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app.py
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app.py
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import numpy as np
import pandas as pd
from flask import Flask, request, jsonify, render_template
import pickle
app = Flask(__name__)
from statsmodels.iolib.smpickle import load_pickle
model = load_pickle("slr_wcat.pkl")
@app.route('/')
def home():
return render_template('startup.html')
@app.route('/predict',methods=['POST'])
def predict():
'''
For rendering results on HTML GUI
'''
float_features = [float(x) for x in request.form.values()]
waist = float_features[0];
waist_sq = waist*waist
waist_cb = waist*waist*waist
wcat = pd.DataFrame([[waist, waist_sq, waist_cb]], columns=["Waist", "Waist_sq", "Waist_cb"])
x = np.exp(model.predict(wcat))
print(float(round(x,2)))
# flt_features = [float(x) for x in request.form.values()]
## int_features = [int(x) for x in request.form.values()]
# final_features = [np.array(int_features)]
# prediction = model.predict(final_features)
#
# output = round(prediction[0], 2)
#
# return render_template('index.html', prediction_text='Employee Salary should be $ {}'.format(output))
return render_template('startup.html', prediction_text= "Adipose Tissue Size is {}".format(float(round(x,2))))
if __name__ == "__main__":
app.run(debug=True)