Create Controller.py

Controller.py

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+#define train and fit model
+#differentiate into train and test code 
+# service endpoint in seperate code- pass inputs to model convert it into json 
+import pandas as pd
+import pickle
+from flask import Flask, request, jsonify
+
+# Create a Flask app
+app = Flask(__name__)
+
+# Load the machine learning model from a pickle file
+model = pickle.load(open("model.pkl", "rb"))
+
+@app.route('/keepalive', methods=['GET'])
+def api_health():
+    return jsonify(Message="Success")
+
+# Define a route for making predictions
+@app.route("/predict", methods=["POST"])
+def predict():
+    # Get JSON data from the request
+    json_ = request.json
+
+    # Convert JSON data into a DataFrame
+    df = pd.DataFrame(json_)
+
+    # Use the loaded model to make predictions on the DataFrame
+    prediction = model.predict(df)
+
+    # Return the predictions as a JSON response
+    return jsonify({"Prediction": list(prediction)})
+
+# Run the Flask app when this script is executed