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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.failure, alpha=0.5) | |
| plt.title("Measurement 13 vs 15 with Failure") | |
| plt.xlabel("Measurement 13") | |
| plt.ylabel("Measurement 15") | |
| plt.savefig("scatter.png") | |
| plt.scatter(df.measurement_10, df.measurement_15, c = df.failure, alpha=0.5) | |
| plt.title("Measurement 10 vs 15 with Failure") | |
| plt.savefig("scatter_2.png") | |
| plt.scatter(df.measurement_14, df.measurement_15, c = df.failure, alpha=0.5) | |
| plt.title("Measurement 13 vs 15 with Failure") | |
| plt.savefig("scatter_3.png") | |
| plt.scatter(df.measurement_16, df.measurement_15, c = df.failure, alpha=0.5) | |
| plt.title("Measurement 16 vs 15 with Failure") | |
| plt.savefig("scatter_4.png") | |
| df['failure'].value_counts().plot(kind='bar') | |
| plt.title("Number of failed vs successful products") | |
| plt.savefig("bar.png") | |
| sns.distplot(df["loading"]) | |
| plt.title("Distribution of Loading Variable") | |
| plt.savefig("loading_dist.png") | |
| sns.distplot(df["attribute_3"]) | |
| plt.title("Distribution of Attribute 3") | |
| plt.savefig("attribute_3.png") | |
| sns.catplot(x='measurement_3', y='measurement_4', hue='failure', data=df, kind='violin') | |
| plt.title("Violin Plot of Measurement 3 vs Measurement 4 with Failures") | |
| plt.xlabel("Measurement 3") | |
| plt.ylabel("Measurement 4") | |
| plt.savefig("violinplot.png") | |
| sns.heatmap(df.select_dtypes(include="number").corr()) | |
| plt.title("Correlation Between Numerical Variables") | |
| plt.savefig("corr.png") | |
| plots = ["corr.png","scatter.png", "scatter_2.png", "scatter_3.png", "scatter_4.png", "bar.png", "loading_dist.png", "attribute_3.png", "violinplot.png"] | |
| return plots | |
| inputs = [gr.Dataframe(label="Supersoaker Production Data")] | |
| outputs = [gr.Gallery(label="Profiling Dashboard").style(grid=(3,3))] | |
| gr.Interface(plot, inputs=inputs, outputs=outputs, examples=[df.head(100)], title="Supersoaker Failures Analysis Dashboard", description="This is a data analysis & visualization dashboard based on supersoaker failures data.").launch() |