand_text': (1.15, 1.4), 'expand_points': (1.15, 1.25), 'expand_objects': (1.05, 1.5),
         'expand_align': (1.05, 1.2), 'autoalign': 'xy', 'va': 'center', 'ha': 'center',
@@ -35,26 +38,29 @@ def draw_scatter_plot_similarity(methods_selected, x_metric, y_metric, title):
 
     # Create the scatter plot using plotnine (ggplot)
     g = (p9.ggplot(data=filtered_df,
-               mapping=p9.aes(x=x_metric,  # Use the selected x_metric
-                              y=y_metric,  # Use the selected y_metric
-                              color='color',  # Use the dynamically generated color
-                              label='method_name'))  # Label each point by the method name
-     + p9.geom_point(position="jitter")  # Add points with slight jitter to avoid overlap
-     + p9.geom_text(adjust_text=adjust_text_dict)  # Add method names as labels with text adjustments
-     + p9.labs(title=title, x=f"{x_metric} Metric", y=f"{y_metric} Metric")  # Dynamic labels for X and Y axes
-     + p9.scale_color_identity()  # This tells plotnine to use the exact color values from the dataframe
-     + p9.theme(legend_position='none', 
-                figure_size=(10, 10),  # Set figure size
-                axis_text=p9.element_text(size=10),   
-                axis_title_x=p9.element_text(size=12),
-                axis_title_y=p9.element_text(size=12))
+                   mapping=p9.aes(x=x_metric,  # Use the selected x_metric
+                                  y=y_metric,  # Use the selected y_metric
+                                  color='color',  # Use the dynamically generated color
+                                  label='method_name'))  # Label each point by the method name
+         + p9.geom_point(position="jitter")  # Add points with slight jitter to avoid overlap
+         + p9.geom_text(adjust_text=adjust_text_dict)  # Add method names as labels with text adjustments
+         + p9.labs(title=title, x=f"{x_metric} Metric", y=f"{y_metric} Metric")  # Dynamic labels for X and Y axes
+         + p9.scale_color_identity()  # Use colors directly from the dataframe
+         + p9.theme(legend_position='none', 
+                    figure_size=(10, 10),  # Set figure size
+                    axis_text=p9.element_text(size=10),   
+                    axis_title_x=p9.element_text(size=12),
+                    axis_title_y=p9.element_text(size=12))
     )
 
-    # Save the plot as an image (you can modify save_path accordingly)
-    filename = title.replace(" ", "_") + "_Similarity_Scatter.png"  # Save the plot
+    # Save the plot as an image
+    save_path = "./plot_images"  # Ensure this folder exists or adjust the path
+    os.makedirs(save_path, exist_ok=True)  # Create directory if it doesn't exist
+    filename = os.path.join(save_path, title.replace(" ", "_") + "_Similarity_Scatter.png")
+    
     g.save(filename=filename, dpi=600)
-
-    return g
+    
+    return filename
 
 def benchmark_plot(benchmark_type, methods_selected, x_metric, y_metric):
     if benchmark_type == 'flexible':