Spaces:
Sleeping
Sleeping
| # app.py — add "HF Transformers (Space/CPU)" backend option | |
| import os | |
| import streamlit as st | |
| from rag import CardioRAG, LLMBackend | |
| st.set_page_config(page_title="Cardiology Paper Explainer", page_icon="🫀", layout="wide") | |
| st.title("🫀 Cardiology Paper Explainer") | |
| st.caption("Local RAG to help researchers understand cross-discipline cardiovascular papers. Educational use only.") | |
| rag = CardioRAG() | |
| with st.sidebar: | |
| st.header("Settings") | |
| options = ["HF Transformers (Space/CPU)", "llama.cpp (MODEL_PATH)", "LM Studio (localhost)"] | |
| backend = st.selectbox("LLM backend", options, index=0) | |
| top_k = st.slider("Top-k chunks", 2, 6, 3) | |
| # Reader selector | |
| reading_level = st.selectbox("Reader", ["Clinician", "Data scientist"]) | |
| # Filter by a single paper (filename) | |
| sources = ["All"] + rag.list_sources() | |
| src_choice = st.selectbox("Filter to paper", sources, index=0) | |
| src_filter = None if src_choice == "All" else src_choice | |
| show_chunks = st.checkbox("Show retrieved snippets", value=True) | |
| # (Optional) choose small CPU model id for Spaces | |
| default_model = os.getenv("HF_LOCAL_MODEL", "google/gemma-2-2b-it") | |
| hf_model = st.text_input("HF model (CPU)", value=default_model, help="e.g., google/gemma-2-2b-it or microsoft/phi-3-mini-4k-instruct") | |
| os.environ["HF_LOCAL_MODEL"] = hf_model | |
| if st.button("Rebuild Index"): | |
| rag.rebuild_index() | |
| st.success("Vector store re-ingested.") | |
| query = st.text_area( | |
| "Ask a question", | |
| height=110, | |
| placeholder="e.g., PICO and clinically relevant endpoints (≤50 words).", | |
| ) | |
| if st.button("Explain Paper", type="primary") and query: | |
| with st.spinner("Retrieving and generating..."): | |
| if backend.startswith("HF Transformers"): | |
| llm = LLMBackend.from_hf_local(temperature=0.2) | |
| elif backend.startswith("llama.cpp"): | |
| model_path = os.getenv("MODEL_PATH") | |
| if not model_path: | |
| st.error("MODEL_PATH not set; export it to your .gguf file.") | |
| st.stop() | |
| llm = LLMBackend.from_llamacpp(model_path=model_path, temperature=0.2) | |
| else: | |
| llm = LLMBackend.from_lmstudio(temperature=0.2) | |
| answer_md, sources_list = rag.answer( | |
| query=query, | |
| top_k=top_k, | |
| reading_level=reading_level, | |
| llm=llm, | |
| source_filter=src_filter | |
| ) | |
| st.markdown(answer_md) | |
| with st.expander("Sources"): | |
| for s in sources_list: | |
| st.write(f"- {s}") | |
| if show_chunks: | |
| st.divider() | |
| st.subheader("Retrieved snippets (preview)") | |
| previews = rag._retrieve(query=query, top_k=top_k, source_filter=src_filter) | |
| for i, (doc, meta) in enumerate(previews, 1): | |
| st.markdown(f"**{i}. {meta.get('source')} — {meta.get('section')}**") | |
| st.write((doc or "")[:400] + ("..." if doc and len(doc) > 400 else "")) | |