getans.py

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained("georgesung/llama2_7b_chat_uncensored")
model = AutoModelForCausalLM.from_pretrained("georgesung/llama2_7b_chat_uncensored")

def get_response(prompt, max_new_tokens=50):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_new_tokens=max_new_tokens, temperature=0.0001, do_sample=True)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)  # Use indexing instead of calling
    ans=response.toString()
    return ans