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| import streamlit as st | |
| import pandas as pd | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| from PIL import Image | |
| def run(): | |
| # Title | |
| st.title('Equipment in Smart Manufacturing for Predictive Maintenance') | |
| # Sub Header | |
| st.subheader('Exploratory Data Analysis (EDA) of dataset') | |
| # Image | |
| image = Image.open('./src/image1.jpg') | |
| st.image(image, caption= "Factory Landscape") | |
| # DataFrame | |
| st.write('##### Data frame') | |
| df = pd.read_csv('./src/smart_manufacturing_dataset.csv') | |
| st.dataframe(df) | |
| # EDA distribution numerical | |
| st.write('##### Distribution plot numerical') | |
| fig = plt. figure(figsize=(15, 5)) | |
| option = st.selectbox('Columns : ', ('temperature', 'vibration', 'humidity', 'pressure', 'energy_consumption', 'predicted_remaining_life', 'downtime_risk', 'maintenance_required')) | |
| sns.histplot(df[option], bins=30, kde=True) | |
| st.pyplot(fig) | |
| # EDA Heatmap correlation numerical with maintenance_required | |
| st.write('##### Heatmap correlation with maintenance_required') | |
| fig = plt. figure(figsize=(15, 10)) | |
| sns.heatmap(df[['temperature', 'vibration', 'humidity', 'pressure', 'energy_consumption', 'predicted_remaining_life', 'downtime_risk', 'maintenance_required']].corr(), annot=True, cmap='coolwarm') | |
| st.pyplot(fig) | |
| # EDA distribution categorical vs maintenance_required | |
| st.write('##### Distribution plot categorical vs maintenance_required') | |
| fig = plt. figure(figsize=(15, 5)) | |
| option = st.selectbox('Columns : ', ('machine_id', 'machine_status', 'anomaly_flag', 'failure_type')) | |
| sns.countplot(x=option, hue='maintenance_required', data=df) | |
| st.pyplot(fig) | |
| # EDA equipment that have maintenance_required=1 | |
| top_machines = ( | |
| df[df['maintenance_required'] == 1] | |
| .groupby('machine_id') | |
| .size() | |
| .sort_values(ascending=False)) | |
| st.write('##### Equipment maintenance signal accumulation') | |
| fig = plt.figure(figsize=(15,5)) | |
| sns.barplot( | |
| x=top_machines.index.astype(str), | |
| y=top_machines.values, | |
| hue=top_machines.index.astype(str), | |
| palette='rocket') | |
| st.pyplot(fig) | |
| if __name__ == '__main__': | |
| run() |