app.py

import streamlit as st
import pickle
import numpy as np

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

st.title("Iris Flower Prediction App")

st.write("""
Enter the **Iris flower measurements** below to predict the species. 
""")

# Create input widgets for each feature
sepal_length = st.number_input("Sepal length (cm)", min_value=0.0, max_value=10.0, value=5.0)
sepal_width  = st.number_input("Sepal width (cm)",  min_value=0.0, max_value=10.0, value=3.0)
petal_length = st.number_input("Petal length (cm)", min_value=0.0, max_value=10.0, value=4.0)
petal_width  = st.number_input("Petal width (cm)",  min_value=0.0, max_value=10.0, value=1.0)

if st.button("Predict"):
    # 1. Convert input to a numpy array
    input_data = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
    
    # 2. Make prediction
    prediction = model.predict(input_data)
    
    # 3. Map numeric prediction to Iris class name
    #  (Iris dataset classes -> 0: setosa, 1: versicolor, 2: virginica)
    class_names = ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
    predicted_class = class_names[prediction[0]]
    
    # 4. Display the result
    st.write(f"**Predicted Iris Species:** {predicted_class}")