Update app.py

app.py

@@ -8,22 +8,61 @@ import json
 import yaml
 import matplotlib.pyplot as plt
 import seaborn as sns
+import plotnine as p9
 
 from src.about import *
 from src.bin.PROBE import run_probe
 
 global data_component, filter_component
 
+def get_method_color(method):
+    return color_dict.get(method, 'black')  # If method is not in color_dict, use black
 
+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)]
+    
+    # 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',
+        'force_text': (.0, 1.), 'force_objects': (.0, 1.),
+        'lim': 500000, 'precision': 1., 'avoid_points': True, 'avoid_text': True
+    }
+
+    # 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))
+    )
+
+    # Save the plot as an image (you can modify save_path accordingly)
+    filename = title.replace(" ", "_") + "_Similarity_Scatter.png"  # Save the plot
+    g.save(filename=filename, dpi=600)
+
+    return g
 
 def benchmark_plot(benchmark_type, methods_selected, x_metric, y_metric):
-    if benchmark_type == 'Flexible':
+    if benchmark_type == 'flexible':
         # Use general visualizer logic
         return general_visualizer_plot(methods_selected, x_metric=x_metric, y_metric=y_metric)
-    elif benchmark_type == 'Benchmark 1':
-        return benchmark_1_plot(x_metric, y_metric)
-    elif benchmark_type == 'Benchmark 2':
-        return benchmark_2_plot(x_metric, y_metric)
+    elif benchmark_type == 'similarity':
+        title = f"Similarity Benchmark: {x_metric} vs {y_metric}"
+        return draw_scatter_plot(methods_selected, x_metric, y_metric, title)
     elif benchmark_type == 'Benchmark 3':
         return benchmark_3_plot(x_metric, y_metric)
     elif benchmark_type == 'Benchmark 4':