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import asyncio, streamlit as st, pandas as pd |
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from mcp.orchestrator import orchestrate_search |
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st.set_page_config(layout="wide", page_title="MedGenesis AI") |
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if "res" not in st.session_state: st.session_state.res = None |
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st.title("🧬 MedGenesis AI") |
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llm = st.radio("LLM engine", ["openai","gemini"], horizontal=True) |
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q = st.text_input("Enter biomedical question") |
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if st.button("Run Search") and q: |
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with st.spinner("Fetching data…"): |
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st.session_state.res = asyncio.run(orchestrate_search(q, llm=llm)) |
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res = st.session_state.res |
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if res: |
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st.subheader("🔬 Papers") |
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for p in res["papers"]: |
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st.markdown(f"**[{p['title']}]({p['link']})** – {p['authors']}") |
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st.write(p["summary"]) |
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st.subheader("💡 AI Summary") |
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st.info(res["ai_summary"]) |
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tabs = st.tabs(["Graph","Variants","Trials"]) |
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with tabs[0]: |
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from mcp.knowledge_graph import build_agraph |
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nodes, edges, cfg = build_agraph(res) |
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from streamlit_agraph import agraph |
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agraph(nodes, edges, cfg) |
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with tabs[1]: |
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if res["variants"]: |
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st.json(res["variants"]) |
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else: |
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st.warning("No variants found. Try TP53 or BRCA1.") |
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with tabs[2]: |
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if res["trials"]: |
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st.json(res["trials"]) |
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else: |
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st.warning("No trials. Try a disease e.g. ‘Breast Neoplasms’ or a drug.") |
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else: |
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st.info("Enter a query and press Run Search.") |
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