app.py

import streamlit as st # type: ignore
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 width' ,2.0,4.4,3.4)
    pepal_length = st.sidebar.slider('Petal length' ,1.0,6.9,1.3)
    petal_width = st.sidebar.slider('Petal width' ,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 )