| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| from datasets import load_dataset | |
| import histos | |
| dataset = load_dataset("cmpatino/optimal_observables", "train") | |
| dataset_df = dataset["train"].to_pandas() | |
| dataset_df["target"] = dataset_df["target"].map({0: "spin-OFF", 1: "spin-ON"}) | |
| def get_plot(features, n_bins): | |
| plotting_df = dataset_df.copy() | |
| if len(features) == 1: | |
| fig, ax = plt.subplots() | |
| values = [ | |
| plotting_df[plotting_df["target"] == "spin-ON"][features[0]], | |
| plotting_df[plotting_df["target"] == "spin-OFF"][features[0]], | |
| ] | |
| labels = ["spin-ON", "spin-OFF"] | |
| fig = histos.ratio_hist( | |
| processes_q=values, | |
| hist_labels=labels, | |
| reference_label=labels[1], | |
| n_bins=n_bins, | |
| hist_range=None, | |
| title=features[0], | |
| ) | |
| return fig | |
| if len(features) == 2: | |
| return sns.displot( | |
| plotting_df, | |
| x=features[0], | |
| y=features[1], | |
| hue="target", | |
| bins=n_bins, | |
| height=8, | |
| aspect=1, | |
| ).fig | |
| with gr.Blocks() as demo: | |
| with gr.Column(): | |
| with gr.Row(): | |
| features = gr.Dropdown( | |
| choices=dataset_df.columns.to_list(), | |
| label="Feature", | |
| value="m_tt", | |
| multiselect=True, | |
| ) | |
| n_bins = gr.Slider( | |
| label="Number of Bins for Histogram", | |
| value=10, | |
| minimum=10, | |
| maximum=100, | |
| step=10, | |
| ) | |
| feature_plot = gr.Plot(label="Feature's Plot") | |
| features.change( | |
| get_plot, | |
| [features, n_bins], | |
| feature_plot, | |
| queue=False, | |
| ) | |
| n_bins.change( | |
| get_plot, | |
| [features, n_bins], | |
| feature_plot, | |
| queue=False, | |
| ) | |
| demo.load( | |
| get_plot, | |
| [features, n_bins], | |
| feature_plot, | |
| queue=False, | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |