Create app.py

app.py

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+from fastapi import FastAPI
+from sklearn.datasets import load_iris
+from sklearn.tree import DecisionTreeClassifier
+import numpy as np
+
+app = FastAPI()
+
+iris = load_iris()
+model = DecisionTreeClassifier(random_state=42)
+model.fit(iris.data, iris.target)
+class_names = ["setosa", "versicolor", "virginica"]
+
+@app.get("/health")
+async def health():
+    return {"status": "ok"}
+
+@app.get("/predict")
+async def predict(sl: float, sw: float, pl: float, pw: float):
+    features = np.array([[sl, sw, pl, pw]])
+    pred = int(model.predict(features)[0])
+    return {"prediction": pred, "class_name": class_names[pred]}