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import streamlit as st
import pandas as pd
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
st.title('Iris Flower Prediction App')
iris = datasets.load_iris()
X = iris.data
y = iris.target
clf = RandomForestClassifier()
clf.fit(X,y)
st.sidebar.header('User Input Parameters')
def user_input_features():
sepal_length = st.sidebar.slider('Sepal length',4.3,8.0,5.0)
sepal_width = st.sidebar.slider('Sepal length',2.0,4.4,3.4)
pepal_length = st.sidebar.slider('Sepal length',1.0,6.9,1.3)
petal_width= st.sidebar.slider('Sepal length',0.1,2.5,0.2)
data ={'sepal_length':sepal_length,
'sepal_width':sepal_width,
'petal_length':pepal_length,
'petal_width':petal_width}
features = pd.DataFrame(data,index=[0])
return features
df = user_input_features()
st.subheader('User Input Parameters')
st.write(df)
prediction = clf.predict(df)
prediction_proba = clf.predict_proba(df)
st.subheader('Class names and corresponding numbers')
st.write(iris.target_names)
st.subheader('Prediction')
st.write(iris.target_names[prediction])
st.subheader('Prediction Probability')
st.write(prediction_proba ) |