Update app.py

app.py

@@ -1,4 +1,5 @@
 import streamlit as st
+from sklearn import neighbors, datasets
 
 with st.form(key='my_form'):
   sLen = st.slider('sepal length (cm) ', 0.0, 10.0)
@@ -7,10 +8,10 @@ with st.form(key='my_form'):
   pWid = st.slider('petal Width (cm) ', 0.0, 10.0)
   st.form_submit_button('predict')
   
- from sklearn import neighbors, datasets
+ 
 iris = datasets.load_iris()
 X,y = iris.data, iris.target
 knn = neighbors.KNeighborsClassifier(n_neighbors=2) #k = 3,4,5,6
 knn.fit(X,y)
-predict = knn.predict([[3,5,4,2]])
-print(iris.target_names[knn.predict([[3,5,4,2]])])
+predict = knn.predict([[sLen,sWid,pLen,pWid]])
+st.write(iris.target_names[predict])