model.py

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

class ModelManager:
    def __init__(self, model_name="BanglaLLM/BanglaLLama-3.2-3b-unlop-culturax-base-v0.0.3"):
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16 if self.device == "cuda" else torch.float32)
        self.model = self.model.to(self.device)

    def generate_response(self, prompt, max_length, temperature, top_k, top_p):
        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
        outputs = self.model.generate(
            inputs["input_ids"],
            max_length=max_length,
            temperature=temperature,
            top_k=top_k,
            top_p=top_p,
            do_sample=True
        )
        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)