app.py

#!/usr/bin/env python3
# MedGenesis AI – Streamlit frontend (OpenAI / Gemini)

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
from mcp.knowledge_graph import build_agraph
from mcp.graph_metrics   import build_nx, get_top_hubs, get_density
from mcp.alerts          import check_alerts

# ── Streamlit telemetry off ─────────────────────────────────────────
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"

# ── PDF helper ──────────────────────────────────────────────────────
def _latin1(txt: str) -> str:
    return txt.encode("latin-1", "replace").decode("latin-1")

def _pdf(papers):
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Helvetica", size=11)
    pdf.cell(200, 8, _latin1("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(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(body)); pdf.ln(1)
    return pdf.output(dest="S").encode("latin-1", "replace")

# ── Sidebar ────────────────────────────────────────────────────────
def _sidebar_workspace():
    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
        for i, item in enumerate(ws, 1):
            with st.expander(f"{i}. {item['query']}"):
                st.write(item["result"]["ai_summary"])

# ── Main UI ────────────────────────────────────────────────────────
def render_ui():
    st.set_page_config("MedGenesis AI", layout="wide")

    # session state
    st.session_state.setdefault("result", None)
    st.session_state.setdefault("last_query", "")
    st.session_state.setdefault("last_llm", "")
    st.session_state.setdefault("followup", "")
    st.session_state.setdefault("answer", "")

    _sidebar_workspace()

    c1, c2 = st.columns([0.15, 0.85])
    if LOGO.exists(): c1.image(str(LOGO), width=105)
    c2.markdown("## 🧬 **MedGenesis AI**")
    c2.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
    if get_workspace():
        try:
            news = asyncio.run(check_alerts([w["query"] for w in get_workspace()]))
            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

    # Run search
    if st.button("Run Search 🚀") and query.strip():
        with st.spinner("Collecting literature & biomedical data …"):
            res = asyncio.run(orchestrate_search(query, llm=llm))
        st.session_state.update(
            result=res, last_query=query, last_llm=llm,
            followup="", answer=""
        )
        st.success(f"Completed with **{res['llm_used'].title()}**")

    res = st.session_state.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)
        c1.download_button("CSV",
                           pd.DataFrame(res["papers"]).to_csv(index=False),
                           "papers.csv", "text/csv")
        c2.download_button("PDF", _pdf(res["papers"]),
                           "papers.pdf", "application/pdf")
        if st.button("💾 Save"):
            save_query(st.session_state.last_query, res)
            st.success("Saved to workspace")

        st.subheader("UMLS concepts")
        for c in res["umls"]:
            if isinstance(c, dict) and 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 res["genes_rich"]:
            for g in res["genes_rich"]:
                st.write(f"- **{g.get('symbol', g.get('approvedSymbol','?'))}**"
                         f" – {g.get('summary','')[:160]}…")
        else:
            st.info("No gene hits (rate-limited or none found).")

        if res["expr_atlas"]:
            st.plotly_chart(px.bar(
                res["expr_atlas"][0].get("expressions", [])[:10],
                x="assayName", y="value", title="Top tissues (Expression Atlas)"
            ))

        if res["cbio_variants"]:
            st.markdown("### cBioPortal cohort variants")
            st.json(res["cbio_variants"][0][:15])

    # Trials
    with tabs[2]:
        st.header("Clinical trials")
        if not res["clinical_trials"]:
            st.info("No trials (rate-limited or none found).")
        for t in res["clinical_trials"]:
            st.markdown(f"**{t['NCTId'][0]}** – {t['BriefTitle'][0]}")
            st.write(f"Phase {t.get('Phase',[''])[0]} | "
                     f"Status {t['OverallStatus'][0]}")

    # Graph
    with tabs[3]:
        nodes, edges, cfg = build_agraph(
            res["papers"], res["umls"], res["drug_safety"]
        )
        hl = st.text_input("Highlight node:", key="hl")
        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 QA
    st.markdown("---")
    st.text_input("Ask follow-up question:",
                  key="followup", placeholder="e.g. Any phase III trials recruiting now?")
    def _on_ask():
        q = st.session_state.followup.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.answer = ans["answer"]

    st.button("Ask AI", on_click=_on_ask)
    if st.session_state.answer:
        st.write(st.session_state.answer)

# entry-point
if __name__ == "__main__":
    render_ui()