app.py

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")
  plt.scatter(df.measurement_16, df.measurement_15, c = df.loading,alpha=0.5)
  plt.savefig("scatter_4.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()