'''
import streamlit as st
from transformers import pipeline

pipe = pipeline('sentiment-analysis')
text = ('enter some text:')

if text:
    out = pipe(text)
    st.json(out)
'''

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "abacusai/Smaug-72B-v0.1"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Encode some input text
input_text = "Who are you?"
input_ids = tokenizer.encode(input_text, return_tensors='pt')

# Generate text using the model
output = model.generate(input_ids, max_length=50)

# Decode and print the output
print("Decoded output: " + tokenizer.decode(output[0], skip_special_tokens=True))