from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from peft import PeftModel, PeftConfig

import gc

gc.collect()

model_name = "MoodChartAI/basicmood"
adapters_name = ""


torch.cuda.empty_cache()


os.system("sudo swapoff -a; swapon -a")

print(f"Starting to load the model {model_name} into memory")

m = AutoModelForCausalLM.from_pretrained(
    model_name,
    #load_in_4bit=True,
).to(device='cpu:7')

print(f"Loading the adapters from {adapters_name}")
m = PeftModel.from_pretrained(m, adapters_name)


tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B", trust_remote_code=True)



while True:
    mood_input = input("Mood: ")

    inputs = tokenizer("Prompt: %s Completions: You're feeling"%mood_input, return_tensors="pt", return_attention_mask=True)
    inputs.to(device='cpu:8')
    outputs = m.generate(**inputs, max_length=12)

    print(tokenizer.batch_decode(outputs)[0])