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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}")
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