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import streamlit as st
import os
import logging
from transformers import AutoModelForCausalLM, AutoTokenizer
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
st.title("Meta LLaMA Text Generation")
@st.cache_resource
def load_model():
model_name = "meta-llama/Meta-Llama-3-8B"
access_token = os.getenv('hf')
if not access_token:
st.error("Hugging Face access token is not set. Please set the environment variable 'hf'.")
return None, None
logger.info("Loading tokenizer and model...")
tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=access_token)
model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=access_token)
return tokenizer, model
tokenizer, model = load_model()
if tokenizer is not None and model is not None:
prompt = st.text_input("Enter a prompt:", "Once upon a time")
if st.button("Generate Text"):
try:
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
st.write(generated_text)
except Exception as e:
st.error(f"An error occurred: {e}")
logger.error(f"An error occurred during text generation: {e}")
else:
st.error("Failed to load the model. Check the logs for more details.")
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