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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
if __name__ == "__main__":
app.run(debug=True) |