#!/usr/bin/env python3 # MedGenesis AI · CPU-only Streamlit front-end (OpenAI / Gemini) from __future__ import annotations import os, pathlib, asyncio, re from pathlib import Path import streamlit as st import pandas as pd import plotly.express as px from fpdf import FPDF from streamlit_agraph import agraph from mcp.orchestrator import orchestrate_search, answer_ai_question from mcp.workspace import get_workspace, save_query, clear_workspace from mcp.knowledge_graph import build_agraph from mcp.graph_utils import build_nx, get_top_hubs, get_density from mcp.alerts import check_alerts # ── Streamlit telemetry dir fix ────────────────────────────────────── os.environ["STREAMLIT_DATA_DIR"] = "/tmp/.streamlit" os.environ["XDG_STATE_HOME"] = "/tmp" os.environ["STREAMLIT_BROWSER_GATHERUSAGESTATS"] = "false" pathlib.Path("/tmp/.streamlit").mkdir(parents=True, exist_ok=True) ROOT = Path(__file__).parent LOGO = ROOT / "assets" / "logo.png" # -------------------------------------------------------------------# # Utility helpers # # -------------------------------------------------------------------# def _latin1_safe(txt: str) -> str: return txt.encode("latin-1", "replace").decode("latin-1") def _pdf(papers: list[dict]) -> bytes: pdf = FPDF() pdf.set_auto_page_break(auto=True, margin=15) pdf.add_page() pdf.set_font("Helvetica", size=11) pdf.cell(200, 8, _latin1_safe("MedGenesis AI – Results"), ln=True, align="C") pdf.ln(3) for i, p in enumerate(papers, 1): pdf.set_font("Helvetica", "B", 11) pdf.multi_cell(0, 7, _latin1_safe(f"{i}. {p['title']}")) pdf.set_font("Helvetica", "", 9) body = f"{p['authors']}\n{p['summary']}\n{p['link']}\n" pdf.multi_cell(0, 6, _latin1_safe(body)) pdf.ln(1) return pdf.output(dest="S").encode("latin-1", "replace") def _workspace_sidebar() -> None: with st.sidebar: st.header("🗂️ Workspace") ws = get_workspace() if not ws: st.info("Run a search then press **Save** to populate this list.") return if st.button("Clear workspace 🗑️"): clear_workspace() st.experimental_rerun() for i, item in enumerate(ws, 1): with st.expander(f"{i}. {item['query']}"): st.write(item["result"]["ai_summary"]) # -------------------------------------------------------------------# # Streamlit main UI # # -------------------------------------------------------------------# def render_ui() -> None: st.set_page_config("MedGenesis AI", layout="wide") # ── session_state bootstrap ──────────────────────────────────── for key, default in { "query_result" : None, "followup_input" : "", "followup_response" : None, "last_query" : "", "last_llm" : "", }.items(): st.session_state.setdefault(key, default) _workspace_sidebar() # ── header ───────────────────────────────────────────────────── c1, c2 = st.columns([0.15, 0.85]) with c1: if LOGO.exists(): st.image(str(LOGO), width=105) with c2: st.markdown("## 🧬 **MedGenesis AI**") st.caption("Multi-source biomedical assistant · OpenAI / Gemini") llm = st.radio("LLM engine", ["openai", "gemini"], horizontal=True) query = st.text_input("Enter biomedical question", placeholder="e.g. CRISPR glioblastoma therapy") # ── alerts for saved queries ─────────────────────────────────── if ws := get_workspace(): try: news = asyncio.run(check_alerts([w["query"] for w in ws])) if news: with st.sidebar: st.subheader("🔔 New papers") for q, lnks in news.items(): st.write(f"**{q}** – {len(lnks)} new") except Exception: pass # ── primary search trigger ───────────────────────────────────── if st.button("Run Search 🚀") and query.strip(): with st.spinner("Collecting literature & biomedical data …"): res = asyncio.run(orchestrate_search(query, llm=llm)) st.success(f"Completed with **{res['llm_used'].title()}**") st.session_state.update({ "query_result" : res, "last_query" : query, "last_llm" : llm, "followup_input" : "", "followup_response" : None, }) res = st.session_state.query_result if not res: st.info("Enter a question and press **Run Search 🚀**") return # ----------------------------------------------------------------# # Tabs # # ----------------------------------------------------------------# tabs = st.tabs(["Results", "Genes", "Trials", "Graph", "Metrics", "Visuals"]) # Results --------------------------------------------------------- with tabs[0]: for i, p in enumerate(res["papers"], 1): st.markdown(f"**{i}. [{p['title']}]({p['link']})** *{p['authors']}*") st.write(p["summary"]) c1, c2 = st.columns(2) with c1: st.download_button("CSV", pd.DataFrame(res["papers"]).to_csv(index=False), "papers.csv", "text/csv") with c2: st.download_button("PDF", _pdf(res["papers"]), "papers.pdf", "application/pdf") if st.button("💾 Save this result"): save_query(st.session_state.last_query, res) st.success("Saved to workspace") st.subheader("UMLS concepts") for c in res["umls"]: if c.get("cui"): st.write(f"- **{c['name']}** ({c['cui']})") st.subheader("OpenFDA safety") for d in res["drug_safety"]: st.json(d) st.subheader("AI summary") st.info(res["ai_summary"]) # Genes ----------------------------------------------------------- with tabs[1]: st.header("Gene / Variant signals") if not res["genes"]: st.info("No gene hits (rate-limited or none found).") for g in res["genes"]: st.write(f"- **{g.get('symbol', g.get('name', ''))}** " f"{g.get('summary', '')[:120]}…") if res["gene_disease"]: st.markdown("### DisGeNET links") st.json(res["gene_disease"][:15]) if res["mesh_defs"]: st.markdown("### MeSH definitions") for d in res["mesh_defs"]: if d: st.write("-", d) # Trials ---------------------------------------------------------- with tabs[2]: st.header("Clinical trials") trials = res["clinical_trials"] if not trials: st.info("No trials (rate-limited or none found).") for t in trials: st.markdown(f"**{t['nctId']}** – {t['briefTitle']}") st.write(f"Phase {t.get('phase','')} | Status {t.get('status')}") # Graph ----------------------------------------------------------- with tabs[3]: nodes, edges, cfg = build_agraph( res["papers"], res["umls"], res["drug_safety"], res["genes"], res["clinical_trials"], res.get("ot_associations", []) ) hl = st.text_input("Highlight node:") if hl: pat = re.compile(re.escape(hl), re.I) for n in nodes: n.color = "#f1c40f" if pat.search(n.label) else "#d3d3d3" agraph(nodes, edges, cfg) # Metrics --------------------------------------------------------- with tabs[4]: G = build_nx([n.__dict__ for n in nodes], [e.__dict__ for e in edges]) st.metric("Density", f"{get_density(G):.3f}") st.markdown("**Top hubs**") for nid, sc in get_top_hubs(G): lab = next((n.label for n in nodes if n.id == nid), nid) st.write(f"- {lab} {sc:.3f}") # Visuals --------------------------------------------------------- with tabs[5]: years = [p["published"] for p in res["papers"] if p.get("published")] if years: st.plotly_chart(px.histogram(years, nbins=12, title="Publication Year")) # ----------------------------------------------------------------# # Follow-up Q & A # # ----------------------------------------------------------------# st.markdown("---") st.text_input("Ask follow-up question:", key="followup_input", placeholder="e.g. Any phase III trials recruiting now?") def _on_ask() -> None: q = st.session_state.followup_input.strip() if not q: st.warning("Please type a question first.") return with st.spinner("Querying LLM …"): ans = asyncio.run( answer_ai_question(q, context=st.session_state.last_query, llm=st.session_state.last_llm)) st.session_state.followup_response = ans["answer"] st.button("Ask AI", on_click=_on_ask) if st.session_state.followup_response: st.write(st.session_state.followup_response) # -------------------------------------------------------------------# if __name__ == "__main__": render_ui()