| <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> |