try1

.dvcignore

@@ -0,0 +1,3 @@
+# Add patterns of files dvc should ignore, which could improve
+# the performance. Learn more at
+# https://dvc.org/doc/user-guide/dvcignore

.gitattributes

@@ -0,0 +1 @@
+*.pkl filter=lfs diff=lfs merge=lfs -text

.github/workflows/sync2_HF_hub.yml

@@ -0,0 +1,23 @@
+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: |
+          # git remote add huggingface https://Meera2602:$HF_TOKEN@huggingface.co/spaces/Meera2602/pred-maintenance-app
+          # git push huggingface main --force
+          git push --force https://MEERA2602:$HF_TOKEN@huggingface.co/spaces/Meera2602/pred-maintenance-app main

Dockerfile

@@ -0,0 +1,6 @@
+FROM python:3.8-slim-buster
+WORKDIR /service
+COPY requirements.txt .
+COPY . ./
+RUN pip install -r requirements.txt
+ENTRYPOINT ["python3", "app.py"]

README.md

@@ -0,0 +1,136 @@
+---
+title: Pred Maintenance App
+emoji: 👷‍♀️🛠️
+colorFrom: yellow
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.34.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# Equipment Predictive Maintenance
+
+## Overview
+
+---
+
+Predictive maintenance can help companies minimize downtime, reduce repair costs, and improve operational efficiency. Developing a web application for predictive maintenance can provide users with real-time insights into equipment performance, enabling proactive maintenance, and reducing unplanned downtime.
+
+The important business questions that are solved by employing data driven approach using machine learning models are:
+
+- Predict whether an equipment will fail or not based.
+
+- Identifies the type of equipment failure.
+
+This web application is a demonstration of predictive maintenance using a synthetic dataset.  The dataset comprises of process parameters, ambient air and process temperatures, rotational speed, torque, and tool wear. 
+
+---
+
+## Performance metrics
+
+---
+
+To evaluate the performance of the ML models used in the project following metrics are used:
+
+- Precsion, recall, and F1 score of the machine learning models.
+- Responsiveness and ease of use of the web application.
+
+---
+
+The web application provides the following functionality:
+
+- Users can provide the process parameters to the model and receive a prediction of whether the equipment will fail or not, and the type of failure.
+- Users can view and infer the performance metrics of different machine learning models.
+- Users can visualize the data and gain insights into the behavior of the equipment.
+- The application should be built using Streamlit and deployed using Docker and Huggingface spaces.
+- The cost of deployment should be minimal.
+
+---
+
+## Problem Statement
+
+---
+
+The problem is to develop a machine learning model that predicts equipment failures based on process parameters.
+
+---
+## Dataset
+---
+
+The dataset consists of more than 10,000 data points stored as rows with 14 features in columns. The features include process parameters such as air and process temperatures, rotational speed, torque, and tool wear. The target variable is a binary label indicating whether the equipment failed or not.
+
+The dataset consists of 10 000 data points stored as rows with 14 features in columns
+
+UID: unique identifier ranging from 1 to 10000
+
+product ID: consisting of a letter L, M, or H for low (50% of all products), medium (30%) and high (20%) as
+
+product quality variants and a variant-specific serial number
+
+air temperature [K]: generated using a random walk process later normalized to a standard deviation of 2 K around 300 K
+
+process temperature [K]: generated using a random walk process normalized to a standard deviation of 1 K, added to the air temperature plus 10 K.
+
+rotational speed [rpm]: calculated from a power of 2860 W, overlaid with a normally distributed noise
+
+torque [Nm]: torque values are normally distributed around 40 Nm
+
+tool wear [min]: The quality variants H/M/L add 5/3/2 minutes of tool wear to the used tool in the process. and a 'machine failure' label that indicates, whether the machine has failed in this particular datapoint for any of the following failure modes are true.
+
+
+---
+## ML models
+---
+We will utilize both a binary classification model, and a multi-class classification model to predict equipment failures, and type of equipment failure respectively. 
+
+### Machine failure consists of five unique modes
+tool wear failure (TWF): the tool will be replaced of fail at a randomly selected tool wear time between 200 to 240 mins. At this point in time, the tool is replaced 69 times, and fails 51 times (randomly assigned).
+
+heat dissipation failure (HDF): heat dissipation causes a process failure, if the difference between air- and process temperature is below 8.6 K and the tool's rotational speed is below 1380 rpm. This is the case for 115 data points.
+
+power failure (PWF): the product of torque and rotational speed (in rad/s) equals the power required for the process. If this power is below 3500 W or above 9000 W, the process fails, which is the case 95 times in our dataset.
+
+overstrain failure (OSF): if the product of tool wear and torque exceeds 11,000 minNm for the L product variant (12,000 M, 13,000 H), the process fails due to overstrain. This is true for 98 datapoints.
+
+random failures (RNF): each process has a chance of 0,1 % to fail regardless of its process parameters. This is the case for only 5 datapoints, less than could be expected for 10,000 datapoints in our dataset.
+
+If at least one of the above failure modes is true, the process fails and the 'machine failure' label is set to 1. It is therefore not transparent to the machine learning method, which of the failure modes has caused the process to fail
+
+The following machine learning models will be used:
+
+- Random Forest
+- Decision Tree
+- Logistic Regression
+- Support Vector Machine (SVM)
+---
+## Generic steps
+---
+- Data preprocessing and cleaning
+- Feature engineering and selection
+- Model selection and training
+- Hyperparameter tuning
+- Model evaluation and testing
+---
+## Architecture
+---
+The web application architecture will consist of the following components:
+
+- A frontend web application built using Streamlit
+- A machine learning model for equipment failure prediction
+- Docker containers to run the frontend, backend, and model
+- Cloud infrastructure to host the application
+- CI/CD pipeline using GitHub Actions for automated deployment
+
+The frontend will interact with the backend server through API calls to request predictions, model training, and data storage. The backend server will manage user authentication, data storage, and model training. The machine learning model will be trained and deployed using Docker containers. The application will be hosted on Huggingface sppaces. The CI/CD pipeline will be used to automate the deployment process.
+
+---
+
+## Mlops practices
+
+---
+
+This project is designed to create an end-to-end workflow for developing and deploying a web application that performs data preprocessing, model training, model evaluation, and prediction. The pipeline leverages Docker containers for encapsulating code, artifacts, and both the frontend and backend components of the application. The application is deployed on a huggingface space to provide a cloud hosting solution.
+
+---

app.py

@@ -0,0 +1,181 @@
+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/model1.png")
+    
+    
+    
+    st.title("Model 2")
+    annotated_text(("Best Model 1", "Random Forest Classifier"))
+    st.image("reports/model2.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

artifacts/eda/question_four_ttest.json

@@ -0,0 +1 @@
+{"columns":["test-statistic","p-value","Hypothesis"],"index":["Air temperature [K]","Process temperature [K]","Rotational speed [rpm]","Torque [Nm]","Tool wear [min]"],"data":[[8.2830178518,1.354800148e-16,"Reject null hypothesis"],[3.5965621887,0.0003240058,"Reject null hypothesis"],[-4.4226338712,0.0000098535,"Reject null hypothesis"],[19.4901955716,4.573804886e-83,"Reject null hypothesis"],[10.6028805888,3.976075963e-26,"Reject null hypothesis"]]}

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frontend/main/main_page.md

@@ -0,0 +1,124 @@
+# Equipment Predictive Maintenance
+
+## Overview
+
+---
+
+Predictive maintenance can help companies minimize downtime, reduce repair costs, and improve operational efficiency. Developing a web application for predictive maintenance can provide users with real-time insights into equipment performance, enabling proactive maintenance, and reducing unplanned downtime.
+
+The important business questions that are solved by employing data driven approach using machine learning models are:
+
+- Predict whether an equipment will fail or not based.
+
+- Identifies the type of equipment failure.
+
+This web application is a demonstration of predictive maintenance using a synthetic dataset.  The dataset comprises of process parameters, ambient air and process temperatures, rotational speed, torque, and tool wear. 
+
+---
+
+## Performance metrics
+
+---
+
+To evaluate the performance of the ML models used in the project following metrics are used:
+
+- Precsion, recall, and F1 score of the machine learning models.
+- Responsiveness and ease of use of the web application.
+
+---
+
+The web application provides the following functionality:
+
+- Users can provide the process parameters to the model and receive a prediction of whether the equipment will fail or not, and the type of failure.
+- Users can view and infer the performance metrics of different machine learning models.
+- Users can visualize the data and gain insights into the behavior of the equipment.
+- The application should be built using Streamlit and deployed using Docker and Huggingface spaces.
+- The cost of deployment should be minimal.
+
+---
+
+## Problem Statement
+
+---
+
+The problem is to develop a machine learning model that predicts equipment failures based on process parameters.
+
+---
+## Dataset
+---
+
+The dataset consists of more than 10,000 data points stored as rows with 14 features in columns. The features include process parameters such as air and process temperatures, rotational speed, torque, and tool wear. The target variable is a binary label indicating whether the equipment failed or not.
+
+The dataset consists of 10 000 data points stored as rows with 14 features in columns
+
+UID: unique identifier ranging from 1 to 10000
+
+product ID: consisting of a letter L, M, or H for low (50% of all products), medium (30%) and high (20%) as
+
+product quality variants and a variant-specific serial number
+
+air temperature [K]: generated using a random walk process later normalized to a standard deviation of 2 K around 300 K
+
+process temperature [K]: generated using a random walk process normalized to a standard deviation of 1 K, added to the air temperature plus 10 K.
+
+rotational speed [rpm]: calculated from a power of 2860 W, overlaid with a normally distributed noise
+
+torque [Nm]: torque values are normally distributed around 40 Nm
+
+tool wear [min]: The quality variants H/M/L add 5/3/2 minutes of tool wear to the used tool in the process. and a 'machine failure' label that indicates, whether the machine has failed in this particular datapoint for any of the following failure modes are true.
+
+
+---
+## ML models
+---
+We will utilize both a binary classification model, and a multi-class classification model to predict equipment failures, and type of equipment failure respectively. 
+
+### Machine failure consists of five unique modes
+tool wear failure (TWF): the tool will be replaced of fail at a randomly selected tool wear time between 200 to 240 mins. At this point in time, the tool is replaced 69 times, and fails 51 times (randomly assigned).
+
+heat dissipation failure (HDF): heat dissipation causes a process failure, if the difference between air- and process temperature is below 8.6 K and the tool's rotational speed is below 1380 rpm. This is the case for 115 data points.
+
+power failure (PWF): the product of torque and rotational speed (in rad/s) equals the power required for the process. If this power is below 3500 W or above 9000 W, the process fails, which is the case 95 times in our dataset.
+
+overstrain failure (OSF): if the product of tool wear and torque exceeds 11,000 minNm for the L product variant (12,000 M, 13,000 H), the process fails due to overstrain. This is true for 98 datapoints.
+
+random failures (RNF): each process has a chance of 0,1 % to fail regardless of its process parameters. This is the case for only 5 datapoints, less than could be expected for 10,000 datapoints in our dataset.
+
+If at least one of the above failure modes is true, the process fails and the 'machine failure' label is set to 1. It is therefore not transparent to the machine learning method, which of the failure modes has caused the process to fail
+
+The following machine learning models will be used:
+
+- Random Forest
+- Decision Tree
+- Logistic Regression
+- Support Vector Machine (SVM)
+---
+## Generic steps
+---
+- Data preprocessing and cleaning
+- Feature engineering and selection
+- Model selection and training
+- Hyperparameter tuning
+- Model evaluation and testing
+---
+## Architecture
+---
+The web application architecture will consist of the following components:
+
+- A frontend web application built using Streamlit
+- A machine learning model for equipment failure prediction
+- Docker containers to run the frontend, backend, and model
+- Cloud infrastructure to host the application
+- CI/CD pipeline using GitHub Actions for automated deployment
+
+The frontend will interact with the backend server through API calls to request predictions, model training, and data storage. The backend server will manage user authentication, data storage, and model training. The machine learning model will be trained and deployed using Docker containers. The application will be hosted on Huggingface sppaces. The CI/CD pipeline will be used to automate the deployment process.
+
+---
+
+## Mlops practices
+
+---
+
+This project is designed to create an end-to-end workflow for developing and deploying a web application that performs data preprocessing, model training, model evaluation, and prediction. The pipeline leverages Docker containers for encapsulating code, artifacts, and both the frontend and backend components of the application. The application is deployed on a huggingface space to provide a cloud hosting solution.
+
+---

mlruns/0/0f148d87fa9e434e8be67038ff601c1e/artifacts/Logistic Regression/MLmodel

@@ -0,0 +1,20 @@
+artifact_path: Logistic Regression
+flavors:
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+      conda: conda.yaml
+      virtualenv: python_env.yaml
+    loader_module: mlflow.sklearn
+    model_path: model.pkl
+    predict_fn: predict
+    python_version: 3.11.9
+  sklearn:
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+    pickled_model: model.pkl
+    serialization_format: cloudpickle
+    sklearn_version: 1.3.0
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+model_uuid: dcd249d24f0842a3ba42ccc1171c80a0
+run_id: 0f148d87fa9e434e8be67038ff601c1e
+utc_time_created: '2024-05-18 18:28:17.570941'

mlruns/0/0f148d87fa9e434e8be67038ff601c1e/artifacts/Logistic Regression/conda.yaml

@@ -0,0 +1,15 @@
+channels:
+- conda-forge
+dependencies:
+- python=3.11.9
+- pip<=24.0
+- pip:
+  - mlflow==2.12.2
+  - cloudpickle==3.0.0
+  - numpy==1.26.4
+  - packaging==23.2
+  - psutil==5.9.8
+  - pyyaml==6.0.1
+  - scikit-learn==1.3.0
+  - scipy==1.13.0
+name: mlflow-env

mlruns/0/0f148d87fa9e434e8be67038ff601c1e/artifacts/Logistic Regression/metadata/MLmodel

@@ -0,0 +1,20 @@
+artifact_path: Logistic Regression
+flavors:
+  python_function:
+    env:
+      conda: conda.yaml
+      virtualenv: python_env.yaml
+    loader_module: mlflow.sklearn
+    model_path: model.pkl
+    predict_fn: predict
+    python_version: 3.11.9
+  sklearn:
+    code: null
+    pickled_model: model.pkl
+    serialization_format: cloudpickle
+    sklearn_version: 1.3.0
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+model_size_bytes: 1212
+model_uuid: dcd249d24f0842a3ba42ccc1171c80a0
+run_id: 0f148d87fa9e434e8be67038ff601c1e
+utc_time_created: '2024-05-18 18:28:17.570941'

mlruns/0/0f148d87fa9e434e8be67038ff601c1e/artifacts/Logistic Regression/metadata/conda.yaml

@@ -0,0 +1,15 @@
+channels:
+- conda-forge
+dependencies:
+- python=3.11.9
+- pip<=24.0
+- pip:
+  - mlflow==2.12.2
+  - cloudpickle==3.0.0
+  - numpy==1.26.4
+  - packaging==23.2
+  - psutil==5.9.8
+  - pyyaml==6.0.1
+  - scikit-learn==1.3.0
+  - scipy==1.13.0
+name: mlflow-env

mlruns/0/0f148d87fa9e434e8be67038ff601c1e/artifacts/Logistic Regression/metadata/python_env.yaml

@@ -0,0 +1,7 @@
+python: 3.11.9
+build_dependencies:
+- pip==24.0
+- setuptools
+- wheel
+dependencies:
+- -r requirements.txt

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+packaging==23.2
+psutil==5.9.8
+pyyaml==6.0.1
+scikit-learn==1.3.0
+scipy==1.13.0

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mlruns/0/0f148d87fa9e434e8be67038ff601c1e/artifacts/Logistic Regression/python_env.yaml

@@ -0,0 +1,7 @@
+python: 3.11.9
+build_dependencies:
+- pip==24.0
+- setuptools
+- wheel
+dependencies:
+- -r requirements.txt

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+psutil==5.9.8
+pyyaml==6.0.1
+scikit-learn==1.3.0
+scipy==1.13.0

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mlruns/0/0f148d87fa9e434e8be67038ff601c1e/metrics/Recall

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