Create handler.py

handler.py

@@ -0,0 +1,53 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+class SentimentAnalysisHandler:
+    def __init__(self):
+        """Load base model and fine-tuned adapter."""
+        self.base_model_id = "unsloth/llama-3-8b-bnb-4bit"
+        self.adapter_model_id = "samiur-r/BanglishSentiment-Llama3-8B"
+
+        # Load tokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(self.base_model_id)
+
+        # Load base model
+        self.model = AutoModelForCausalLM.from_pretrained(
+            self.base_model_id,
+            device_map="auto",
+            torch_dtype=torch.bfloat16
+        )
+
+        # Attach LoRA adapter
+        self.model = PeftModel.from_pretrained(self.model, self.adapter_model_id)
+        self.model.eval()
+
+    def preprocess(self, input_text):
+        """Tokenize input text."""
+        inputs = self.tokenizer(input_text, return_tensors="pt").to("cuda")
+        return inputs
+
+    def inference(self, inputs):
+        """Perform model inference."""
+        with torch.no_grad():
+            output = self.model.generate(**inputs, max_new_tokens=256)
+        return output
+
+    def postprocess(self, output):
+        """Decode model output."""
+        sentiment = self.tokenizer.decode(output[0], skip_special_tokens=True)
+        return sentiment
+
+    def predict(self, input_text):
+        """Full prediction pipeline."""
+        inputs = self.preprocess(input_text)
+        output = self.inference(inputs)
+        return self.postprocess(output)
+
+# Create handler instance
+_model_handler = SentimentAnalysisHandler()
+
+def handle(inputs, context):
+    """Entry point for model API inference."""
+    text = inputs.get("text", "")
+    return {"prediction": _model_handler.predict(text)}