app.py

import streamlit as st
import pandas as pd
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier

st.title('simple iris flower prediction app')

st.sidebar.header('user input parameters')

iris = datasets.load_iris()
X = iris.data
y = iris.target

clf = RandomForestClassifier()
clf.fit(X,y)

def user_input_features():
    sepal_lenght = st.sidebar.slider('sepal lenght',3.5,8.9,5.4)
    # valeur de depart : 3.5
    # valeur de fin : 8.9
    # valeur de positionement initial : 5.4
    sepal_width = st.sidebar.slider('sepal width',2.0,4.4,3.4)
    petal_lenght = st.sidebar.slider('petal lenght',1.0,8.9,1.3)
    petal_width = st.sidebar.slider('petal width',3.5,8.9,5.4)
    data = {'sepal_length':sepal_lenght,
            'sepal_width':sepal_width,
            'petal_lenght':petal_lenght,
            'petal_width':petal_width}
    features = pd.DataFrame(data,index = [0])
    return(features)

df = user_input_features()
st.subheader('user input features')
st.write(df)

prediction = clf.predict(df)
prediction_proba = clf.predict_proba(df)

st.subheader('class labels and corresponding indexes')
st.write(iris.target_names)

st.subheader('prediction')
st.write(iris.target_names[prediction])

st.subheader('prediction probability')
st.write(prediction_proba)