flask_app.py

from flask import Flask, request, jsonify
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

app = Flask(__name__)

iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    prediction = model.predict([data['features']])
    return jsonify({'prediction': int(prediction[0])})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)