# app.py - MedGenesis AI Streamlit app (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

# --- Fix Streamlit temp dir ---
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"

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.get('title', '')}"))
        pdf.set_font("Helvetica", "", 9)
        body = f"{p.get('authors','')}\n{p.get('summary','')}\n{p.get('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():
    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"].get("ai_summary", ""))

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

    # Session state
    for k, v in [
        ("query_result", None), ("followup_input", ""),
        ("followup_response", None), ("last_query", ""), ("last_llm", "")
    ]:
        if k not in st.session_state:
            st.session_state[k] = v

    _workspace_sidebar()
    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
    wsq = get_workspace()
    if wsq:
        try:
            news = asyncio.run(check_alerts([w["query"] for w in wsq]))
            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

    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.get('llm_used','LLM').title()}**")
        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 not res:
        st.info("Enter a question and press **Run Search 🚀**")
        return

    tabs = st.tabs(["Results", "Genes", "Trials", "Variants", "Graph", "Metrics", "Visuals"])
    # --------------- Results Tab ---------------
    with tabs[0]:
        for i, p in enumerate(res.get("papers", []), 1):
            st.markdown(f"**{i}. [{p.get('title','')}]({p.get('link','')})**  *{p.get('authors','')}*")
            st.write(p.get("summary", ""))
        col1, col2 = st.columns(2)
        with col1:
            st.download_button("CSV", pd.DataFrame(res.get("papers", [])).to_csv(index=False),
                               "papers.csv", "text/csv")
        with col2:
            st.download_button("PDF", _pdf(res.get("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.get("umls", []):
            if isinstance(c, dict) and c.get("cui"):
                st.write(f"- **{c.get('name','')}** ({c.get('cui')})")
        st.subheader("OpenFDA safety signals")
        st.json(res.get("drug_safety", []))
        st.subheader("AI summary")
        st.info(res.get("ai_summary", ""))

    # --------------- Genes Tab ---------------
    with tabs[1]:
        st.header("Gene / Variant signals")
        genes = res.get("genes", [])
        if not genes:
            st.info("No gene hits (rate-limited or none found).")
        else:
            for g in genes:
                if isinstance(g, dict):
                    lab = g.get("name") or g.get("symbol") or g.get("geneid")
                    st.write(f"- **{lab}** {g.get('description','')}")
        if res.get("gene_disease"):
            st.markdown("### DisGeNET associations")
            st.json(res.get("gene_disease")[:15])
        if res.get("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")
        trials = res.get("clinical_trials", [])
        if not trials:
            st.info("No trials (rate-limited or none found).")
        else:
            for t in trials:
                nct = t.get("nctId") or (t.get("NCTId", [""])[0] if isinstance(t.get("NCTId"), list) else "")
                title = t.get("briefTitle") or (t.get("BriefTitle", [""])[0] if isinstance(t.get("BriefTitle"), list) else "")
                phase = t.get("phase") or (t.get("Phase", [""])[0] if isinstance(t.get("Phase"), list) else "")
                status = t.get("status") or (t.get("OverallStatus", [""])[0] if isinstance(t.get("OverallStatus"), list) else "")
                st.markdown(f"**{nct}** – {title}")
                st.write(f"Phase {phase} | Status {status}")

    # --------------- Variants Tab ---------------
    with tabs[3]:
        st.header("Cancer variants (cBioPortal)")
        variants = res.get("variants", [])
        if not variants:
            st.info("No variant data.")
        else:
            for v in variants:
                st.json(v)

    # --------------- Graph Tab ---------------
    with tabs[4]:
        nodes, edges, cfg = build_agraph(res.get("papers", []), res.get("umls", []), res.get("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]:
        nodes, edges, _ = build_agraph(res.get("papers", []), res.get("umls", []), res.get("drug_safety", []))
        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", "") for p in res.get("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")
    def handle_followup():
        follow = st.session_state.followup_input
        if follow.strip():
            ans = asyncio.run(answer_ai_question(
                follow,
                context=st.session_state.last_query,
                llm=st.session_state.last_llm))
            st.session_state.followup_response = ans.get("answer", "No answer.")
        else:
            st.session_state.followup_response = None
    st.button("Ask AI", on_click=handle_followup)
    if st.session_state.followup_response:
        st.write(st.session_state.followup_response)

if __name__ == "__main__":
    render_ui()