Create handler.py

handler.py

@@ -0,0 +1,26 @@
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import torch
+
+class ModelHandler:
+    def __init__(self):
+        self.model_path = "asritha22bce/bart-positive-tone"  # Change if needed
+        self.model = AutoModelForSeq2SeqLM.from_pretrained(self.model_path)
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_path)
+
+    def preprocess(self, text):
+        return self.tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+    def inference(self, inputs):
+        with torch.no_grad():
+            output_ids = self.model.generate(**inputs, max_length=50)
+        return self.tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    def postprocess(self, output):
+        return {"positive_headline": output}
+
+handler = ModelHandler()
+
+def handle_request(text):
+    inputs = handler.preprocess(text)
+    output = handler.inference(inputs)
+    return handler.postprocess(output)