app.py

#!/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()