app.py

#!/usr/bin/env python3
# app.py  –  MedGenesis AI · Streamlit front-end (v3)
# ---------------------------------------------------
# • Dual-LLM selector (OpenAI | Gemini)
# • Robust PDF export (all Unicode → Latin-1 safe)
# • Lazy session-state handling so a failed background
#   request never kills the whole app.
# • New “Variants” tab (cBioPortal) + null-safe “Graph”
#   and “Metrics” using the patched helpers.

import os, pathlib, asyncio, re
from pathlib import Path

import streamlit as st
import pandas as pd
import plotly.express as px
from streamlit_agraph import agraph
from fpdf import FPDF

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

# ── 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"

# ── PDF export helper (robust to ALL Unicode) ───────────────────────
def _latin1_safe(txt: str) -> str:
    return txt.encode("latin-1", "replace").decode("latin-1")

def _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", "", 9)
        body = (
            f"{p['authors']}\n"
            f"{p['summary']}\n"
            f"{p['link']}\n"
        )
        pdf.multi_cell(0, 6, _latin1_safe(body))
        pdf.ln(1)
    return pdf.output(dest="S").encode("latin-1", "replace")

# ── Sidebar workspace ───────────────────────────────────────────────
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"])

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

    # Session-state defaults
    for key, default in {
        "query_result"     : None,
        "last_query"       : "",
        "last_llm"         : "openai",
        "followup_input"   : "",
        "followup_response": None,
    }.items():
        if key not in st.session_state:
            st.session_state[key] = default

    _workspace_sidebar()

    # Header block
    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")

    # Controls
    llm   = st.radio("LLM engine", ["openai", "gemini"],
                     horizontal=True, index=0)
    query = st.text_input("Enter biomedical question",
                          placeholder="e.g. CRISPR glioblastoma therapy")

    # Run search
    if st.button("Run Search 🚀") and query:
        with st.spinner("Collecting literature & biomedical data …"):
            res = asyncio.run(orchestrate_search(query, llm=llm))
        st.session_state.query_result = res
        st.session_state.last_query   = query
        st.session_state.last_llm     = llm
        st.session_state.followup_input   = ""
        st.session_state.followup_response = None

    res = st.session_state.query_result
    if res:
        # Guard against missing keys
        for key in (
            "papers", "umls", "drug_safety", "genes", "mesh_defs",
            "gene_disease", "clinical_trials", "variants"
        ):
            res.setdefault(key, [])

        # -------------- TABS -------------------------------------------------
        tabs = st.tabs([
            "Results", "Genes", "Trials", "Variants",
            "Graph", "Metrics", "Visuals"
        ])

        # ── Results tab ─────────────────────────────────────────────────────
        with tabs[0]:
            st.subheader("Literature")
            for i, p in enumerate(res["papers"], 1):
                st.markdown(f"**{i}. [{p['title']}]({p['link']})**  *{p['authors']}*")
                st.write(p["summary"])
            col1, col2 = st.columns(2)
            with col1:
                st.download_button(
                    "CSV",
                    pd.DataFrame(res["papers"]).to_csv(index=False),
                    "papers.csv",
                    "text/csv",
                )
            with col2:
                st.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 c.get("cui"):
                    st.write(f"- **{c['name']}** ({c['cui']})")

            st.subheader("OpenFDA safety signals")
            for d in res["drug_safety"]:
                st.json(d)

            st.subheader("AI summary")
            st.info(res["ai_summary"])

        # ── Genes tab ───────────────────────────────────────────────────────
        with tabs[1]:
            st.header("Gene / Variant signals")
            for g in res["genes"]:
                lab = g.get("name") or g.get("symbol") or g.get("geneid")
                st.write(f"- **{lab}**")
            if res["gene_disease"]:
                st.markdown("### DisGeNET associations")
                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 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')}")

        # ── Variants tab ────────────────────────────────────────────────────
        with tabs[3]:
            st.header("Cancer variants (cBioPortal)")
            if not res["variants"]:
                st.info("No variant data.")
            else:
                st.json(res["variants"][:50])

        # ── Graph tab ───────────────────────────────────────────────────────
        with tabs[4]:
            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 tab ─────────────────────────────────────────────────────
        with tabs[5]:
            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 tab ────────────────────────────────────────────────────
        with tabs[6]:
            years = [p.get("published", "")[:4] 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 block ────────────────────────────────────────────
        st.markdown("---")
        st.text_input("Ask follow-up question:", key="followup_input")

        def _on_ask():
            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)

    else:
        st.info("Enter a question and press **Run Search 🚀**")


if __name__ == "__main__":
    render_ui()