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| import streamlit as st | |
| from streamlit_extras import add_vertical_space | |
| import streamlit.components.v1 as components | |
| from annotated_text import annotated_text | |
| st.set_page_config(layout='wide') | |
| import pandas as pd | |
| import json | |
| import plotly.express as px | |
| import plotly.graph_objs as go | |
| from src.Predictive_Maintenance.pipelines.prediction_pipeline import prediction | |
| with st.sidebar: | |
| st.title("Predictive Maintenance Project") | |
| choice = st.radio("Choose from the below options:", ["Main","EDA","Monitoring Reports","Performance Measures","Prediction"]) | |
| if choice == "Main": | |
| with open("frontend/main/main_page.md", "r") as file: | |
| readme_contents = file.read() | |
| st.markdown(readme_contents) | |
| if choice == "EDA": | |
| st.title('Exploratory Data Analysis') | |
| st.header('Question 1') | |
| st.write("What is the frequency distribution of the target label 'machine failure' in the dataset? How many instances indicate a failure compared to those that do not??") | |
| st.image("reports/q1.png") | |
| st.write("**The success rate of the machine is 96.52% and the highest type of failure is HDF(Heat Dissipation Failure) with 1.15% failure rate**") | |
| st.header('Question 2') | |
| st.write("How is the 'productID' variable distributed across the dataset? Specifically, how many instances correspond to low, medium, and high-quality variants?") | |
| st.image("reports/q2.png") | |
| st.write("**Low quality varient makes up majority of the dataset with 60% of the data, followed by medium quality varient with 30% and high quality varient with 10%**") | |
| st.header('Question 3') | |
| st.write("What are the minimum, maximum, and typical values for 'air temperature', 'process temperature', 'rotational speed', 'torque', and 'tool wear'?") | |
| st.write("Are there any significant outliers in these variables?") | |
| st.image("reports/q3.png") | |
| st.write("**Rotational speed may or may not be actual outliers, therefore we'll keep them in the dataset for now.**") | |
| st.header('Question 4') | |
| st.write("Is there any correlation between the continuous variables and the 'machine failure' label? For example, does the tool wear increase the likelihood of machine failure?") | |
| st.image("reports/q4.png") | |
| st.write("**Null Hypothesis: There is no significant relationship between the different colums and Machine Failure**") | |
| st.write("**Alternate Hypothesis: There is a significant relationship between the tool wear and the machine failure label**") | |
| st.image("reports/h0.png") | |
| st.header('Question 5') | |
| st.write("Is there any correlation between the categorical variable 'productID' and the continuous variable? For example, is the 'rotational speed' higher for high-quality products than for low-quality products? ") | |
| st.image("reports/q5.png") | |
| st.write("**Process Temperature seems to have an effect on high quality varient machines. Therefore we can say that Process Temperature is correlated with machine type.**") | |
| st.header('Question 6') | |
| st.write("Are there any significant interactions or non-linear relationships between the variables that could be important for predictive maintenance? For example, does torque increase non-linearly with rotational speed?") | |
| st.image("reports/q5.png") | |
| st.write("**Process Temperature seems to have an effect on high quality varient machines. Therefore we can say that Process Temperature is correlated with machine type.**") | |
| st.header('Question 7') | |
| st.write("Are there any discernible patterns or anomalies in the timing of machine failures?How do machine failure rates change over time? ") | |
| st.image("reports/q5.png") | |
| st.write("**Process Temperature seems to have an effect on high quality varient machines. Therefore we can say that Process Temperature is correlated with machine type.**") | |
| st.header('Question 8') | |
| st.write("Do the distributions of continuous variables differ significantly across various product types?") | |
| st.image("reports/q5.png") | |
| # st.write("**Process Temperature seems to have an effect on high quality varient machines. Therefore we can say that Process Temperature is correlated with machine type.**") | |
| st.header('Question 9') | |
| st.write("How does machine failure frequency vary with different operating conditions, such as changes in air temperature and rotational speed?") | |
| st.image("reports/q5.png") | |
| st.write("**Process Temperature seems to have an effect on high quality varient machines. Therefore we can say that Process Temperature is correlated with machine type.**") | |
| if choice == "Performance Measures": | |
| st.title("Model 1") | |
| annotated_text(("Best Model 1", "Random Forest Classifier")) | |
| st.image("reports/model1n.png") | |
| st.title("Model 2") | |
| annotated_text(("Best Model 2", "Random Forest Classifier-registered")) | |
| st.image("reports/model2n.png") | |
| if choice == "Monitoring Reports": | |
| options = st.selectbox('Choose the reports: ',('Data Report', 'Model 1 report', 'Model 2 report')) | |
| if options=='Data Report': | |
| with open("reports/data_drift.html", "r",encoding="utf-8") as f: | |
| html_report = f.read() | |
| components.html(html_report, scrolling=True, height=700) | |
| if options=='Model 1 report': | |
| with open("reports/classification_performance_report.html", "r",encoding="utf-8") as f: | |
| html_report = f.read() | |
| components.html(html_report, height=750, scrolling=True) | |
| if options=='Model 2 report': | |
| with open("reports/classification_performance_report2.html", "r",encoding="utf-8") as f: | |
| html_report = f.read() | |
| components.html(html_report, height=750, scrolling=True) | |
| if choice == "Prediction": | |
| st.title('Predictive Maintenance') | |
| st.write("**Please enter the following parameters**") | |
| type = st.selectbox( | |
| 'Type',('Low', 'Medium', 'High')) | |
| st.write('You selected:', type) | |
| rpm = st.number_input('RPM', value=1500.0) | |
| st.write('The current rpm is ', rpm) | |
| torque = st.number_input('Torque', value=75) | |
| st.write('The current rpm is ', torque) | |
| tool_wear = st.number_input('Tool Wear', value=25.00) | |
| st.write('The current rpm is ', tool_wear) | |
| air_temp = st.number_input('Air Temperature', value=35.4) | |
| st.write('The current rpm is ', air_temp) | |
| process_temp = st.number_input('Process Temperature', value=46.65) | |
| st.write('The current rpm is ', process_temp) | |
| if st.button("Predict"): | |
| result1, result2 = prediction(type, rpm, torque, tool_wear, air_temp, process_temp) | |
| st.write("Machine Failure?: ", result1) | |
| st.write("Type of Failure: ", result2) | |
| #type, rpm, torque, tool_wear, air_temp, process_temp |