St_app.py

import streamlit as st
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier

@st.cache_data
def load_data():
    iris = load_iris()
    df = pd.DataFrame(iris.data, columns = iris.feature_names)
    df['species'] = iris.target
    return df, iris.target_names

df, target_names= load_data()

model = RandomForestClassifier()
model.fit(df.iloc[:,:-1], df["species"])

st.sidebar.title("Input Feature")
sepal_length=st.sidebar.slider("Sepal length", float(df["sepal length (cm)"].min()), float(df["sepal length (cm)"].max()))
sepal_width=st.sidebar.slider("Sepal width", float(df["sepal width (cm)"].min()), float(df["sepal width (cm)"].max()))
petal_length=st.sidebar.slider("Petal length", float(df["petal length (cm)"].min()), float(df["petal length (cm)"].max()))
petal_width=st.sidebar.slider("Petal width", float(df["petal width (cm)"].min()), float(df["petal width (cm)"].max()))

input_data = [[sepal_length, sepal_width, petal_length, petal_width]]

## Prediction
prediction = model.predict(input_data)
predicted_species = target_names[prediction[0]]

st.write("Prediction")
st.write(f"The predicted species is: {predicted_species}")