app.py

import joblib
import pandas as pd
import streamlit as st


model = joblib.load('model_XG.joblib')
unique_values = joblib.load('unique_values_XG.joblib')
    
def main():
    st.title("Iris's Class")

    with st.form("questionaire"):
        sepal_length = st.slider("Sepal_length", min_value=0, max_value=6)
        sepal_width = st.slider("Sepal_width", min_value=0, max_value=6)
        petal_length = st.slider("Petal_length", min_value=0, max_value=6)
        petal_width = st.slider("Petal_width", min_value=0, max_value=6)

        # clicked==True only when the button is clicked
        clicked = st.form_submit_button("Predict class")
        if clicked:
            result=model.predict(pd.DataFrame({"sepal_length": [sepal_length],
                                               "sepal_width": [sepal_width],
                                               "petal_length": [petal_length],
                                               "petal_width": [petal_width]}))
            # Show prediction
            if result[0] == 'Iris-setosa':
                st.write("Iris-setosa")
            elif result[0] == 'Iris-versicolor':
                st.write("Iris-versicolor")
            elif result[0] == 'Iris-virginica':
                st.write("Iris-virginica")
            
            st.success("Your predicted class is"+result[0])
if __name__ == "__main__":
    main()