app.py

import pandas as pd
import streamlit as st
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
from joblib import dump, load
from pathlib import Path

st.title("Iris Inference")
cols = st.columns(2)
with cols[0]:
    st.header('Input Features')
    st.number_input('sepal length in cm', key = 'sl')
    st.number_input('sepal width in cm', key = 'sw')
    st.number_input('petal length in cm', key = 'pl')
    st.number_input('pepal length in cm', key = 'pw')


    data = pd.DataFrame.from_dict({'sepal length (cm)': [float(st.session_state.sl)],
                         'sepal width (cm)': [float(st.session_state.sw)],
                         'petal length (cm)': [float(st.session_state.pl)],
                         'petal width (cm)': [float(st.session_state.pw)]})
    st.button('Submit')
def load_model():
    path_model = Path(Path.cwd(), 'weights/rf.joblib')
    model = load(str(path_model))
    return model
model = load_model()

@st.cache
def predict_iris(data):
    return model.predict(data)[0]
with cols[1]:
    st.markdown("## Predictors: ")
    st.write("Class: ", predict_iris(data))