from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import streamlit as st

def gptq_model_options():
    return [
        "TheBloke/Llama-2-7B-Chat-GPTQ", 
        "TheBloke/Llama-2-13B-chat-GPTQ",
        "TheBloke/meditron-7B-GPTQ",
        "TheBloke/meditron-70B-GPTQ",
    ]

loaded_model = None
loaded_model_name = ""

def get_llm_response(model_name_or_path, temperature, do_sample, top_p, top_k, max_new_tokens, repetition_penalty, formatted_prompt):
    global loaded_model
    global loaded_model_name
    
    if loaded_model != model_name_or_path:
        
        loaded_model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                                device_map="auto",
                                                trust_remote_code=False,
                                                revision="main")
        loaded_model_name = model_name_or_path
        

    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
            
    print("Formatted prompt:")
    print(formatted_prompt)
    
    st.session_state["llm_messages"].append(formatted_prompt)
    
    #print("\n\n*** Generate:")
    #input_ids = tokenizer(formatted_prompt, return_tensors='pt').input_ids.cuda()
    #output = model.generate(inputs=input_ids, temperature=temperature, do_sample=do_sample, top_p=top_p, top_k=top_k, max_new_tokens=max_new_tokens)
    #print(tokenizer.decode(output[0], skip_special_tokens=True)) 

    print("*** Pipeline:")
    pipe = pipeline(
        "text-generation",
        model=loaded_model,
        tokenizer=tokenizer,
        max_new_tokens=max_new_tokens,
        do_sample=do_sample,
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        repetition_penalty=repetition_penalty,
        return_full_text=False
    )

    pipe_response = pipe(formatted_prompt)
    st.session_state["llm_messages"].append(pipe_response)
    print(pipe_response)
    return pipe_response[0]['generated_text']