import streamlit as st
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
import torch
import subprocess

# Function to get memory info
def get_gpu_memory():
    try:
        result = subprocess.check_output(["nvidia-smi", "--query-gpu=memory.free,memory.total", "--format=csv,nounits,noheader"], text=True)
        memory_info = [x.split(',') for x in result.strip().split('\n')]
        memory_info = [{"free": int(x[0].strip()), "total": int(x[1].strip())} for x in memory_info]
    except FileNotFoundError:
        memory_info = [{"free": "N/A", "total": "N/A"}]
    return memory_info

# Display GPU memory information
gpu_memory = get_gpu_memory()
st.write(f"GPU Memory Info: {gpu_memory}")

# Define pretrained model directory
pretrained_model_dir = "FPHam/Jackson_The_Formalizer_V2_13b_GPTQ"

# Check if CUDA is available and get the device
device = "cuda:0" if torch.cuda.is_available() else "cpu"

# Before allocating or loading the model, clear up memory if CUDA is available
if device == "cuda:0":
    torch.cuda.empty_cache()

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True)

# Attempt to load the model, catch any OOM errors
try:
    model = AutoGPTQForCausalLM.from_quantized(
        pretrained_model_dir,
        model_basename="Jackson2-4bit-128g-GPTQ",
        use_safetensors=True,
        device=device,
        max_memory={0: "10GIB"}
    )
except RuntimeError as e:
    if 'CUDA out of memory' in str(e):
        st.error("CUDA out of memory. Try reducing the model size or input length.")
        st.stop()
    else:
        raise e

# User input for the model
user_input = st.text_input("Input a phrase")

# Generate button
if st.button("Generate the prompt"):
    try:
        prompt_template = f'USER: {user_input}\nASSISTANT:'
        inputs = tokenizer(prompt_template, return_tensors='pt', max_length=512, truncation=True, padding='max_length')
        output = model.generate(**inputs)
        st.markdown(f"**Generated Text:**\n{tokenizer.decode(output[0])}")
    except RuntimeError as e:
        if 'CUDA out of memory' in str(e):
            st.error("CUDA out of memory during generation. Try reducing the input length.")
        else:
            raise e