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app.py
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app.py
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import pickle
from flask import Flask, request, jsonify, app, url_for, render_template
import numpy as np
import pandas as pd
app = Flask(__name__)
# Load the model
reg_model = pickle.load(open('model_lr_boston.pkl', 'rb'))
scaler = pickle.load(open('scaling_lr.pkl', 'rb'))
@app.route('/')
def home():
return render_template('home.html')
@app.route('/predict_api', methods=['POST'])
def predict_api():
data = request.json['data']
print(data)
print(np.array(list(data.values())).reshape(1, -1))
new_data = scaler.transform(np.array(list(data.values())).reshape(1, -1))
output = reg_model.predict(new_data)
print(output[0])
return jsonify({'prediction': output[0]})
@app.route('/predict', methods=['POST'])
def predict():
data = [float(x) for x in request.form.values()]
final_input = scaler.transform(np.array(data).reshape(1, -1))
print(final_input)
# print(np.array(list(data.values())).reshape(1, -1))
# new_data = scaler.transform(np.array(list(data.values())).reshape(1, -1))
output = reg_model.predict(final_input)[0]
return render_template('home.html', prediction_text='Predicted Price is {}'.format(output))
if __name__ == '__main__':
app.run(debug=True)