app.py

import streamlit as st
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM

# Load the model and tokenizer
@st.cache_resource
def load_model():
    tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")
    model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B")
    return pipeline("text-generation", model=model, tokenizer=tokenizer)

# Load the text generation pipeline
text_gen_pipeline = load_model()

# Streamlit app interface
st.title("Text Generation with Meta-Llama-3-8B")
st.write("Enter some text and click the button to generate a continuation.")

# User input
user_input = st.text_area("Input Text", "Once upon a time")

# Generate text on button click
if st.button("Generate Text"):
    with st.spinner("Generating..."):
        generated_text = text_gen_pipeline(user_input, max_length=100, num_return_sequences=1)
        st.write(generated_text[0]['generated_text'])