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# Import necessary libraries
import numpy as np
import joblib # For loading the serialized model
import pandas as pd # For data manipulation
from flask import Flask, request, jsonify # For creating the Flask API
# Initialize Flask app with a name
nozzleselect_api = Flask("ABMs Nozzle Selection") #define the name of the app
# Load the trained prediction model
model = joblib.load("nozzle_selection_model.joblib") #define the location of the serialized model
# Define a route for the home page
@nozzleselect_api.get('/')
def home():
"""
This function handles GET requests to the root URL ('/') of the API.
It returns a simple welcome message.
"""
return "Welcome to ABM's Nozzle Select Predictor API!" #define a welcome message
# Define an endpoint to predict churn for a single customer
@nozzleselect_api.post('/v1/predict')
def predict_nozzle():
"""
This function handles POST requests to the '/v1/predict' endpoint.
It expects a JSON payload containing property details and returns
the predicted sales outcome price as a JSON response.
"""
# Get JSON data from the request
data = request.get_json()
# Extract relevant product ans store features from the input data. The order of the column names matters.
sample = {
'nozzle': data['nozzle'],
'cg': data['cg'],
'cg_fw': data['cg_fw'],
# 'Decide': data['Decide']
}
# Convert the extracted data into a DataFrame
input_data = pd.DataFrame([sample])
# Make a sales prediction using the trained model
prediction = model.predict(input_data).tolist()[0]
# Return the prediction as a JSON response
return jsonify({'Nozzle_Pressure': prediction})
"""
# Define an endpoint for batch prediction (POST request)
@nozzleselect_api.post('/v1/predictbatch')
def predict_nozzle_batch():
This function handles POST requests to the '/v1/predictbatch' endpoint.
It expects a CSV file containing property details for multiple properties
and returns the predicted rental prices as a dictionary in the JSON response.
# Get the uploaded CSV file from the request
file = request.files['file']
# Read the CSV file into a Pandas DataFrame
input_data = pd.read_csv(file)
# Make predictions for all properties in the DataFrame
predicted_nozzle = model.predict(input_data).tolist()
# Return the prediction as a JSON response
return jsonify({'nozzle': predicted_nozzle})
"""
# Run the Flask app in debug mode
if __name__ == '__main__':
nozzleselect_api.run(debug=True)
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