iris2.py

#import library needed
import pandas as pd
import streamlit as st 
import pickle
import numpy as np

# Load the model 
with open('iris_model.pkl', 'rb') as f:
    model = pickle.load(f)

# adding images
species = ['setosa', 'versicolor', 'virginica']
image = ['setosa.jpg', 'versicolor.jpg', 'virginica.jpg'] 

# title and explanation
st.title("Iris Flower Classification")
st.write("This app correctly classifies iris flower among 3 possible species")

# Creating Sidebar for inputs
st.sidebar.title("Inputs")
sepal_length = st.sidebar.slider("sepal length (cm)", 4.3, 7.9, 5.0)
sepal_width = st.sidebar.slider("sepal width (cm)", 2.0, 4.4, 3.6)
petal_length = st.sidebar.slider("petal length (cm)", 1.0, 6.9, 1.4)
petal_width = st.sidebar.slider("petal width (cm)", 0.1, 2.5, 0.2)

# Button to trigger prediction
if st.button("Predict"):
    # Getting Prediction from model
    inp = np.array([sepal_length, sepal_width, petal_length, petal_width])
    inp = np.expand_dims(inp, axis=0)
    prediction = model.predict(inp)

    # Show Results when the button is clicked
    st.write('''
    ## Results
    Following is the probability of each class
    ''')

    df = pd.DataFrame(prediction, index=['result'], columns=species)
    st.dataframe(df)
    result = species[np.argmax(prediction)]
    st.write("**This flower belongs to " + result + " class**")
    st.image(image[np.argmax(prediction)])