| 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) | |
| 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') | |
| iris = datasets. load_iris() | |
| X, y = iris.data, iris.target | |
| knn = neighbors.KNeighborsClassifier(n_neighbors=3) | |
| knn.fit(X, y) | |
| predict = knn.predict([[sLen,sWid,pLen,pWid]]) | |
| st.text(iris.target_names[predict]) |