app.py

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

# Load the model and tokenizer from Hugging Face
@st.cache_resource
def load_model():
    model_name = "meta-llama/Meta-Llama-3.1-8B-Instruct"  # Replace with your model name
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)
    return model, tokenizer

model, tokenizer = load_model()

# Create the pipeline for text generation
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

# Streamlit app title
st.title("Question Answering with Hugging Face Model")

# User input
question = st.text_input("Enter your question:")

# Button to generate the answer
if st.button("Generate Answer"):
    if question:
        prompt = f"Question: {question}\nAnswer: Let's think step by step."
        result = generator(prompt, max_length=100, do_sample=True, top_k=10)
        st.text_area("Generated Answer:", value=result[0]['generated_text'], height=200)
    else:
        st.warning("Please enter a question.")