handler.py

import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

class ModelHandler:
    def __init__(self):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model = AutoModelForSeq2SeqLM.from_pretrained("shaheerzk/text_to_sql")
        self.tokenizer = AutoTokenizer.from_pretrained("shaheerzk/text_to_sql")
        self.model.to(self.device)

    def handle(self, inputs):
        # Preprocess input
        text = inputs.get("text", "")
        inputs = self.tokenizer(text, return_tensors="pt").to(self.device)

        # Inference
        with torch.no_grad():
            outputs = self.model.generate(**inputs)

        # Post-process output
        generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        return {"generated_text": generated_text}