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import joblib
import pandas as pd
from flask import Flask, request, jsonify
# Initialize Flask app
house_price_api = Flask("Boston House Price Predictor")
# Load the trained Boston housing model
model = joblib.load("boston_housing_model_v1_0.joblib")
# Define a route for the home page
@house_price_api.get('/')
def home():
return "Welcome to the Boston House Price Prediction API!"
# Define an endpoint to predict price for a single house
@house_price_api.post('/v1/house')
def predict_house_price():
# Get JSON data from the request
house_data = request.get_json()
# Extract relevant house features from the input data
sample = {
'CRIM': house_data['CRIM'],
'ZN': house_data['ZN'],
'INDUS': house_data['INDUS'],
'CHAS': house_data['CHAS'],
'NX': house_data['NX'], # should be NOX in your dataset, check consistency
'RM': house_data['RM'],
'AGE': house_data['AGE'],
'DIS': house_data['DIS'],
'RAD': house_data['RAD'],
'TAX': house_data['TAX'],
'PTRATIO': house_data['PTRATIO'],
'LSTAT': house_data['LSTAT']
}
# Convert the extracted data into a DataFrame
input_data = pd.DataFrame([sample])
# Make a prediction using the trained model
prediction = model.predict(input_data).tolist()[0]
# Return the prediction as a JSON response
return jsonify({'Predicted_MEDV': prediction})
# Define an endpoint to predict price for a batch of houses
@house_price_api.post('/v1/housebatch')
def predict_house_batch():
# Get the uploaded CSV file from the request
file = request.files['file']
# Read the file into a DataFrame
input_data = pd.read_csv(file)
# Make predictions for the batch data
predictions = model.predict(input_data).tolist()
# Add predictions to the DataFrame
input_data['Predicted_MEDV'] = predictions
# Convert results to dictionary
result = input_data.to_dict(orient="records")
return jsonify(result)
# Run the Flask app in debug mode
if __name__ == '__main__':
house_price_api.run(debug=True)
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