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app.py
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app.py
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import flask
import pickle
import pandas as pd
# Use pickle to load in the pre-trained model.
with open('model/bike_model_xgboost.pkl', 'rb') as f:
model = pickle.load(f)
app = flask.Flask(__name__, template_folder='templates')
@app.route('/', methods=['GET', 'POST'])
def main():
if flask.request.method == 'GET':
return(flask.render_template('main.html'))
if flask.request.method == 'POST':
temperature = flask.request.form['temperature']
humidity = flask.request.form['humidity']
windspeed = flask.request.form['windspeed']
input_variables = pd.DataFrame([[temperature, humidity, windspeed]],
columns=['temperature', 'humidity', 'windspeed'],
dtype=float)
prediction = model.predict(input_variables)[0]
return flask.render_template('main.html',
original_input={'Temperature':temperature,
'Humidity':humidity,
'Windspeed':windspeed},
result=prediction,
)
if __name__ == '__main__':
app.run()