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| import streamlit as st | |
| import pickle | |
| import pandas as pd | |
| from PIL import Image | |
| import datetime | |
| # Load File | |
| with open('./src/model_best.pkl', 'rb') as file: | |
| best_pipe = pickle.load(file) | |
| def run(): | |
| # Title | |
| st.title('Equipment in Smart Manufacturing for Predictive Maintenance') | |
| # Sub Header | |
| st.subheader('Equipment Predictive Maintenance Prediction') | |
| # Image | |
| image = Image.open('./src/image2.jpg') | |
| st.image(image) | |
| # Create form | |
| with st.form(key='maintenance-prediction'): | |
| st.markdown('Data ID') | |
| date = st.date_input("Select a date") | |
| time = st.time_input("Select a time") | |
| timestamp = datetime.datetime.combine(date, time) | |
| machine_id = st.text_input('Machine ID', value='---machine id--') | |
| st.markdown('Equipment Operation Parameters') | |
| temperature = st.number_input('Temperature', min_value=0.00, max_value=200.00, value=0.00) | |
| vibration = st.number_input('Vibration', min_value=-20.00, max_value=200.00, value=0.00) | |
| humidity = st.number_input('Humidity', min_value=0.00, max_value=85.00, value=0.00) | |
| pressure = st.number_input('Pressure', min_value=0.00, max_value=6.00, value=0.00) | |
| energy_consumption = st.number_input('Energy Consumption', min_value=0.00, max_value=6.00, value=0.00) | |
| st.markdown('Equipment Status and Condition') | |
| machine_status = st.selectbox('Machine Status', (0, 1), index=0, help='0 = not running, 1 = running') | |
| anomaly_flag = st.selectbox('Anomaly Flag', (0, 1), index=0, help='0 = normal temperature & vibration, 1 = extreme temperature & vibration') | |
| predicted_remaining_life = st.number_input ('Remaining life Prediction', min_value=0, max_value=500, value=0) | |
| failure_type = st.selectbox('Failure Type', ('Normal', 'Vibration Issue', 'Overheating', 'Pressure Drop', 'Electrical Fault'), index=0) | |
| downtime_risk = st.number_input('Downtime Risk Score', min_value=0.00, max_value=1.00, value=0.00, help='range from 0-1') | |
| submitted = st.form_submit_button('Predict') | |
| # Data inference | |
| data_inf_input = { | |
| 'timestamp': timestamp, | |
| 'machine_id': machine_id, | |
| 'temperature': temperature, | |
| 'vibration': vibration, | |
| 'humidity': humidity, | |
| 'pressure': pressure, | |
| 'energy_consumption': energy_consumption, | |
| 'machine_status': machine_status, | |
| 'anomaly_flag': anomaly_flag, | |
| 'predicted_remaining_life': predicted_remaining_life, | |
| 'failure_type': failure_type, | |
| 'downtime_risk': downtime_risk, | |
| } | |
| # Data frame | |
| st.markdown('Data Summary:') | |
| data_inference = pd.DataFrame([data_inf_input]) | |
| st.dataframe(data_inference) | |
| st.markdown('Result:') | |
| if submitted: | |
| # Prediction (0/1) | |
| pred = best_pipe.predict(data_inference) | |
| if pred == 1: | |
| st.write('### Equipment NEEDS Maintenance') | |
| else: | |
| st.write('### Equipment NO NEED Maintenance') | |
| if __name__ == '__main__': | |
| run() |