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README.md

@@ -1,13 +1,12 @@
 ---
-title: JupyterLab
-emoji: 💻🐳
-colorFrom: gray
+title: Machine Failure Predictor
+emoji: 📊
+colorFrom: purple
 colorTo: green
-sdk: docker
+sdk: gradio
+sdk_version: 4.25.0
+app_file: app.py
 pinned: false
-tags:
-  - jupyterlab
-suggested_storage: small
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import uuid
+import joblib
+import json
+
+import gradio as gr
+import pandas as pd
+
+from huggingface_hub import CommitScheduler
+from pathlib import Path
+
+# Run the training script in the same directory
+
+os.system("python train.py")
+
+# Load the freshly trained model
+
+machine_failure_predictor = joblib.load('model.joblib')
+
+# Prepare the logging functionality
+
+log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
+log_folder = log_file.parent
+
+scheduler = CommitScheduler(
+    repo_id="machine-failure-logs",
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=2
+)
+
+# Set up UI components for input and output
+
+air_temperature_input = gr.Number(label='Air temperature [K]')
+process_temperature_input = gr.Number(label='Process temperature [K]')
+rotational_speed_input = gr.Number(label='Rotational speed [rpm]')
+torque_input = gr.Number(label='Torque [Nm]')
+tool_wear_input = gr.Number(label='Tool wear [min]')
+type_input = gr.Dropdown(
+    ['L', 'M', 'H'],
+    label='Type'
+)
+
+model_output = gr.Label(label="Machine failure")
+
+# Define the predict function that runs when 'Submit' is clicked or when a API request is made
+def predict_machine_failure(air_temperature, process_temperature, rotational_speed, torque, tool_wear, type):
+    sample = {
+        'Air temperature [K]': air_temperature,
+        'Process temperature [K]': process_temperature,
+        'Rotational speed [rpm]': rotational_speed,
+        'Torque [Nm]': torque,
+        'Tool wear [min]': tool_wear,
+        'Type': type
+    }
+    data_point = pd.DataFrame([sample])
+    prediction = machine_failure_predictor.predict(data_point).tolist()
+
+    with scheduler.lock:
+        with log_file.open("a") as f:
+            f.write(json.dumps(
+                {
+                    'Air temperature [K]': air_temperature,
+                    'Process temperature [K]': process_temperature,
+                    'Rotational speed [rpm]': rotational_speed,
+                    'Torque [Nm]': torque,
+                    'Tool wear [min]': tool_wear,
+                    'Type': type,
+                    'prediction': prediction[0]
+                }
+            ))
+            f.write("\n")
+            
+    return prediction[0]
+
+# Create the interface
+demo = gr.Interface(
+    fn=predict_machine_failure,
+    inputs=[air_temperature_input, process_temperature_input, rotational_speed_input, 
+            torque_input, tool_wear_input, type_input],
+    outputs=model_output,
+    title="Machine Failure Predictor",
+    description="This API allows you to predict the machine failure status of an equipment",
+    allow_flagging="auto",
+    concurrency_limit=8
+)
+
+# Launch with a load balancer
+demo.queue()
+demo.launch(share=False)

client_requirements.txt

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+scikit-learn==1.2.2
+gradio-client==0.15.0

inference.py

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+"""
+Obtain Predictions for Machine Failure Predictor Model using Gradio Client
+======================================================================
+
+This script connects to a deployed machine failure predictor model using Gradio Client,
+fetches the dataset, preprocesses the data, and generates predictions for a
+sample of test data using the deployed model. The resulting predictions are
+stored in a list. A time delay of one second is added after each prediction
+submission to avoid overloading the model server.
+"""
+
+import time
+
+from gradio_client import Client
+
+from sklearn.datasets import fetch_openml
+from sklearn.model_selection import train_test_split
+
+
+client = Client("pgurazada1/machine-failure-predictor")
+
+dataset = fetch_openml(data_id=42890, as_frame=True, parser="auto")
+
+data_df = dataset.data
+
+target = 'Machine failure'
+numeric_features = [
+    'Air temperature [K]',
+    'Process temperature [K]',
+    'Rotational speed [rpm]',
+    'Torque [Nm]',
+    'Tool wear [min]'
+]
+categorical_features = ['Type']
+
+X = data_df[numeric_features + categorical_features]
+y = data_df[target]
+
+Xtrain, Xtest, ytrain, ytest = train_test_split(
+    X, y,
+    test_size=0.2,
+    random_state=42
+)
+
+Xtest_sample = Xtest.sample(100)
+
+Xtest_sample_rows = list(Xtest_sample.itertuples(index=False, name=None))
+
+batch_predictions = []
+
+for row in Xtest_sample_rows:
+    try:
+        job = client.submit(
+            air_temperature=row[0],
+            process_temperature=row[1],
+            rotational_speed=row[2],
+            torque=row[3],
+            tool_wear=row[4],
+            type=row[5],
+            api_name="/predict"
+        )
+
+        batch_predictions.append(job.result())
+
+        time.sleep(1)
+
+    except Exception as e:
+        print(e)

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a0db284be28e1303ab3612a3a6e35076ff8e9e32c035dd4e2ffdf9635b940780
+size 3838

requirements.txt

@@ -1,4 +1 @@
-jupyterlab==3.6.1
-jupyter-server==2.3.0
-tornado==6.2
-ipywidgets
+scikit-learn==1.2.2

train.py

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+
+import joblib
+
+from sklearn.datasets import fetch_openml
+
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import make_column_transformer
+
+from sklearn.pipeline import make_pipeline
+
+from sklearn.model_selection import train_test_split, RandomizedSearchCV
+
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score, classification_report
+
+dataset = fetch_openml(data_id=42890, as_frame=True, parser="auto")
+
+data_df = dataset.data
+
+target = 'Machine failure'
+numeric_features = [
+    'Air temperature [K]',
+    'Process temperature [K]',
+    'Rotational speed [rpm]',
+    'Torque [Nm]',
+    'Tool wear [min]'
+]
+categorical_features = ['Type']
+
+print("Creating data subsets")
+
+X = data_df[numeric_features + categorical_features]
+y = data_df[target]
+
+Xtrain, Xtest, ytrain, ytest = train_test_split(
+    X, y,
+    test_size=0.2,
+    random_state=42
+)
+
+preprocessor = make_column_transformer(
+    (StandardScaler(), numeric_features),
+    (OneHotEncoder(handle_unknown='ignore'), categorical_features)
+)
+
+model_logistic_regression = LogisticRegression(n_jobs=-1)
+
+print("Estimating Best Model Pipeline")
+
+model_pipeline = make_pipeline(
+    preprocessor,
+    model_logistic_regression
+)
+
+param_distribution = {
+    "logisticregression__C": [0.001, 0.01, 0.1, 0.5, 1, 5, 10]
+}
+
+rand_search_cv = RandomizedSearchCV(
+    model_pipeline,
+    param_distribution,
+    n_iter=3,
+    cv=3,
+    random_state=42
+)
+
+rand_search_cv.fit(Xtrain, ytrain)
+
+print("Logging Metrics")
+print(f"Accuracy: {rand_search_cv.best_score_}")
+
+print("Serializing Model")
+
+saved_model_path = "model.joblib"
+
+joblib.dump(rand_search_cv.best_estimator_, saved_model_path)