backend/chart.py

import pandas as pd

from pathlib import Path

audio_attacks_with_variations = [
    "random_noise",
    "lowpass_filter",
    "highpass_filter",
    "boost_audio",
    "duck_audio",
    "shush",
]

attacks_plot_metrics = ["bit_acc", "log10_p_value", "TPR", "FPR", "watermark_det_score"]

image_attacks_with_variations = [
    "center_crop",
    "jpeg",
    "brightness",
    "contrast",
    "saturation",
    "sharpness",
    "resize",
    "perspective",
    "median_filter",
    "hue",
    "gaussian_blur",
]


video_attacks_with_variations = [
    "Rotate",
    "Resize",
    "Crop",
    "Brightness",
    "Contrast",
    "Saturation",
    "H264",
    "H264rgb",
    "H265",
]


def plot_data(metric, selected_attack, all_attacks_df):
    attack_df = all_attacks_df[all_attacks_df.attack == selected_attack]

    # if metric == "None":
    #     return gr.LinePlot(x_bin=None)

    # return gr.LinePlot(
    #     attack_df,
    #     x="strength",
    #     y=metric,
    #     color="model",
    # )


def mk_variations(
    all_attacks_df,
    metrics: list[str] = attacks_plot_metrics,
    attacks_with_variations: list[str] = audio_attacks_with_variations,
):
    # all_attacks_df = pd.read_csv(csv_file)
    # print(all_attacks_df)
    # print(csv_file)

    # with gr.Row():
    #     group_by = gr.Radio(metrics, value=metrics[0], label="Choose metric")
    #     attacks_dropdown = gr.Dropdown(
    #         attacks_with_variations,
    #         label=attacks_with_variations[0],
    #         info="Select attack",
    #     )

    # attacks_by_strength = plot_data(
    #     group_by.value, attacks_dropdown.value, all_attacks_df
    # )

    # all_graphs = [
    #     attacks_by_strength,
    # ]

    # group_by.change(
    #     lambda group: plot_data(group, attacks_dropdown.value, all_attacks_df),
    #     group_by,
    #     all_graphs,
    # )

    # attacks_dropdown.change(
    #     lambda attack: plot_data(group_by.value, attack, all_attacks_df),
    #     attacks_dropdown,
    #     all_graphs,
    # )

    return {
        "metrics": metrics,
        "attacks_with_variations": attacks_with_variations,
        "all_attacks_df": all_attacks_df.to_dict(orient="records"),
    }