Create handler.py

handler.py

@@ -0,0 +1,33 @@
+from typing import Dict, Any
+import numpy as np
+import joblib
+
+class EndpointHandler():
+    def __init__(self, path: str = ""):
+        """
+        Initialize the model and encoder when the endpoint starts.
+        """
+        self.model = joblib.load(f"{path}/soil.pkl")
+        self.label_encoder = joblib.load(f"{path}/label_encoder.pkl")
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Perform prediction using the trained model.
+        Expects input data in the format:
+            {
+                "inputs": [N, P, K, temperature, humidity, ph, rainfall]
+            }
+        Returns:
+            {
+                "crop": predicted_crop_name
+            }
+        """
+        inputs = data.get("inputs")
+        if inputs is None:
+            return {"error": "No input data provided."}
+
+        inputs = np.array(inputs).reshape(1, -1)
+        prediction = self.model.predict(inputs)
+        crop = self.label_encoder.inverse_transform(prediction)
+
+        return {"crop": crop[0]}