Update app.py

app.py

@@ -15,103 +15,6 @@ from src.saving_utils import *
 from src.vis_utils 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)]
-
-    def get_method_color(method):
-        return color_dict.get(method.upper(), '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',
-        '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_names'))  # Label each point by the method name
-         + p9.geom_point(size=3)  # Add points with no jitter, set point size
-         + p9.geom_text(nudge_y=0.02, size=8)  # Add method names as labels, nudge slightly above the points
-         + p9.labs(title=title, x=f"{x_metric}", y=f"{y_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=(8, 8),  # 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
-    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=400)
-    
-    return filename
-
-def benchmark_plot(benchmark_type, methods_selected, x_metric, y_metric):
-    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 == 'similarity':
-        title = f"{x_metric} vs {y_metric}"
-        return draw_scatter_plot_similarity(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':
-        return benchmark_4_plot(x_metric, y_metric)
-    else:
-        return "Invalid benchmark type selected."
-
-
-def get_baseline_df(selected_methods, selected_metrics):
-    df = pd.read_csv(CSV_RESULT_PATH)
-    present_columns = ["method_name"] + selected_metrics
-    df = df[df['method_name'].isin(selected_methods)][present_columns]
-    return df
-
-def general_visualizer(methods_selected, x_metric, y_metric):
-    df = pd.read_csv(CSV_RESULT_PATH)
-    filtered_df = df[df['method_name'].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_name",  # 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.grid(True)
-    
-    # Save the plot to display it in Gradio
-    plot_path = "plot.png"
-    plt.savefig(plot_path)
-    plt.close()
-    
-    return plot_path
-
 def add_new_eval(
     human_file,
     skempi_file,