Delete handler.py

handler.py

@@ -1,44 +0,0 @@
-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}. 👎🏻"