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<html>
<head>
<script type="module" crossorigin src="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js"></script>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css" />
</head>
<body>
<gradio-lite>
<gradio-requirements>
lightgbm
plotly
scikit-learn
seaborn
</gradio-requirements>
<gradio-file name="app.py" entrypoint>
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()
</gradio-file>
</gradio-lite>
</body>
</html> |