app.py

import streamlit as st
from transformers import pipeline

# Streamlit app
st.title("Finance Question Answering with Llama 3.1")

st.write("Enter a financial question and get an answer from the finetuned Llama 3.1 model.")

# Model loading
model_path = "deadbeee/finQA-llama3.1-finetuned-LoRA"
@st.cache_resource
def load_pipeline():
    try:
        return pipeline("text-generation", model=model_path)
    except Exception as e:
        st.error(f"Error loading model: {str(e)}")
        return None

pipe = load_pipeline()

if pipe is None:
    st.stop()

# Text input
user_input = st.text_input("Your question:")

if user_input:
    # Prepare input for the pipeline
    messages = [
        {"role": "user", "content": user_input},
    ]
    
    # Generate response
    response = pipe(messages, max_new_tokens=512, do_sample=True, temperature=0.7)
    
    # Display the result
    st.write("Model response:")
    st.write(response[0]['generated_text'])

# Run the Streamlit app
if __name__ == "__main__":
    st.write("Running the Streamlit app.")