Update app.py
Browse files
app.py
CHANGED
@@ -1,23 +1,23 @@
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#!/usr/bin/env python3
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#
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import os, pathlib
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os.environ["STREAMLIT_DATA_DIR"]
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os.environ["XDG_STATE_HOME"]
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os.environ["STREAMLIT_BROWSER_GATHERUSAGESTATS"] = "false"
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pathlib.Path("/tmp/.streamlit").mkdir(parents=True, exist_ok=True)
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# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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import asyncio, re
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from pathlib import Path
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import streamlit as st
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import pandas as pd
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import plotly.express as px
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from fpdf import FPDF
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from streamlit_agraph import agraph
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from mcp.orchestrator import orchestrate_search, answer_ai_question
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from mcp.workspace import get_workspace, save_query
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from mcp.knowledge_graph import build_agraph
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@@ -27,19 +27,35 @@ from mcp.alerts import check_alerts
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ROOT = Path(__file__).parent
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LOGO = ROOT / "assets" / "logo.png"
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#
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def _pdf(papers):
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pdf = FPDF()
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pdf.
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for i, p in enumerate(papers, 1):
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pdf.set_font("
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pdf.multi_cell(0, 7, f"{i}. {p['title']}")
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pdf.set_font("
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pdf.ln(1)
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return pdf.output(dest="S").encode("latin-1")
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with st.sidebar:
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st.header("ποΈ Workspace")
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ws = get_workspace()
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@@ -50,12 +66,12 @@ def _sidebar_workspace():
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with st.expander(f"{i}. {item['query']}"):
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st.write(item["result"]["ai_summary"])
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#
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def render_ui():
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st.set_page_config("MedGenesis AI", layout="wide")
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#
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c1, c2 = st.columns([0.15, 0.85])
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with c1:
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if LOGO.exists():
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@@ -64,21 +80,23 @@ def render_ui():
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st.markdown("## 𧬠**MedGenesis AI**")
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st.caption("Multi-source biomedical assistant Β· OpenAI / Gemini")
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llm
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query = st.text_input("Enter biomedical question",
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placeholder="e.g. CRISPR glioblastoma therapy")
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#
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if get_workspace():
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try:
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news = asyncio.run(check_alerts([w["query"] for w in get_workspace()]))
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if news:
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st.sidebar
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if st.button("Run Search π") and query:
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with st.spinner("Collecting literature & biomedical data β¦"):
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res = asyncio.run(orchestrate_search(query, llm=llm))
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@@ -87,7 +105,7 @@ def render_ui():
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tabs = st.tabs(["Results", "Genes", "Trials", "Graph",
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"Metrics", "Visuals"])
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# Results
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with tabs[0]:
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for i, p in enumerate(res["papers"], 1):
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st.markdown(f"**{i}. [{p['title']}]({p['link']})** *{p['authors']}*")
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@@ -118,7 +136,7 @@ def render_ui():
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st.subheader("AI summary")
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st.info(res["ai_summary"])
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# Genes
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with tabs[1]:
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st.header("Gene / Variant signals")
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for g in res["genes"]:
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if d:
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st.write("-", d)
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# Trials
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with tabs[2]:
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st.header("Clinical trials")
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if not res["clinical_trials"]:
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st.info("No trials (rate-limited or none found).")
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for t in res["clinical_trials"]:
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st.markdown(f"**{t['NCTId'][0]}** β {t['BriefTitle'][0]}")
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st.write(f"Phase {t.get('Phase', [''])[0]}
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f"Status {t['OverallStatus'][0]}")
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# Graph
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with tabs[3]:
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nodes, edges, cfg = build_agraph(res["papers"],
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res["umls"],
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n.color = "#f1c40f" if pat.search(n.label) else "#d3d3d3"
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agraph(nodes, edges, cfg)
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# Metrics
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with tabs[4]:
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G = build_nx([n.__dict__ for n in nodes],
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[e.__dict__ for e in edges])
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lab = next((n.label for n in nodes if n.id == nid), nid)
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st.write(f"- {lab} {sc:.3f}")
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# Visuals
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with tabs[5]:
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years = [p["published"] for p in res["papers"] if p.get("published")]
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if years:
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st.plotly_chart(px.histogram(years, nbins=12,
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title="Publication Year"))
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st.markdown("---")
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follow = st.text_input("Ask follow-up:")
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if st.button("Ask AI"):
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#!/usr/bin/env python3
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# MedGenesis AI Β· CPU-only Streamlit app (OpenAI / Gemini)
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# ββ Streamlit telemetry dir fix βββββββββββββββββββββββββββββββββββββββ
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import os, pathlib
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os.environ["STREAMLIT_DATA_DIR"] = "/tmp/.streamlit"
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os.environ["XDG_STATE_HOME"] = "/tmp"
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os.environ["STREAMLIT_BROWSER_GATHERUSAGESTATS"] = "false"
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pathlib.Path("/tmp/.streamlit").mkdir(parents=True, exist_ok=True)
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# ββ Std-lib / third-party imports ββββββββββββββββββββββββββββββββββββ
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import asyncio, re
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from pathlib import Path
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import streamlit as st
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import pandas as pd
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import plotly.express as px
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from fpdf import FPDF # classic FPDF β Latin-1 only
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from streamlit_agraph import agraph
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# ββ Internal helpers ββββββββββββββββββββββββββββββββββββββββββββββββ
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from mcp.orchestrator import orchestrate_search, answer_ai_question
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from mcp.workspace import get_workspace, save_query
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from mcp.knowledge_graph import build_agraph
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ROOT = Path(__file__).parent
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LOGO = ROOT / "assets" / "logo.png"
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# ββ PDF export helper (UTF-8 β Latin-1 βsafeβ) ββββββββββββββββββββββ
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def _latin1_safe(txt: str) -> str:
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"""Return text that FPDF(latin-1) can embed; replace unknown chars."""
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return txt.encode("latin-1", "replace").decode("latin-1")
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def _pdf(papers):
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pdf = FPDF()
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pdf.set_auto_page_break(auto=True, margin=15)
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pdf.add_page()
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pdf.set_font("Helvetica", size=11)
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pdf.cell(200, 8, _latin1_safe("MedGenesis AI β Results"), ln=True, align="C")
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pdf.ln(3)
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for i, p in enumerate(papers, 1):
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pdf.set_font("Helvetica", "B", 11)
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pdf.multi_cell(0, 7, _latin1_safe(f"{i}. {p['title']}"))
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pdf.set_font("Helvetica", "", 9)
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body = (
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f"{p['authors']}\n"
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f"{p['summary']}\n"
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f"{p['link']}\n"
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)
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pdf.multi_cell(0, 6, _latin1_safe(body))
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pdf.ln(1)
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return pdf.output(dest="S").encode("latin-1", "replace")
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# ββ Sidebar workspace βββββββββββββββββββββββββββββββββββββββββββββββ
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def _workspace_sidebar():
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with st.sidebar:
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st.header("ποΈ Workspace")
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ws = get_workspace()
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with st.expander(f"{i}. {item['query']}"):
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st.write(item["result"]["ai_summary"])
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# ββ Main UI βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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def render_ui():
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st.set_page_config("MedGenesis AI", layout="wide")
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_workspace_sidebar()
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# Header
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c1, c2 = st.columns([0.15, 0.85])
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with c1:
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if LOGO.exists():
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st.markdown("## 𧬠**MedGenesis AI**")
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st.caption("Multi-source biomedical assistant Β· OpenAI / Gemini")
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llm = st.radio("LLM engine", ["openai", "gemini"], horizontal=True)
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query = st.text_input("Enter biomedical question",
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placeholder="e.g. CRISPR glioblastoma therapy")
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# Alert check
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if get_workspace():
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try:
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news = asyncio.run(check_alerts([w["query"] for w in get_workspace()]))
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if news:
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with st.sidebar:
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st.subheader("π New papers")
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for q, lnks in news.items():
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st.write(f"**{q}** β {len(lnks)} new")
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except Exception:
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pass
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# Run search
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if st.button("Run Search π") and query:
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with st.spinner("Collecting literature & biomedical data β¦"):
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res = asyncio.run(orchestrate_search(query, llm=llm))
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tabs = st.tabs(["Results", "Genes", "Trials", "Graph",
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"Metrics", "Visuals"])
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# Results
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with tabs[0]:
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for i, p in enumerate(res["papers"], 1):
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st.markdown(f"**{i}. [{p['title']}]({p['link']})** *{p['authors']}*")
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st.subheader("AI summary")
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st.info(res["ai_summary"])
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# Genes
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with tabs[1]:
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st.header("Gene / Variant signals")
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for g in res["genes"]:
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if d:
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st.write("-", d)
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# Trials
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with tabs[2]:
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st.header("Clinical trials")
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if not res["clinical_trials"]:
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st.info("No trials (rate-limited or none found).")
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for t in res["clinical_trials"]:
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st.markdown(f"**{t['NCTId'][0]}** β {t['BriefTitle'][0]}")
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st.write(f"Phase {t.get('Phase', [''])[0]} "
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f"| Status {t['OverallStatus'][0]}")
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# Graph
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with tabs[3]:
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nodes, edges, cfg = build_agraph(res["papers"],
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res["umls"],
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n.color = "#f1c40f" if pat.search(n.label) else "#d3d3d3"
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agraph(nodes, edges, cfg)
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# Metrics
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with tabs[4]:
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G = build_nx([n.__dict__ for n in nodes],
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[e.__dict__ for e in edges])
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lab = next((n.label for n in nodes if n.id == nid), nid)
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st.write(f"- {lab} {sc:.3f}")
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# Visuals
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with tabs[5]:
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years = [p["published"] for p in res["papers"] if p.get("published")]
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if years:
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st.plotly_chart(px.histogram(years, nbins=12,
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title="Publication Year"))
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# Follow-up Q-A
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st.markdown("---")
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follow = st.text_input("Ask follow-up:")
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if st.button("Ask AI"):
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