model.py

import numpy as np
import pickle
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier

# Load the dataset
iris = load_iris()
X, y = iris.data, iris.target

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Standardize the data
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Save the scaler
with open('scaler.pkl', 'wb') as f:
    pickle.dump(scaler, f)

# Train the RandomForestClassifier
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Save the model
with open('iris_classifier_model.pkl', 'wb') as f:
    pickle.dump(model, f)