import joblib
import pandas as pd
import streamlit as st

from PIL import Image
image = Image.open('iris.png')
image_setosa = Image.open('Irissetosa.jpg')
image_virginica = Image.open('iris_virginica.jpg')
image_versicolor = Image.open('versicolor.jpg')

st.image(image, caption='Iris')
model = joblib.load('model_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"
                st.image(image_setosa, caption='Iris Setosa', width = 350)
            elif result[0] == 1:
                result = "Iris-versicolor"
                st.image(image_versicolor, caption='Iris Versicolor', width = 350)
            else:
                result = "Iris-virginica"
                st.image(image_virginica, caption='Iris Virginica', width = 350)
            st.write(f"Your predicted class is {result}")
            
                
if __name__ == '__main__':
    main()