Update app.py

app.py

@@ -7,6 +7,7 @@ import os
 import json
 import yaml
 import matplotlib.pyplot as plt
+import seaborn as sns
 
 from src.about import *
 from src.bin.PROBE import run_probe
@@ -19,21 +20,24 @@ def get_baseline_df(selected_methods, selected_metrics):
     df = df[df['method_name'].isin(selected_methods)][present_columns]
     return df
 
-# Function to create the plot
 def create_plot(methods_selected, x_metric, y_metric):
     df = pd.read_csv(CSV_RESULT_PATH)
-    filtered_df = df[df['method_name'].isin(methods_selected)]
-    
-    # Create a larger plot
-    plt.figure(figsize=(10, 8))  # Increase the figure size
-    for method in methods_selected:
-        method_data = filtered_df[filtered_df['method_name'] == method]
-        plt.plot(method_data[x_metric], method_data[y_metric], label=method, marker='o')
+    filtered_df = df[df['Method'].isin(methods_selected)]
+
+    # Create a Seaborn lineplot with method as hue
+    plt.figure(figsize=(10, 8))  # Increase figure size
+    sns.lineplot(
+        data=filtered_df, 
+        x=x_metric, 
+        y=y_metric, 
+        hue="Method",  # Different colors for different methods
+        marker="o",  # Add markers to the line plot
+    )
     
+    # Add labels and title
     plt.xlabel(x_metric)
     plt.ylabel(y_metric)
     plt.title(f'{y_metric} vs {x_metric} for selected methods')
-    plt.legend()
     plt.grid(True)
     
     # Save the plot to display it in Gradio