Create app.py

app.py

@@ -0,0 +1,30 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+import joblib
+import numpy as np
+
+app = FastAPI(title="Iris Classification API")
+
+model = joblib.load("model.joblib")
+labels = ["setosa", "versicolor", "virginica"]
+
+class IrisInput(BaseModel):
+    sepal_length: float
+    sepal_width: float
+    petal_length: float
+    petal_width: float
+
+@app.get("/")
+def home():
+    return {"status": "Iris API is running"}
+
+@app.post("/predict")
+def predict(data: IrisInput):
+    X = np.array([[data.sepal_length, data.sepal_width,
+                   data.petal_length, data.petal_width]])
+    probs = model.predict_proba(X)[0]
+    idx = probs.argmax()
+    return {
+        "prediction": labels[idx],
+        "confidence": float(probs[idx])
+    }