handler.py

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer globally
MODEL_NAME = "abhijsrwala/lora_model"

def load_model():
    # Load the model and tokenizer
    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    return model, tokenizer

# Load model once to avoid reloading on every request
model, tokenizer = load_model()

def handle_request(input_data):
    """
    Handles inference requests.
    Args:
        input_data (str): The input text prompt.
    Returns:
        str: The model's response.
    """
    # Tokenize the input text
    inputs = tokenizer.encode(input_data, return_tensors="pt")

    # Generate text
    outputs = model.generate(inputs, max_length=200, num_return_sequences=1)
    
    # Decode the output
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response