Create app.py

app.py

@@ -0,0 +1,44 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+import joblib
+import numpy as np
+from sklearn.datasets import load_iris
+
+# Load the trained model
+model = joblib.load("iris_model.pkl")
+
+app = FastAPI()
+
+
+class IrisInput(BaseModel):
+    sepal_length: float
+    sepal_width: float
+    petal_length: float
+    petal_width: float
+
+
+class IrisPrediction(BaseModel):
+    predicted_class: int
+    predicted_class_name: str
+
+
+@app.post("/predict", response_model=IrisPrediction)
+def predict(data: IrisInput):
+    # Convert the input data to a numpy array
+    input_data = np.array(
+        [[data.sepal_length, data.sepal_width, data.petal_length, data.petal_width]]
+    )
+
+    # Make a prediction
+    predicted_class = model.predict(input_data)[0]
+    predicted_class_name = load_iris().target_names[predicted_class]
+
+    return IrisPrediction(
+        predicted_class=predicted_class, predicted_class_name=predicted_class_name
+    )
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="127.0.0.1", port=8000)