app.py

from flask import Flask, request, jsonify, render_template
import pickle, numpy as np, os

app = Flask(__name__)

if not os.path.exists("model.pkl"):
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.datasets import load_iris
    from sklearn.model_selection import train_test_split
    iris = load_iris()
    X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)
    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(X_train, y_train)
    with open("model.pkl", "wb") as f:
        pickle.dump(model, f)
    print("Model trained and saved!")
else:
    with open("model.pkl", "rb") as f:
        model = pickle.load(f)
    print("Model loaded!")

@app.route("/")
def home():
    return render_template("index.html")

@app.route("/predict", methods=["POST"])
def predict():
    try:
        data = request.get_json()
        features = np.array([[data["sepal_length"], data["sepal_width"], data["petal_length"], data["petal_width"]]])
        pred = model.predict(features)[0]
        probs = model.predict_proba(features)[0]
        conf = round(max(probs) * 100, 2)
        species = {0: "Setosa", 1: "Versicolor", 2: "Virginica"}[pred]
        return jsonify({"species": species, "confidence": conf, "prediction": int(pred)})
    except Exception as e:
        return jsonify({"error": str(e)}), 400

@app.route("/health")
def health():
    return jsonify({"status": "healthy"})

if __name__ == "__main__":
    port = int(os.environ.get("PORT", 7860))
    app.run(host="0.0.0.0", port=port, debug=False)