Create app.py

app.py

@@ -0,0 +1,16 @@
+import streamlit as st
+
+with st.form(key='my_form'):
+  sLen = st.slider('sepal length (cm) ', 0.0, 10.0)
+  sWid = st.slider('sepal Width (cm) ', 0.0, 10.0)
+  pLen = st.slider('petal length (cm) ', 0.0, 10.0)
+  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]])])