import streamlit as st
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
from huggingface_hub import snapshot_download
import os
import threading

cwd = os.getcwd()
cachedir = cwd + '/cache'

# Check if the directory exists before creating it
if not os.path.exists(cachedir):
    os.mkdir(cachedir)

os.environ['HF_HOME'] = cachedir

local_folder = cachedir + "/model"

quantized_model_dir = "FPHam/Jackson_The_Formalizer_V2_13b_GPTQ"
snapshot_download(repo_id=quantized_model_dir, local_dir=local_folder, local_dir_use_symlinks=True)

model_basename = cachedir + "/model/Jackson2-4bit-128g-GPTQ"

use_strict = False
use_triton = False

tokenizer = AutoTokenizer.from_pretrained(local_folder, use_fast=False)

quantize_config = BaseQuantizeConfig(
    bits=4,
    group_size=128,
    desc_act=False
)

model = AutoGPTQForCausalLM.from_quantized(
    local_folder,
    use_safetensors=True,
    strict=use_strict,
    model_basename=model_basename,
    device="cuda:0",
    use_triton=use_triton,
    quantize_config=quantize_config
)

user_input = st.text_input("Input a phrase")

prompt_template = f'USER: {user_input}\nASSISTANT:'

# Generate output when the "Generate" button is pressed
if st.button("Generate the prompt"):
    inputs = tokenizer(prompt_template, return_tensors="pt")
    outputs = model.generate(
        input_ids=inputs.input_ids.to("cuda:0"),
        attention_mask=inputs.attention_mask.to("cuda:0"),
        max_length=512 + inputs.input_ids.size(-1),
        temperature=0.1,
        top_p=0.95,
        repetition_penalty=1.15
    )
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    st.text_area("Prompt", value=generated_text)