app.py

# app.py — MedGenesis AI (CPU edition)

import 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

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

# ---------------------------------------------------------------------
def pdf_from_papers(papers):
    pdf = FPDF(); pdf.add_page(); pdf.set_font("Arial", size=12)
    pdf.cell(200, 10, "MedGenesis AI — Results", ln=True, align="C"); pdf.ln(8)
    for i, p in enumerate(papers, 1):
        pdf.set_font("Arial", "B", 12)
        pdf.multi_cell(0, 8, f"{i}. {p['title']}")
        pdf.set_font("Arial", "", 9)
        pdf.multi_cell(0, 6, f"{p['authors']}\n{p['summary']}\n{p['link']}\n")
        pdf.ln(2)
    return pdf.output(dest="S").encode("latin-1")

# ---------------------------------------------------------------------
def sidebar_workspace():
    with st.sidebar:
        st.header("🗂️ Workspace")
        ws = get_workspace()
        if not ws:
            st.info("Run a search and click **Save** to build your workspace.")
            return
        for i, item in enumerate(ws, 1):
            with st.expander(f"{i}. {item['query']}"):
                st.write("**AI Summary**:", item["result"]["ai_summary"])
                df = pd.DataFrame(item["result"]["papers"])
                st.download_button("📥 CSV", df.to_csv(index=False),
                                   f"workspace_{i}.csv", "text/csv")

# ---------------------------------------------------------------------
def render_ui():
    st.set_page_config(page_title="MedGenesis AI", layout="wide")

    # 🔔 quick alert check
    saved_q = [q["query"] for q in get_workspace()]
    if saved_q:
        try:
            alerts = asyncio.run(check_alerts(saved_q))
            if alerts:
                with st.sidebar:
                    st.subheader("🔔 New Papers")
                    for q, links in alerts.items():
                        st.write(f"**{q}** – {len(links)} new")
        except Exception as e:
            st.sidebar.warning(f"Alert check failed: {e}")

    sidebar_workspace()

    # Header
    col1, col2 = st.columns([0.15, 0.85])
    with col1:
        if LOGO.exists(): st.image(str(LOGO), width=100)
    with col2:
        st.markdown("## 🧬 **MedGenesis AI**")
        st.caption("PubMed·ArXiv·OpenFDA·UMLS·NCBI·DisGeNET·ClinicalTrials·GPT-4o")

    st.markdown("---")
    query = st.text_input("🔍 Ask your biomedical question:",
                          placeholder="e.g. CRISPR for glioblastoma")

    # -----------------------------------------------------------------
    if st.button("Run Search 🚀") and query:
        with st.spinner("Synthesizing multi-source biomedical intel…"):
            res = asyncio.run(orchestrate_search(query))
        st.success("Ready!")

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

        # ----------- RESULTS -----------------
        with tabs[0]:
            st.header("📚 Literature")
            for i, p in enumerate(res["papers"], 1):
                st.markdown(f"**{i}. [{p['title']}]({p['link']})**  *{p['authors']}*")
                st.markdown(f"<span style='color:gray'>{p['summary']}</span>",
                            unsafe_allow_html=True)

            colA, colB = st.columns(2)
            with colA:
                if st.button("💾 Save to Workspace"):
                    save_query(query, res); st.success("Saved!")
            with colB:
                st.download_button("📥 CSV", pd.DataFrame(res["papers"]).to_csv(index=False),
                                   "papers.csv", "text/csv")

            st.download_button("📄 PDF", pdf_from_papers(res["papers"]),
                               "papers.pdf", "application/pdf")

            st.subheader("🧠 UMLS")
            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 & VARIANTS --------
        with tabs[1]:
            st.header("🧬 Gene Signals")
            for g in res["genes"]:
                st.write(f"- **{g.get('name', g.get('geneid'))}** – {g.get('description','')}")
            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"]: st.write("-", d)

        # ----------- TRIALS ------------------
        with tabs[2]:
            st.header("💊 Clinical Trials")
            if not res["clinical_trials"]:
                st.info("No trials retrieved (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]} | Status: {t['OverallStatus'][0]}")

        # ----------- GRAPH -------------------
        with tabs[3]:
            st.header("🗺️ Knowledge Graph")
            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:
                    if pat.search(n.label): n.color, n.size = "#f1c40f", 30
                    else: n.color = "#d3d3d3"
            agraph(nodes=nodes, edges=edges, config=cfg)

        # ----------- METRICS -----------------
        with tabs[4]:
            st.header("📈 Graph Metrics")
            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("#### Hub Nodes")
            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 AI ---------------
        st.markdown("---")
        follow = st.text_input("Ask follow-up question:")
        if st.button("Ask AI"):
            st.write(asyncio.run(answer_ai_question(follow, context=query))["answer"])

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


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