def predict():
if request.method == 'POST':
Cylinders = int(request.form['Cylinders'])
Displacement = float(request.form['Displacement'])
Horsepower = float(request.form['Horsepower'])
Weight = float(request.form['Weight'])
Acceleration = float(request.form['Acceleration'])
Model_Year = int(request.form['Model_Year'])
Origin = int(request.form['Origin'])
if Origin == 1:
country = [1, 2, 3]
elif Origin == 2:
country = [2, 1, 3]
else:
country = [3, 1, 2]
vehicle_config = {
'Cylinders': [Cylinders, 2, 4],
'Displacement': [Displacement, 100, 130],
'Horsepower': [Horsepower, 93, 120],
'Weight': [Weight, 2500, 3500],
'Acceleration': [Acceleration, 13, 15],
'Model Year': [Model_Year, 78, 82],
'Origin': country
}
# data = np.array([[preg, glucose, bp, st, insulin, bmi, dpf, age]])
my_prediction = predict_mpg(vehicle_config, classifier)
return render_template('result.html', prediction=my_prediction[0])
def predict():
vehicle = request.get_json()
print(vehicle)
with open('./model_files/model.bin', 'rb') as f_in:
model = pickle.load(f_in)
f_in.close()
predictions = predict_mpg(vehicle, model)
result = {'mpg_prediction': list(predictions)}
return jsonify(result)
def predict():
vehicle_config = request.get_json()
with open(" ./model_files/model.bin", 'rb') as f_in:
model = pickle.load(f_in)
f_in.close()
predictions = predict_mpg(vehicle_config, model)
response = {'mpg_predictions': list(predictions)}
return jsonify(response)
def predict():
vehicle = request.get_json()
print(vehicle)
with open('./model_files/model.bin', 'rb') as f_in:
model = pickle.load(f_in)
f_in.close()
predictions = predict_mpg(vehicle, model)
result = {'mpg_prediction': list(predictions)}
return jsonify(result)
# @app.route('/ping', methods=['GET'])
# def ping():
# return "Pinging Model!!"
# Commented code below while deploying.
# if __name__ == '__main__':
# app.run(debug=True, host='localhost', port=9696)
