Upload handler.py

handler.py

@@ -0,0 +1,62 @@
+import numpy as np
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from typing import Dict, List, Any
+
+
+def middle_truncate(tokenized_ids, max_length, tokenizer):
+    if len(tokenized_ids) <= max_length:
+        return tokenized_ids + [tokenizer.pad_token_id] * (
+            max_length - len(tokenized_ids)
+        )
+
+    excess_length = len(tokenized_ids) - max_length
+    left_remove = excess_length // 2
+    right_remove = excess_length - left_remove
+
+    return tokenized_ids[left_remove:-right_remove]
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        self.model = AutoModelForSequenceClassification.from_pretrained(path)
+        self.id2label = {
+            i: label for i, label in enumerate(self.model.config.id2label.values())
+        }
+        self.MAX_LENGTH = 512  # or any other max length you prefer
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        # get inputs
+        inputs = data.pop("inputs", data)
+
+        encodings = self.tokenizer(
+            inputs, padding=False, truncation=False, max_length=514
+        )
+        truncated_input_ids = middle_truncate(
+            encodings["input_ids"], 514, self.tokenizer
+        )
+        truncated_input_ids_array = np.array(truncated_input_ids)
+        attention_masks = (truncated_input_ids_array != 1).astype(int)
+        truncated_encodings = {
+            "input_ids": truncated_input_ids,
+            "attention_mask": attention_masks,
+        }
+
+        outputs = self.model(**truncated_encodings)
+
+        # transform logits to probabilities and apply threshold
+        probs = 1 / (1 + np.exp(-outputs.logits.detach().cpu().numpy()))
+        predictions = (probs >= 0.5).astype(float)
+
+        # transform predicted id's into actual label names
+        predicted_labels = [
+            self.id2label[idx]
+            for idx, label in enumerate(predictions[0])
+            if label == 1.0
+        ]
+
+        # You can return it in any format you like, here's an example:
+        return [
+            {"label": label, "score": prob}
+            for label, prob in zip(predicted_labels, probs[0])
+        ]