handler.py

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_NAME = "lumolabs-ai/Lumo-8B-Instruct"

class ModelHandler:
    def __init__(self):
        """Load the model and tokenizer"""
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        print(f"Loading model on {self.device}...")
        
        self.tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
        self.model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).to(self.device)
        print("Model loaded successfully.")

    def __call__(self, inputs):
        """Handle inference requests"""
        text = inputs.get("inputs", "")
        if not text:
            return {"error": "No input provided"}
        
        # Tokenize input
        input_tokens = self.tokenizer(text, return_tensors="pt").to(self.device)

        # Generate output
        with torch.no_grad():
            output_tokens = self.model.generate(**input_tokens, max_length=200)

        # Decode output
        response = self.tokenizer.decode(output_tokens[0], skip_special_tokens=True)
        return {"response": response}