Create handler.py

handler.py

@@ -0,0 +1,44 @@
+from transformers import pipeline
+import torch
+from typing import Dict, Any
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        device = 0 if torch.cuda.is_available() else -1
+        torch_d_type = torch.float16 if torch.cuda.is_available() else torch.float32
+
+        self.classifier = pipeline(
+            task="text-classification",
+            model="abullard1/albert-v2-steam-review-constructiveness-classifier",
+            tokenizer="albert-base-v2",
+            device=device,
+            top_k=None,
+            truncation=True,
+            max_length=512,
+            torch_dtype=torch_d_type
+        )
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        input_text = data.get("inputs", "")
+
+        results = self.classifier(input_text)
+
+        label_1, score_1 = results[0][0]["label"], results[0][0]["score"]
+        label_2, score_2 = results[0][1]["label"], results[0][1]["score"]
+
+        return {
+            "label_1": label_1,
+            "score_1": score_1,
+            "label_2": label_2,
+            "score_2": score_2,
+            "prediction_text": self.format_prediction_text(label_1, score_1, label_2, score_2)
+        }
+
+    def format_prediction_text(self, label_1, score_1, label_2, score_2) -> str:
+        def label_to_constructiveness(label):
+            return "Constructive" if label == "LABEL_1" else "Not Constructive"
+        
+        if score_1 >= score_2:
+            return f"{label_to_constructiveness(label_1)} with a score of {score_1:.2f}. 👍🏻"
+        else:
+            return f"{label_to_constructiveness(label_2)} with a score of {score_2:.2f}. 👎🏻"