Delete app.py

app.py

@@ -1,717 +0,0 @@
-"""User Modeling Agent — the demo.
-
-DSN × BCT LLM Agent Challenge · Task A.
-
-Takes a user persona and product details as input, and generates a star
-rating and a written review as that user would write it — then critiques
-and revises its own draft (self-reflection). Optionally renders the review
-in Nigerian English.
-
-Two ways to use it:
-  1. Compose a persona  — type a persona + product (the brief's input contract)
-  2. Dataset reader     — pick a real user, compare against ground truth
-
-Run:
-    streamlit run app.py
-"""
-from __future__ import annotations
-
-import html
-import sys
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parent
-if str(ROOT) not in sys.path:
-    sys.path.insert(0, str(ROOT))
-
-import pandas as pd
-import streamlit as st
-
-from core.config import settings
-from core.persona import PersonaEngine, UserPersona
-from task_a_user_modeling.agent import ImpersonationAgent, ItemInput
-
-st.set_page_config(page_title="User Modeling Agent", page_icon="✶",
-                   layout="wide", initial_sidebar_state="expanded")
-
-esc = html.escape
-
-
-# ══════════════════════════════════════════════════════════════════════════════
-# Design system
-# ══════════════════════════════════════════════════════════════════════════════
-
-CSS = """
-<style>
-@import url('https://fonts.googleapis.com/css2?family=Fraunces:opsz,wght@9..144,400;9..144,500;9..144,600;9..144,900&family=Newsreader:ital,opsz,wght@0,6..72,400;0,6..72,500;0,6..72,600;1,6..72,400&family=Spline+Sans+Mono:wght@400;500;600&display=swap');
-
-:root {
-  --paper:#f3ecdb; --paper-2:#fffdf6; --paper-3:#ece2cb;
-  --pine:#1d3a2b; --pine-2:#2c5440; --pine-ink:#14241b;
-  --clay:#b0472b; --ochre:#c98a3c; --gold:#d8a64a;
-  --ink:#221e16; --muted:#6f6651; --hair:#d4c8aa;
-}
-.stApp { background:var(--paper); color:var(--ink); }
-.stApp::before {
-  content:""; position:fixed; inset:0; pointer-events:none; z-index:0;
-  background:
-    radial-gradient(900px 600px at 12% -5%, rgba(45,84,64,.10), transparent 60%),
-    radial-gradient(800px 600px at 95% 8%, rgba(176,71,43,.08), transparent 55%);
-}
-[data-testid="stMainBlockContainer"] { max-width:1140px; padding-top:2rem; padding-bottom:4rem; }
-h1,h2,h3,h4 { font-family:'Fraunces',Georgia,serif !important; color:var(--pine) !important;
-  letter-spacing:-0.015em; font-weight:600 !important; }
-html,body,p,div,span,label,li,.stMarkdown { font-family:'Newsreader',Georgia,serif; }
-.stCaption,[data-testid="stCaptionContainer"] { font-family:'Spline Sans Mono',monospace !important; }
-
-.masthead { position:relative; z-index:1; margin-bottom:0.3rem; }
-.mast-rule { height:2px; background:var(--pine); margin-bottom:0.5rem; }
-.mast-kicker { font-family:'Spline Sans Mono',monospace; font-size:0.70rem;
-  letter-spacing:0.30em; text-transform:uppercase; color:var(--clay); font-weight:600; }
-.mast-title { font-family:'Fraunces',serif; font-weight:900;
-  font-size:clamp(2.3rem,5.5vw,3.7rem); line-height:1.0; color:var(--pine);
-  margin:0.16rem 0 0.1rem; letter-spacing:-0.03em; }
-.mast-title .em { color:var(--clay); font-style:italic; font-weight:500; }
-.mast-stand { font-family:'Newsreader',serif; font-size:1.08rem; color:#45402f;
-  max-width:66ch; line-height:1.45; }
-.mast-stand em { color:var(--clay); font-style:italic; }
-.mast-rule-bot { height:1px; background:var(--hair); margin:0.85rem 0 0.2rem; }
-
-.sec-label { font-family:'Spline Sans Mono',monospace; font-size:0.70rem;
-  letter-spacing:0.2em; text-transform:uppercase; color:var(--clay);
-  font-weight:600; margin:0.3rem 0 0.15rem; }
-
-.card { background:var(--paper-2); border:1px solid var(--hair); border-radius:3px;
-  padding:1.1rem 1.3rem; margin:0.5rem 0 0.85rem; position:relative; z-index:1; }
-.card-kicker { font-family:'Spline Sans Mono',monospace; font-size:0.64rem;
-  letter-spacing:0.2em; text-transform:uppercase; color:var(--clay);
-  font-weight:600; margin-bottom:0.5rem; }
-
-.persona-quote { font-family:'Fraunces',serif; font-weight:500; font-style:italic;
-  font-size:1.26rem; line-height:1.34; color:var(--pine); margin:0.1rem 0 0.8rem;
-  padding-left:0.8rem; border-left:3px solid var(--ochre); }
-.pstats { display:flex; gap:1.7rem; flex-wrap:wrap; align-items:flex-end; }
-.pstat .num { font-family:'Fraunces',serif; font-weight:900; font-size:1.5rem;
-  color:var(--pine); line-height:1; }
-.pstat .lab { font-family:'Spline Sans Mono',monospace; font-size:0.60rem;
-  letter-spacing:0.13em; text-transform:uppercase; color:var(--muted); margin-top:0.2rem; }
-.chips { margin-top:0.6rem; }
-.chip-lab { font-family:'Spline Sans Mono',monospace; font-size:0.60rem;
-  letter-spacing:0.12em; text-transform:uppercase; color:var(--muted); margin-right:0.4rem; }
-.chip { display:inline-block; margin:0.15rem 0.25rem 0.15rem 0; padding:0.15rem 0.6rem;
-  border-radius:999px; font-family:'Spline Sans Mono',monospace; font-size:0.72rem;
-  background:var(--paper-3); color:var(--pine-2); border:1px solid var(--hair); }
-.chip.warn { background:#f0ddd2; color:var(--clay); border-color:#e3c4b4; }
-
-.panel { background:var(--pine-ink); border-radius:3px; padding:1.35rem 1.55rem;
-  margin:0.5rem 0 0.85rem; position:relative; z-index:1;
-  box-shadow:0 14px 34px -22px rgba(20,36,27,.7); }
-.panel .card-kicker { color:var(--gold); }
-.rating-row { display:flex; align-items:center; gap:0.8rem; margin:0.25rem 0 0.65rem; }
-.rating-chip { font-family:'Fraunces',serif; font-weight:900; font-size:1.6rem;
-  background:var(--clay); color:#fff7ec; padding:0.05rem 0.65rem; border-radius:3px; }
-.stars { font-size:1.15rem; letter-spacing:0.1em; color:var(--gold); }
-.review-body { font-family:'Newsreader',serif; font-size:1.1rem; line-height:1.7;
-  color:#f0e9d6; white-space:pre-wrap; }
-.naija-badge { display:inline-block; margin-left:0.45rem; font-family:'Spline Sans Mono',monospace;
-  font-size:0.60rem; letter-spacing:0.12em; font-weight:600; background:#e9f0e2;
-  color:var(--pine); padding:0.12rem 0.5rem; border-radius:999px; border:1px solid #cdd9bf; }
-
-.stepper { display:flex; gap:0; margin:0.3rem 0 0.2rem; flex-wrap:wrap; }
-.step { flex:1; min-width:125px; padding:0.5rem 0.65rem; position:relative; }
-.step .dot { width:11px; height:11px; border-radius:50%; background:var(--pine); margin-bottom:0.35rem; }
-.step.flag .dot { background:var(--clay); }
-.step.pass .dot { background:var(--pine-2); }
-.step .st-name { font-family:'Fraunces',serif; font-weight:600; font-size:0.93rem;
-  color:var(--pine); line-height:1.1; }
-.step .st-sub { font-family:'Spline Sans Mono',monospace; font-size:0.63rem;
-  color:var(--muted); margin-top:0.18rem; }
-.step:not(:last-child)::after { content:""; position:absolute; top:0.87rem; right:-2px;
-  width:100%; height:1px;
-  background:repeating-linear-gradient(90deg,var(--hair) 0 6px,transparent 6px 12px); }
-.critique-note { font-family:'Newsreader',serif; font-style:italic; font-size:0.93rem;
-  color:#5a4030; line-height:1.45; background:#f0ddd2; border-left:3px solid var(--clay);
-  padding:0.5rem 0.75rem; border-radius:2px; margin-top:0.45rem; }
-
-.cmp { background:var(--paper-2); border:1px solid var(--hair); border-radius:3px;
-  padding:0.9rem 1.05rem; height:100%; }
-.cmp.truth { border-top:3px solid var(--pine-2); }
-.cmp.agent { border-top:3px solid var(--clay); }
-.cmp-head { font-family:'Spline Sans Mono',monospace; font-size:0.62rem;
-  letter-spacing:0.15em; text-transform:uppercase; color:var(--muted); margin-bottom:0.35rem; }
-.cmp-body { font-family:'Newsreader',serif; font-size:0.97rem; line-height:1.5;
-  color:#4a4434; white-space:pre-wrap; }
-.delta { font-family:'Spline Sans Mono',monospace; font-size:0.70rem; font-weight:600;
-  padding:0.16rem 0.55rem; border-radius:999px; }
-.delta.good { background:#e3ecd9; color:var(--pine); }
-.delta.mid { background:#f3e6c8; color:#8a6420; }
-.delta.far { background:#f0d8cc; color:var(--clay); }
-
-.empty { border:1px dashed var(--hair); border-radius:3px; padding:1.5rem; text-align:center;
-  font-family:'Newsreader',serif; font-style:italic; color:var(--muted); font-size:1rem;
-  background:rgba(255,253,246,.5); }
-
-@keyframes rise { from{opacity:0;transform:translateY(13px);} to{opacity:1;transform:translateY(0);} }
-.reveal { animation:rise 0.55s cubic-bezier(.2,.7,.2,1) both; }
-.d1{animation-delay:.04s;} .d2{animation-delay:.13s;} .d3{animation-delay:.22s;}
-
-.stButton > button { background:var(--pine); color:var(--paper); border:none; border-radius:3px;
-  font-family:'Spline Sans Mono',monospace; font-weight:600; font-size:0.82rem;
-  letter-spacing:0.05em; padding:0.55rem 1rem; }
-.stButton > button:hover { background:var(--clay); color:#fff7ec; }
-[data-testid="stSidebar"] { background:var(--pine-ink); border-right:1px solid #2c4133; }
-[data-testid="stSidebar"] * { color:#e7e0cd; }
-[data-testid="stSidebar"] h1,[data-testid="stSidebar"] h2,[data-testid="stSidebar"] h3 { color:#f3ecdb !important; }
-
-/* toggle — colour ONLY the switch (track + knob), never the label row */
-[data-testid="stSidebar"] [data-testid="stWidgetLabel"] ~ div [role="switch"],
-[data-testid="stSidebar"] [data-baseweb="checkbox"] [role="switch"],
-[data-testid="stSidebar"] div[role="switch"] {
-  background-color:#c9bf9f !important;
-  border:1.5px solid #d8a64a !important;
-}
-[data-testid="stSidebar"] [data-baseweb="checkbox"] [role="switch"][aria-checked="true"],
-[data-testid="stSidebar"] div[role="switch"][aria-checked="true"] {
-  background-color:#d8a64a !important;
-  border-color:#e8c98a !important;
-}
-[data-testid="stSidebar"] [data-baseweb="checkbox"] [role="switch"] > div,
-[data-testid="stSidebar"] div[role="switch"] > div {
-  background-color:#1d3a2b !important;
-}
-[data-testid="stSidebar"] [data-baseweb="checkbox"] [role="switch"][aria-checked="true"] > div,
-[data-testid="stSidebar"] div[role="switch"][aria-checked="true"] > div {
-  background-color:#fffdf6 !important;
-}
-[data-baseweb="tab-list"] { gap:2.4rem; border-bottom:2px solid var(--pine); }
-[data-baseweb="tab"] { font-family:'Fraunces',serif !important; font-weight:600;
-  padding-left:0.7rem; padding-right:0.7rem;
-  font-size:1.04rem; color:var(--muted); }
-[data-baseweb="tab"][aria-selected="true"] { color:var(--pine) !important;
-  font-weight:900 !important; background:#ece2cb; border-radius:5px 5px 0 0; }
-[data-baseweb="tab-highlight"] { background:var(--clay) !important; height:4px; }
-
-/* form field contrast — fields were blending into the cream page */
-.stTextInput input, .stTextArea textarea,
-[data-baseweb="select"] > div, [data-baseweb="input"] {
-  background:#fffdf6 !important;
-  border:1px solid #c2b48f !important;
-  border-radius:4px !important;
-  color:#7d7560 !important;
-  font-weight:500 !important;
-}
-.stTextInput input:focus, .stTextArea textarea:focus {
-  border-color:var(--clay) !important;
-  box-shadow:0 0 0 1px var(--clay) !important;
-}
-.stTextInput input::placeholder, .stTextArea textarea::placeholder {
-  color:#9a917a !important;
-  font-weight:400 !important;
-}
-[data-testid="stExpander"] {
-  border:1px solid #c2b48f !important;
-  border-radius:5px !important;
-  background:#faf5e6 !important;
-}
-.foot { margin-top:2.2rem; padding-top:0.85rem; border-top:1px solid var(--hair);
-  font-family:'Spline Sans Mono',monospace; font-size:0.68rem; color:var(--muted); line-height:1.6; }
-</style>
-"""
-st.markdown(CSS, unsafe_allow_html=True)
-
-
-# ══════════════════════════════════════════════════════════════════════════════
-# HTML builders
-# ══════════════════════════════════════════════════════════════════════════════
-
-def stars(r: float) -> str:
-    f = int(round(r))
-    return "★" * f + "☆" * (5 - f)
-
-
-def persona_card(p: UserPersona) -> str:
-    themes = "".join(f'<span class="chip">{esc(t)}</span>'
-                     for t in p.preferred_themes) or '<span class="chip">—</span>'
-    comps = "".join(f'<span class="chip warn">{esc(t)}</span>'
-                    for t in p.common_complaints) or '<span class="chip warn">—</span>'
-    nrev = (f'{p.n_reviews}' if p.n_reviews else 'composed')
-    return f"""
-    <div class="card reveal d1">
-      <div class="card-kicker">The Reader · persona</div>
-      <div class="persona-quote">“{esc(p.voice_one_liner or 'No voice captured.')}”</div>
-      <div class="pstats">
-        <div class="pstat"><div class="num">{nrev}</div><div class="lab">history</div></div>
-        <div class="pstat"><div class="num">{p.avg_rating:.1f}★</div><div class="lab">avg rating</div></div>
-        <div class="pstat"><div class="num">{esc(p.tone or '—')}</div><div class="lab">tone</div></div>
-      </div>
-      <div class="chips"><span class="chip-lab">drawn to</span>{themes}</div>
-      <div class="chips"><span class="chip-lab">put off by</span>{comps}</div>
-    </div>"""
-
-
-def reflection_stepper(iters: int, refined: bool, notes: list[str] | None) -> str:
-    steps = ['<div class="step pass"><div class="dot"></div>'
-             '<div class="st-name">First draft</div>'
-             '<div class="st-sub">generated in-voice</div></div>']
-    if refined:
-        steps += ['<div class="step flag"><div class="dot"></div>'
-                  '<div class="st-name">Self-critique</div>'
-                  '<div class="st-sub">found issues</div></div>',
-                  '<div class="step pass"><div class="dot"></div>'
-                  '<div class="st-name">Revised draft</div>'
-                  '<div class="st-sub">rewritten with feedback</div></div>',
-                  '<div class="step pass"><div class="dot"></div>'
-                  '<div class="st-name">Re-checked</div>'
-                  '<div class="st-sub">critique cleared</div></div>']
-    else:
-        steps += ['<div class="step pass"><div class="dot"></div>'
-                  '<div class="st-name">Self-critique</div>'
-                  '<div class="st-sub">passed first pass</div></div>',
-                  '<div class="step pass"><div class="dot"></div>'
-                  '<div class="st-name">Accepted</div>'
-                  '<div class="st-sub">no revision needed</div></div>']
-    note = ""
-    if notes:
-        real = [n for n in notes if n and n.strip().lower() != "passed"]
-        if real:
-            note = f'<div class="critique-note">The critic flagged: {esc(real[0])}</div>'
-    return f"""
-    <div class="card reveal d3">
-      <div class="card-kicker">Self-reflection · {iters} critique cycle(s)</div>
-      <div class="stepper">{''.join(steps)}</div>
-      {note}
-    </div>"""
-
-
-# ════════════════════════��═════════════════════════════════════════════════════
-# Cached resources
-# ══════════════════════════════════════════════════════════════════════════════
-
-@st.cache_data(show_spinner=False)
-def load_data():
-    rev = pd.read_parquet(settings.processed_dir / "reviews.parquet")
-    items = pd.read_parquet(settings.processed_dir / "items.parquet")
-    return rev, items
-
-
-@st.cache_resource(show_spinner=False)
-def get_engines():
-    return PersonaEngine(), ImpersonationAgent()
-
-
-def composed_persona(desc: str, themes: list[str], dislikes: list[str],
-                     tone: str, avg_rating: float) -> UserPersona:
-    """Build a UserPersona from typed input — the brief's persona-as-input contract."""
-    # rating distribution skewed around the stated average
-    lo, hi = int(avg_rating), min(5, int(avg_rating) + 1)
-    dist = {lo: 0.55, hi: 0.35} if lo != hi else {lo: 0.9}
-    dist.setdefault(3, 0.1)
-    return UserPersona(
-        user_id="composed", n_reviews=0, avg_rating=avg_rating,
-        std_rating=0.6, avg_review_length=90.0, std_review_length=30.0,
-        verified_rate=1.0, domains=[], n_domains=0,
-        rating_distribution=dist, top_terms=[],
-        tone=tone, preferred_themes=themes, common_complaints=dislikes,
-        voice_one_liner=desc, history_samples=[],
-    )
-
-
-def persona_from_reviews(rows: list[dict]) -> UserPersona:
-    """Build a UserPersona from pasted past reviews.
-
-    Each row: {rating, title, domain, text, date(optional)}. Assembles them
-    into the column shape PersonaEngine.from_dataframe expects, then lets the
-    engine do the real modelling. This is the agent building a persona itself
-    from raw user history.
-    """
-    import pandas as _pd
-    records = []
-    for i, r in enumerate(rows):
-        ts = r.get("date")
-        # PersonaEngine sorts history by timestamp; fall back to entry order
-        try:
-            ts_val = _pd.Timestamp(ts) if ts else _pd.Timestamp("2020-01-01") + _pd.Timedelta(days=i)
-        except Exception:
-            ts_val = _pd.Timestamp("2020-01-01") + _pd.Timedelta(days=i)
-        records.append({
-            "user_id": "pasted",
-            "parent_asin": f"pasted_{i}",
-            "rating": float(r["rating"]),
-            "text": r["text"],
-            "verified_purchase": True,
-            "domain": r["domain"],
-            "timestamp": ts_val,
-        })
-    df = _pd.DataFrame(records)
-    engine = PersonaEngine()
-    persona = engine.from_dataframe("pasted", df)
-    return engine.enrich(persona)
-
-
-
-
-st.markdown("""
-<div class="masthead">
-  <div class="mast-rule"></div>
-  <div class="mast-kicker">DSN × BCT LLM Agent Challenge · Task A</div>
-  <div class="mast-title">User Modeling <span class="em">Agent</span></div>
-  <div class="mast-stand">
-    Give it a <em>user persona</em> and a <em>product</em>. It writes the star
-    rating and the review that user would write — weighing what they usually do
-    against what this specific item signals — then <em>critiques and revises</em>
-    its own draft before showing it.
-  </div>
-  <div class="mast-rule-bot"></div>
-</div>
-""", unsafe_allow_html=True)
-
-try:
-    reviews, items = load_data()
-except Exception as e:
-    st.error(f"Could not load data — ensure data/processed/*.parquet exist.\n\n{e}")
-    st.stop()
-
-train = reviews[reviews["split"] == "train"]
-test = reviews[reviews["split"] == "test"]
-persona_engine, agent = get_engines()
-
-with st.sidebar:
-    st.markdown("## ✶  Controls")
-    st.markdown(
-        '<div style="background:#1d3a2b;border:1px solid #3a5c46;border-radius:6px;'
-        'padding:0.7rem 0.85rem;margin-bottom:0.6rem">'
-        '<div style="font-family:Fraunces,serif;font-weight:600;font-size:1.05rem;'
-        'color:#f3ecdb">🇳🇬 Naija Mode</div>'
-        '<div style="font-family:Spline Sans Mono,monospace;font-size:0.68rem;'
-        'color:#d8a64a;letter-spacing:0.04em;margin-top:0.15rem">'
-        'NIGERIAN-ENGLISH LOCALIZATION</div></div>',
-        unsafe_allow_html=True)
-    naija = st.toggle("Render output in Nigerian English", value=False)
-    if naija:
-        st.markdown(
-            '<div style="background:#d8a64a;border-radius:5px;padding:0.4rem 0.7rem;'
-            'margin-top:0.3rem"><span style="font-family:Spline Sans Mono,monospace;'
-            'font-size:0.72rem;font-weight:600;color:#1d3a2b">'
-            '● NAIJA MODE ACTIVE</span></div>', unsafe_allow_html=True)
-    else:
-        st.markdown(
-            '<div style="background:#3a4d40;border:1px solid #5a6b60;border-radius:5px;'
-            'padding:0.4rem 0.7rem;margin-top:0.3rem">'
-            '<span style="font-family:Spline Sans Mono,monospace;font-size:0.72rem;'
-            'font-weight:600;color:#9bb0a3">○ OFF · STANDARD ENGLISH</span></div>',
-            unsafe_allow_html=True)
-    st.divider()
-    st.markdown("### How it works")
-    st.caption("The agent builds a persona, drafts a review in that voice, then "
-               "runs a self-reflection loop — a critic LLM checks rating-text "
-               "consistency, voice match and on-topic fit, and the agent revises "
-               "if the critic objects.")
-    st.divider()
-    st.caption(f"provider · {settings.llm_provider}")
-
-st.session_state.setdefault("result", None)
-st.session_state.setdefault("ctx", None)
-
-if naija:
-    st.markdown(
-        '<div style="background:linear-gradient(90deg,#1d3a2b,#2c5440);'
-        'border-left:4px solid #d8a64a;border-radius:4px;padding:0.7rem 1.1rem;'
-        'margin:0.4rem 0 0.2rem;display:flex;align-items:center;gap:0.7rem">'
-        '<span style="font-size:1.3rem">🇳🇬</span>'
-        '<span><span style="font-family:Fraunces,serif;font-weight:600;'
-        'font-size:1.02rem;color:#f3ecdb">Naija Mode is active</span>'
-        '<span style="font-family:Spline Sans Mono,monospace;font-size:0.74rem;'
-        'color:#e8c98a;margin-left:0.6rem">output localized to Nigerian English'
-        '</span></span></div>', unsafe_allow_html=True)
-
-
-# ══════════════════════════════════════════════════════════════════════════════
-# Tabs — Compose (primary) · Dataset reader (secondary)
-# ══════════════════════════════════════════════════════════════════════════════
-
-tab_compose, tab_dataset, tab_history = st.tabs([
-    "✎  Compose a Persona",
-    "⊞  Dataset Reader",
-    "❏  Build From Past Reviews"])
-
-# ── COMPOSE ───────────────────────────────────────────────────────────────────
-with tab_compose:
-    st.markdown('<div class="sec-label">Input · Persona and Product</div>',
-                unsafe_allow_html=True)
-
-    with st.expander("The Reader", expanded=True):
-        p_desc = st.text_area(
-            "Describe the Reader's Reviewing Voice",
-            value="A thoughtful reader who loves character-driven stories and "
-                  "rich world-building, but is impatient with slow pacing.",
-            height=90, key="p_desc")
-        p_themes = st.text_input("Drawn To (Comma-Separated)",
-                                 value="character development, immersive worlds, "
-                                       "original plots", key="p_themes")
-        p_dislikes = st.text_input("Put Off By (Comma-Separated)",
-                                   value="slow pacing, thin characters", key="p_dis")
-        c1, c2 = st.columns(2)
-        with c1:
-            p_tone = st.selectbox("Tone", ["enthusiastic", "analytical", "casual",
-                                           "critical", "earnest", "terse"], key="p_tone")
-        with c2:
-            p_rating = st.slider("Typical Rating", 1.0, 5.0, 4.0, 0.5, key="p_rate")
-
-    with st.expander("The Product", expanded=True):
-        i_title = st.text_input("Title", value="The Midnight Library", key="i_title")
-        i_domain = st.selectbox("Domain", ["Books", "Movies_and_TV", "Kindle_Store",
-                                           "Other"], key="i_domain")
-        i_desc = st.text_area(
-            "Description / Synopsis",
-            value="A novel about a library between life and death, where each "
-                  "book lets a woman try a different version of her life.",
-            height=110, key="i_desc")
-
-    go = st.button("Generate review  ✶", key="go_compose", use_container_width=True)
-
-    if go:
-        try:
-            with st.status("The agent is working…", expanded=True) as status:
-                themes = [t.strip() for t in p_themes.split(",") if t.strip()]
-                dislikes = [t.strip() for t in p_dislikes.split(",") if t.strip()]
-                st.write("Assembling the persona…")
-                persona = composed_persona(p_desc, themes, dislikes, p_tone, p_rating)
-                item = ItemInput(parent_asin="composed", title=i_title,
-                                 description=i_desc, categories="",
-                                 domain=i_domain)
-                st.write("Drafting in the reader's voice, then self-critiquing…")
-                result = agent.run(persona, item, naija_mode=naija)
-                st.write("Self-reflection complete")
-                status.update(label="Review generated", state="complete")
-            st.session_state.result = result
-            st.session_state.ctx = {"persona": persona, "item": item, "truth": None}
-        except Exception as e:
-            st.session_state.result = None
-            st.markdown(f'<div class="card" style="border-left:3px solid var(--clay)">'
-                        f'<div class="card-kicker">Generation interrupted</div>'
-                        f'The model call did not complete — it may be rate-limited. '
-                        f'Try again shortly.<br><span style="font-family:Spline Sans Mono,'
-                        f'monospace;font-size:0.72rem;color:#6f6651">'
-                        f'{esc(type(e).__name__)}</span></div>', unsafe_allow_html=True)
-
-# ── DATASET READER ────────────────────────────────────────────────────────────
-with tab_dataset:
-    st.markdown('<div class="sec-label">Input · A Real Reader From the Data</div>',
-                unsafe_allow_html=True)
-
-    elig = train.groupby("user_id").size().reset_index(name="n")
-    elig = elig[(elig["n"] >= 5) & (elig["user_id"].isin(set(test["user_id"])))]
-    users = elig.sample(min(40, len(elig)), random_state=7)["user_id"].tolist()
-
-    with st.expander("The Reader", expanded=True):
-        st.caption("Pick a real reader. The agent builds their persona from "
-                   "actual history and is scored against a held-out review.")
-        user = st.selectbox("Reader", users, key="sel_user")
-
-    go_ds = st.button("Generate review  ✶", key="go_ds", use_container_width=True)
-
-    if go_ds and user:
-        try:
-            with st.status("The agent is working…", expanded=True) as status:
-                ut = test[test["user_id"] == user]
-                if ut.empty:
-                    status.update(label="No held-out item for this reader",
-                                  state="error")
-                    st.stop()
-                tr = ut.iloc[0]
-                tid = tr["parent_asin"]
-                meta = items[items["parent_asin"] == tid]
-                if meta.empty:
-                    item = ItemInput(parent_asin=tid, title=str(tr.get("title", "")),
-                                     description="", categories="", domain=tr["domain"])
-                else:
-                    m = meta.iloc[0]
-                    item = ItemInput(parent_asin=tid, title=str(m.get("title", "")),
-                                     description=str(m.get("description", ""))[:1500],
-                                     categories=str(m.get("categories", "")),
-                                     domain=tr["domain"],
-                                     average_rating=(float(m["average_rating"])
-                                                     if pd.notna(m.get("average_rating"))
-                                                     else None))
-                st.write("Reading the reader's history…")
-                persona = persona_engine.from_dataframe(user, train)
-                persona = persona_engine.enrich(persona)
-                st.write(f"Persona built from {persona.n_reviews} reviews")
-                st.write("Drafting in their voice, then self-critiquing…")
-                result = agent.run(persona, item, naija_mode=naija)
-                st.write("Self-reflection complete")
-                status.update(label="Review generated", state="complete")
-            st.session_state.result = result
-            st.session_state.ctx = {"persona": persona, "item": item,
-                                    "truth": {"rating": float(tr["rating"]),
-                                              "text": str(tr["text"])}}
-        except Exception as e:
-            st.session_state.result = None
-            st.markdown(f'<div class="card" style="border-left:3px solid var(--clay)">'
-                        f'<div class="card-kicker">Generation interrupted</div>'
-                        f'The model call did not complete — it may be rate-limited. '
-                        f'Try again shortly.<br><span style="font-family:Spline Sans Mono,'
-                        f'monospace;font-size:0.72rem;color:#6f6651">'
-                        f'{esc(type(e).__name__)}</span></div>', unsafe_allow_html=True)
-
-
-# ── BUILD FROM PAST REVIEWS ────────────────────────────────────────────────────
-with tab_history:
-    st.markdown('<div class="sec-label">Input · Raw Past Reviews</div>',
-                unsafe_allow_html=True)
-    st.markdown("Paste a person's past reviews — the agent builds their persona "
-                "from this history, then writes a review of a new product. "
-                "Three to four reviews give the strongest persona.")
-
-    DOMAINS = ["Books", "Movies_and_TV", "Kindle_Store", "Other"]
-    hist_rows = []
-    for i in range(5):
-        with st.expander(f"Past Review {i + 1}", expanded=(i == 0)):
-            hc1, hc2, hc3 = st.columns([1, 2, 1])
-            with hc1:
-                h_rating = st.selectbox("Rating", [1.0, 2.0, 3.0, 4.0, 5.0],
-                                        index=3, key=f"h_rate_{i}")
-            with hc2:
-                h_title = st.text_input("Product Title", key=f"h_title_{i}",
-                                        placeholder="e.g. The Silent Patient")
-            with hc3:
-                h_domain = st.selectbox("Domain", DOMAINS, key=f"h_dom_{i}")
-            h_text = st.text_area("Review Text", key=f"h_text_{i}", height=80,
-                                  placeholder="Paste what this person wrote\u2026")
-            h_date = st.text_input("Date (Optional, e.g. 2024-03)", key=f"h_date_{i}",
-                                   placeholder="optional")
-            if h_text.strip():
-                hist_rows.append({"rating": h_rating, "title": h_title.strip(),
-                                  "domain": h_domain, "text": h_text.strip(),
-                                  "date": h_date.strip() or None})
-
-    st.markdown('<div class="sec-label" style="margin-top:0.8rem">'
-                'The New Product to Review</div>', unsafe_allow_html=True)
-    th1, th2 = st.columns([2, 1])
-    with th1:
-        ht_title = st.text_input("Title", value="The Midnight Library",
-                                 key="ht_title")
-    with th2:
-        ht_domain = st.selectbox("Domain ", DOMAINS, key="ht_domain")
-    ht_desc = st.text_area("Description / Synopsis", height=90, key="ht_desc",
-                           value="A novel about a library between life and death, "
-                                 "where each book lets a woman try a different "
-                                 "version of her life.")
-
-    go_hist = st.button("Build persona & generate review  ✶", key="go_hist",
-                        use_container_width=True)
-
-    if go_hist:
-        if not hist_rows:
-            st.warning("Add at least one past review with text so the agent "
-                       "has history to model.")
-        else:
-            try:
-                with st.status("The agent is working…", expanded=True) as status:
-                    st.write(f"Reading {len(hist_rows)} pasted review(s)…")
-                    persona = persona_from_reviews(hist_rows)
-                    st.write(f"Persona built by the agent from "
-                             f"{persona.n_reviews} reviews")
-                    item = ItemInput(parent_asin="pasted_target", title=ht_title,
-                                     description=ht_desc, categories="",
-                                     domain=ht_domain)
-                    st.write("Drafting in the inferred voice, then self-critiquing…")
-                    result = agent.run(persona, item, naija_mode=naija)
-                    st.write("Self-reflection complete")
-                    status.update(label="Review generated", state="complete")
-                st.session_state.result = result
-                st.session_state.ctx = {"persona": persona, "item": item,
-                                        "truth": None}
-            except Exception as e:
-                st.session_state.result = None
-                st.markdown(f'<div class="card" style="border-left:3px solid var(--clay)">'
-                            f'<div class="card-kicker">Generation interrupted</div>'
-                            f'The model call did not complete \u2014 it may be '
-                            f'rate-limited. Try again shortly.<br>'
-                            f'<span style="font-family:Spline Sans Mono,monospace;'
-                            f'font-size:0.72rem;color:#6f6651">'
-                            f'{esc(type(e).__name__)}</span></div>',
-                            unsafe_allow_html=True)
-
-
-# ══════════════════════════════════════════════════════════════════════════════
-# Result — shown below both tabs
-# ══════════════════════════════════════════════════════════════════════════════
-
-res = st.session_state.result
-ctx = st.session_state.ctx
-st.markdown("---")
-if res and ctx:
-    st.markdown(persona_card(ctx["persona"]), unsafe_allow_html=True)
-
-    it = ctx["item"]
-    st.markdown(f"""
-    <div class="card reveal d2">
-      <div class="card-kicker">The Item</div>
-      <span style="font-family:Spline Sans Mono,monospace;font-size:0.6rem;
-        letter-spacing:0.13em;text-transform:uppercase;color:var(--pine-2)">
-        {esc(it.domain)}</span>
-      <div style="font-family:Fraunces,serif;font-weight:600;font-size:1.14rem;
-        color:var(--ink);margin-top:0.1rem">{esc(it.title)}</div>
-    </div>""", unsafe_allow_html=True)
-
-    badge = '<span class="naija-badge">NAIJA VOICE</span>' if res.naija_mode else ""
-    st.markdown(f"""
-    <div class="panel reveal d3">
-      <div class="card-kicker">The Generated Review · written as the reader</div>
-      <div class="rating-row">
-        <span class="rating-chip">{res.rating:.1f}</span>
-        <span class="stars">{stars(res.rating)}</span>{badge}
-      </div>
-      <div class="review-body">{esc(res.review)}</div>
-    </div>""", unsafe_allow_html=True)
-
-    st.markdown(reflection_stepper(res.reflection_iterations,
-                                   res.reflection_refined,
-                                   res.reflection_notes), unsafe_allow_html=True)
-
-    st.markdown('<div class="sec-label">Why This Rating</div>', unsafe_allow_html=True)
-    truth = ctx.get("truth")
-    if truth:
-        col1, col2 = st.columns(2)
-        with col1:
-            st.markdown(f"""
-            <div class="cmp agent reveal d1">
-              <div class="cmp-head">The agent rated it {res.rating:.1f}★</div>
-              <div class="cmp-body">{esc(res.reasoning)}</div>
-            </div>""", unsafe_allow_html=True)
-        with col2:
-            d = abs(res.rating - truth["rating"])
-            dc = "good" if d <= 0.5 else ("mid" if d <= 1.0 else "far")
-            t = truth["text"].replace("<br />", "\n").replace("<br>", "\n")
-            t = t[:520] + ("…" if len(t) > 520 else "")
-            st.markdown(f"""
-            <div class="cmp truth reveal d2">
-              <div class="cmp-head">The reader actually wrote &nbsp;
-                <span class="delta {dc}">Δ {d:.1f}★</span></div>
-              <div style="margin:0.15rem 0 0.35rem">
-                <span class="stars" style="color:var(--pine-2)">{stars(truth['rating'])}</span>
-                <span style="font-family:Spline Sans Mono,monospace;font-size:0.74rem;
-                color:#6f6651"> {truth['rating']:.1f}★</span></div>
-              <div class="cmp-body">{esc(t)}</div>
-            </div>""", unsafe_allow_html=True)
-    else:
-        st.markdown(f"""
-        <div class="cmp agent reveal d1">
-          <div class="cmp-head">The agent rated it {res.rating:.1f}★</div>
-          <div class="cmp-body">{esc(res.reasoning)}</div>
-        </div>""", unsafe_allow_html=True)
-    st.caption(f"Grounded on {res.used_history_count} similar past reviews")
-else:
-    st.markdown('<div class="empty">Compose a persona and a product, or pick a '
-                'dataset reader — then press <b>Generate</b>. The agent writes '
-                'the review in that reader\'s voice and shows its reasoning.</div>',
-                unsafe_allow_html=True)
-
-st.markdown("""
-<div class="foot">
-  User Modeling Agent · DSN × BCT LLM Agent Challenge 2026 ·
-  persona → draft in-voice → self-reflection critique &amp; revise ·
-  rating predicted as persona prior adjusted by item evidence
-</div>
-""", unsafe_allow_html=True)