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from flask import *
import seaborn as sns
from sklearn.linear_model import LogisticRegression
app = Flask(__name__)
@app.route('/predict', methods =['POST'])
def predictflower():
# receive all four values
# send these 4 values to predict method of model
# return the flower type returned by predict method
# def greet_json():
iris1 =sns.load_dataset("iris")
modlog= LogisticRegression (max_iter=300)
irisarr = iris1.values
X = irisarr[:,0:4]
Y = irisarr[:,4]
modlog.fit(X,Y)
sl = float(request.form['sl'])
sw = float(request.form['sw'] )
pl = float(request.form['pl'])
pw = float(request.form['pw'])
res = modlog.predict([[ sl , sw ,pl ,pw ] ])
return render_template("form.html" , result = res )
@app.route('/')
def hello_world():
return render_template("form.html")
if __name__ == '__main__':
app.run()
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