Streamlit_iris/MLiris.py at main · prishuprograms/Streamlit_iris · GitHub

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# Loading req. modules...
import streamlit as st
import pandas as pd
from sklearn import datasets
from sklearn.neighbors import KNeighborsClassifier

st.write("""
# Simple Iris Flower Prediction App

This app predicts tye of the flower""")

st.sidebar.header('User Input Parameters')

def user_input_features():
    sepal_length = st.sidebar.slider('sepal_length',4.3,7.9,5.4)
    sepal_width = st.sidebar.slider('sepal_width',2.0,4.4,3.4)
    petal_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' : petal_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)

#  Laoding Dataset
iris = datasets.load_iris()

#  Printing data"s discription and features
# print(iris.DESCR)
features = iris.data
labels = iris.target
# print(features[0], labels[0])

#  Training Classifier
clf = KNeighborsClassifier()
clf.fit(features, labels)

# Predicition part
prediction = clf.predict(df)
prediction_probs = clf.predict_proba(df)

st.subheader('Class labels and their corresponding index number')
st.write(iris.target_names)

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

st.subheader('Prediction Probability')
st.write(prediction_probs)