lightgbm plotly scikit-learn seaborn import gradio as gr import pandas as pd import seaborn as sns import matplotlib.pyplot as plt df = pd.read_csv("hf://datasets/merve/supersoaker-failures/supersoaker.csv") df.dropna(axis=0, inplace=True) def plot(df): plt.scatter(df.measurement_13, df.measurement_15, c = df.loading,alpha=0.5) plt.savefig("scatter.png") df['failure'].value_counts().plot(kind='bar') plt.savefig("bar.png") sns.heatmap(df.select_dtypes(include="number").corr()) plt.savefig("corr.png") plots = ["corr.png","scatter.png", "bar.png"] return plots inputs = [gr.Dataframe(label="Supersoaker Production Data")] outputs = [gr.Gallery(label="Profiling Dashboard")] gr.Interface(plot, inputs=inputs, outputs=outputs, examples=[df.head(100)], title="Supersoaker Failures Analysis Dashboard").launch()