app.py

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import joblib

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

# Split dataset into training set and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

# Create a Gaussian Classifier
clf = RandomForestClassifier()

# Train the model using the training sets
clf.fit(X_train, y_train)

# Predict the response for test dataset
y_pred = clf.predict(X_test)

# Model Accuracy, how often is the classifier correct?
print(f"Accuracy: {accuracy_score(y_test, y_pred)}")

# Save the trained model
joblib.dump(clf, 'iris_model.pkl')
print("Model saved!")