iris_app.py

import streamlit as st
import pandas as pd
import joblib
from PIL import Image

# Loading Our final trained Knn model
modeltri = open("Knn_Classifier.pkl", "rb")
knn_clf = joblib.load(model)


st.title("Iris flower species Classification App")

# Loading images

setosa = Image.open("setosa.png")
versicolor = Image.open("versicolor.png")
virginica = Image.open("virginica.png")

st.sidebar.title("Features")

# Intializing
parameter_list = [
    "Sepal length (cm)",
    "Sepal Width (cm)",
    "Petal length (cm)",
    "Petal Width (cm)",
]
parameter_input_values = []
parameter_default_values = ["5.2", "3.2", "4.2", "1.2"]

values = []

# Display
for parameter, parameter_df in zip(parameter_list, parameter_default_values):

    values = st.sidebar.slider(
        label=parameter,
        key=parameter,
        value=float(parameter_df),
        min_value=0.0,
        max_value=8.0,
        step=0.1,
    )
    parameter_input_values.append(values)

input_variables = pd.DataFrame(
    [parameter_input_values], columns=parameter_list, dtype=float
)
st.write("\n\n")

if st.button("Click Here to Classify"):
    prediction = knn_clf.predict(input_variables)
    st.image(setosa) if prediction == 0 else st.image(
        versicolor
    ) if prediction == 1 else st.image(virginica)