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(cm)", 0.0, 10.0, 0.1)
        sepal_width = st.slider("Sepal_width(cm)", 0.0, 10.0, 0.1)
        petal_length = st.slider("Petal_length(cm)", 0.0, 10.0, 0.1)
        petal_width = st.slider("Petal_width(cm)", 0.0, 10.0, 0.1)

        # 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] == 0:
                result = "Iris-setosa"
            elif result[0] == 1:
                result = "Iris-versicolor"
            else:
                result = "Iris-virginica"
            st.success(f"Your predicted class is {result}")
            
if __name__ == "__main__":
    main()