Fix merge conflicts - keep correct iris classifier

app.py

@@ -1,16 +1,20 @@
+<<<<<<< HEAD
 from fastapi import FastAPI
 from sklearn.datasets import load_iris
 from sklearn.tree import DecisionTreeClassifier
 import numpy as np
 
 app = FastAPI()
+
+# Train model at startup
 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"}
+async def health():
+    return {"status": "ok"}
 
 @app.get("/predict")
 async def predict(sl: float, sw: float, pl: float, pw: float):

requirements.txt

@@ -1,4 +1,4 @@
-fastapi 
-uvicorn 
-scikit-learn 
+fastapi
+uvicorn
+scikit-learn
 numpy