app.py

# ──────────────────────────── app.py ─────────────────────────────────
"""Streamlit UI – MedGenesis v2 with gene + variant + trial integration."""
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_utils      import build_nx, get_top_hubs, get_density
from mcp.alerts          import check_alerts

# ---- Streamlit telemetry patch -------------------------------------
os.environ.update({
    "STREAMLIT_DATA_DIR": "/tmp/.streamlit",
    "XDG_STATE_HOME":     "/tmp",
    "STREAMLIT_BROWSER_GATHERUSAGESTATS": "false",
})
pathlib.Path("/tmp/.streamlit").mkdir(parents=True, exist_ok=True)

ROOT = Path(__file__).parent
LOGO = ROOT / "assets" / "logo.png"

# ---------------- helpers -------------------------------------------

def _latin1_safe(t: str) -> str:
    return t.encode("latin-1", "replace").decode("latin-1")

def _export_pdf(papers):
    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", size=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")

# ---------------- sidebar -------------------------------------------

def _workspace_sidebar():
    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 ----------------------------------------------

def render_ui():
    st.set_page_config("MedGenesis AI", layout="wide")
    _workspace_sidebar()

    # header ---------------------------------------------------------
    c1, c2 = st.columns([0.15, 0.85])
    if LOGO.exists():
        with c1: 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", "CRISPR glioblastoma therapy")

    if st.button("Run Search 🚀") and query:
        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.result = res
        st.session_state.last_query = query
        st.session_state.last_llm   = llm

    res = st.session_state.get("result")
    if not res:
        st.info("Enter a question and press **Run Search 🚀**")
        return

    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")
        with c2:
            st.download_button("PDF", _export_pdf(res["papers"]), "papers.pdf", mime="application/pdf")
        if st.button("💾 Save"):
            save_query(query, res)
            st.success("Saved to workspace")
        st.subheader("AI summary")
        st.info(res["ai_summary"])

    # gene tab -------------------------------------------------------
    with tabs[1]:
        if not res["genes"]:
            st.info("No gene hits (rate‑limited or none found).")
        for g in res["genes"]:
            st.json(g)
        if res["variants"]:
            st.markdown("### Tumour variants (cBioPortal)")
            for k, v in res["variants"].items():
                st.write(f"**{k}** – {len(v)} variants")

    # trials tab -----------------------------------------------------
    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']}** – {t['briefTitle']}")
            st.write(f"Phase {t.get('phase')} | Status {t.get('status')}")

    # graph tab ------------------------------------------------------
    with tabs[3]:
        nodes, edges, cfg = build_agraph(res["papers"], res["umls"], res["drug_safety"])
        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 tab ----------------------------------------------------
    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}")
        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.get("published", "")[:4] for p in res["papers"] if p.get("published")]
        if years:
            fig = px.histogram(years, nbins=12, title="Publication Year")
            st.plotly_chart(fig)

    # follow‑up QA ---------------------------------------------------
    st.markdown("---")
    q = st.text_input("Ask follow‑up question:")
    if st.button("Ask AI"):
        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.write(ans["answer"])


if __name__ == "__main__":
    render_ui()