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af70fe3 5497177 af70fe3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | # 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 the Flask application
sales_price_predictor_api = Flask("Sales Price Predictor")
# Load the trained machine learning model
model = joblib.load("sales_price_prediction_model_v1_0.joblib")
# Define a route for the home page (GET request)
@sales_price_predictor_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 the Sales Price Prediction API!"
# Define an endpoint for single property prediction (POST request)
@sales_price_predictor_api.post('/v1/sale')
def predict_sale_price():
"""
This function handles POST requests to the '/v1/sale' endpoint.
It expects a JSON payload containing property details and returns
the predicted sales price as a JSON response.
"""
# Get the JSON data from the request body
property_data = request.get_json()
# Extract relevant features from the JSON data
sample = {
'Product_Weight': property_data['Product_Weight'],
'Product_Allocated_Area': property_data['Product_Allocated_Area'],
'Product_MRP': property_data['Product_MRP'],
'Product_Sugar_Content': property_data['Product_Sugar_Content'],
'Store_Size': property_data['Store_Size'],
'Store_Location_City_Type': property_data['Store_Location_City_Type'],
'Store_Type': property_data['Store_Type'],
'Product_Type': property_data['Product_Type'],
}
# Convert the extracted data into a Pandas DataFrame
input_data = pd.DataFrame([sample])
# Make prediction (get log_price)
predicted_sale_price = model.predict(input_data)[0]
# Return the actual price
return jsonify({'Predicted Price (in dollars)': predicted_sale_price})
# Define an endpoint for batch prediction (POST request)
@sales_price_predictor_api.post('/v1/salebatch')
def predict_sale_price_batch():
"""
This function handles POST requests to the '/v1/salebatch' endpoint.
It expects a CSV file containing property details for multiple properties
and returns the predicted sale 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 (get log_prices)
predicted_sale_prices = model.predict(input_data).tolist()
# Create a dictionary of predictions with sale IDs as keys
sale_ids = input_data['id'].tolist() # Assuming 'id' is the sale ID column
output_dict = dict(zip(sale_ids, predicted_sale_prices)) # Use actual prices
# Return the predictions dictionary as a JSON response
return output_dict
# Run the Flask application in debug mode if this script is executed directly
if __name__ == '__main__':
sales_price_predictor_api.run(debug=True)
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