Deploy predictive maintenance model

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app.py .
+COPY model.pkl .
+
+EXPOSE 7860
+
+CMD ["python", "app.py"]

app.py

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+from flask import Flask, request, jsonify
+import joblib
+import pandas as pd
+
+app = Flask(__name__)
+model = joblib.load("model.pkl")
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    """
+    Predict machine failure
+    Expected JSON format:
+    {
+        "Type": 1,
+        "Air temperature [K]": 300.0,
+        "Process temperature [K]": 310.0,
+        "Rotational speed [rpm]": 1500,
+        "Torque [Nm]": 40.0,
+        "Tool wear [min]": 100
+    }
+    """
+    data = request.json
+    df = pd.DataFrame([data])
+    prediction = int(model.predict(df)[0])
+    probability = float(model.predict_proba(df)[0][1])
+
+    return jsonify({
+        "prediction": prediction,
+        "failure_probability": probability,
+        "status": "failure" if prediction == 1 else "normal"
+    })
+
+@app.route("/health", methods=["GET"])
+def health():
+    return jsonify({"status": "healthy"})
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:aa8129cd105089df9f9a42c9808f6818c1fa0339a28d6f01e2cafa327d8fce15
+size 2050841

requirements.txt

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+flask==3.0.0
+scikit-learn==1.3.2
+pandas==2.1.4
+joblib==1.3.2