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Dockerfile

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+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9-slim
+
+# Set the working directory inside the container to /app
+WORKDIR /app
+
+# Copy all files from the current directory to the container's working directory
+COPY . .
+
+# Install dependencies from the requirements file without using cache to reduce image size
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Define the command to start the application using Gunicorn with 4 worker processes
+# - `-w 4`: Uses 4 worker processes for handling requests
+# - `-b 0.0.0.0:7860`: Binds the server to port 7860 on all network interfaces
+# - `app:app`: Runs the Flask app (assuming `app.py` contains the Flask instance named `app`)
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:SuperKart_predictor_api"]

app.py

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+
+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+# Initialize Flask app with a name
+pred_mainteanance_api = Flask ("Engine Maintenance Predictor")
+
+# Load the trained churn prediction model
+model = joblib.load ("???.joblib")
+
+# Define a route for the home page
+@pred_mainteanance_api.get ('/')
+def home ():
+    return "Welcome to the Engine Maintenance Prediction!"
+
+# Define an endpoint to predict sales for Super Kart
+@pred_mainteanance_api.post ('/v1/EngPredMaintenance')
+def predict_need_maintenance ():
+    # Get JSON data from the request
+    engine_sensor_inputs = request.get_json ()
+
+    import datetime
+
+    current_year = datetime.datetime.now ().year   # dynamic current year
+
+    # Extract relevant features from the input data
+    data_info = {
+        'Engine_rpm'                : engine_sensor_inputs ['Engine_rpm'],
+        'Lub_oil_pressure'          : engine_sensor_inputs ['Lub_oil_pressure'],
+        'Fuel_pressure'             : engine_sensor_inputs ['Fuel_pressure'],
+        'Coolant_pressure'          : engine_sensor_inputs ['Coolant_pressure'],
+        'lub_oil_temp'              : engine_sensor_inputs ['lub_oil_temp'],
+        'Coolant_temp'              : engine_sensor_inputs ['Coolant_temp']
+    }
+
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame ([data_info])
+
+    # Enforce types - convert all to float
+    input_data = input_data.astype (float)
+
+    # Make prediction using the trained model
+    predicted_sales = model.predict (input_data).tolist ()[0]
+
+    # Return the prediction as a JSON response
+    return jsonify ({'NeedsMaintenance': predicted_sales})
+
+
+# Run the Flask app
+if __name__ == "__main__":
+    import os
+    port = int (os.environ.get("PORT", 7860))
+    pred_mainteanance_api.run(host="0.0.0.0", port=port)

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
+requests==2.28.1
+uvicorn[standard]
+streamlit==1.43.2
+dill==0.3.8