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app.py

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+import gradio as gr
+import pandas as pd
+import datasets
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+df = datasets.load_dataset("merve/supersoaker-failures")
+df = df["train"].to_pandas()
+df.dropna(axis=0, inplace=True)
+df.drop(columns=["id"], inplace=True)
+
+def plot(df):
+  plots = []
+  plt.scatter(df.measurement_13, df.measurement_15, c = df.loading,alpha=0.5)
+  plt.savefig("scatter.png")
+  plt.scatter(df.measurement_10, df.measurement_15, c = df.loading,alpha=0.5)
+  plt.savefig("scatter_2.png")
+  plt.scatter(df.measurement_14, df.measurement_15, c = df.loading,alpha=0.5)
+  plt.savefig("scatter_3.png")
+  df['failure'].value_counts().plot(kind='bar')
+  plt.savefig("bar.png")
+  sns.distplot(df["loading"])
+  plt.savefig("loading_dist.png")
+  sns.distplot(df["attribute_3"])
+  plt.savefig("attribute_3.png")
+  sns.catplot(x='measurement_3', y='measurement_4', hue='failure', data=df, kind='violin')
+  plt.savefig("swarmplot.png")
+  sns.heatmap(df.select_dtypes(include="number").corr())
+  plt.savefig("corr.png")
+  plots = ["corr.png","scatter.png", "bar.png", "loading_dist.png", "attribute_3.png", "swarmplot.png"]
+  return plots
+  
+inputs = [gr.Dataframe()]
+outputs = [gr.Gallery().style(grid=(3,3))]
+
+gr.Interface(plot, inputs=inputs, outputs=outputs, examples=[df.head(100)]).launch()