app.py

import gc

import streamlit as st
import torch
from rag import load_all, run_query


@st.cache_resource(
    show_spinner="Loading models and indices. This might take a while..."
)

def get_rag_qa() -> dict:
    gc.collect()
    torch.cuda.empty_cache()
    return load_all(
        embedder_path="Snowflake/snowflake-arctic-embed-l",
        context_file="data/bioasq_contexts.jsonl",
        index_file="data/bioasq_contexts__snowflake-arctic-embed-l__float32_hnsw.index",
        reader_path="meta-llama/Llama-3.2-1B-Instruct",
    )



left_column, cent_column, last_column = st.columns(3)
with cent_column:
    st.image("cover.webp", width=400)
st.title("Ask the BioASQ Database Anything!")

# Initialize the RagQA model, might be already cached.
_ = get_rag_qa()

# Run QA
st.subheader("Ask away:")
question = st.text_input("Ask away:", "", label_visibility="collapsed")
submit = st.button("Submit")


st.markdown(
    
    """
    > **For example, ask things like:**
    >
    > What is the Bartter syndrome?  
    > Which genes have been found to be associated with restless leg syndrome?  
    > Which diseases can be treated with Afamelanotide?
    ---
    """,
    unsafe_allow_html=False,
)


if submit:
    if not question.strip():
        st.error("Machine Learning still can't read minds. Please enter a question.")
    else:
        try:
            with st.spinner(
                "Combing through 3000+ documents from the BioASQ database..."
            ):
                rag_qa = get_rag_qa()
                retrieved_context_ids, sources, answer = run_query(question, **rag_qa)
                print(answer)
                print(retrieved_context_ids)
                print(sources)

            st.subheader("Answer:")
            st.write(answer)

            st.write("")

            with st.expander("Show Sources"):
                st.subheader("Sources:")
                for i, (context_id, source) in enumerate(
                    zip(retrieved_context_ids, sources)
                ):
                    st.markdown(f"**BioASQ Document ID:** {context_id}")
                    st.markdown(f"**Text:**")
                    st.write(source)
                    if i < len(sources) - 1:
                        st.markdown("---")

        except Exception as e:
            st.error(f"An error occurred: {e}")