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

# --- Train model on startup ---
@st.cache_resource
def train_model():
    iris = load_iris()
    X = pd.DataFrame(iris.data, columns=iris.feature_names)
    y = iris.target

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(X_train, y_train)
    return model, iris

model, iris = train_model()

# --- Streamlit UI ---
st.title("🌸 Iris Flower Classifier (MLOps Demo)")
st.write("This app trains a RandomForestClassifier on the Iris dataset and predicts the flower type.")

# User input
sepal_length = st.slider("Sepal length (cm)", 4.0, 8.0, 5.5)
sepal_width  = st.slider("Sepal width (cm)", 2.0, 4.5, 3.0)
petal_length = st.slider("Petal length (cm)", 1.0, 7.0, 4.0)
petal_width  = st.slider("Petal width (cm)", 0.1, 2.5, 1.0)

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

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

st.subheader("Prediction")
st.write(f"🌼 The predicted Iris species is: **{predicted_class}**")