app.py

import streamlit as st
import pandas as pd
import numpy as np
import joblib

def predict(data):
    clf = joblib.load("rf_model.sav")
    return clf.predict(data)

# Function to map classes to images
def class_to_image(class_name):
    if class_name == "setosa":
        return "images/setosa.jpg"  # Replace with the actual path to your setosa image
    elif class_name == "versicolor":
        return "images/versicolor.jpg"  # Replace with the actual path to your versicolor image
    elif class_name == "virginica":
        return "images/virginica.jpg"  # Replace with the actual path to your virginica image

st.title('Classifying Iris Flowers')
st.markdown('Model to classify iris flowers into \
     (setosa, versicolor, virginica) based on their sepal/petal \
    and length/width.')

st.header("Plant Features")
col1, col2 = st.columns(2)

with col1:
    st.text("Sepal characteristics")
    sepal_l = st.slider('Sepal length (cm)', 1.0, 8.0, 0.5)
    sepal_w = st.slider('Sepal width (cm)', 2.0, 4.4, 0.5)

with col2:
    st.text("Petal characteristics")
    petal_l = st.slider('Petal length (cm)', 1.0, 7.0, 0.5)
    petal_w = st.slider('Petal width (cm)', 0.1, 2.5, 0.5)

st.text('')
if st.button("Predict type of Iris"):
    result = predict(
        np.array([[sepal_l, sepal_w, petal_l, petal_w]]))
    st.text(result[0])

    # Display the image/icon corresponding to the predicted class
    image_path = class_to_image(result[0].split("-")[1])
    st.image(image_path, use_column_width=True)


st.text('')