fourth commit

.env.example

@@ -0,0 +1,14 @@
+# Customs Compass - Environment Configuration
+#
+# Copy this file to `.env` and fill in your real values. The .env file
+# is loaded automatically when the app starts and is git-ignored.
+
+# --- Ollama (local LLM for the main Q&A assistant) ---
+OLLAMA_URL=http://localhost:11434
+OLLAMA_MODEL=llama3.2:3b
+
+# --- OrbitAI (used for premium agent features: Market Intelligence,
+#     Localization, GTM roadmap, etc.). Get your key from Orbit AI dashboard.
+ORBITAI_API_KEY=
+ORBITAI_BASE_URL=https://api.orbitai.global/v1
+ORBITAI_MODEL=gpt-5.4

.gitignore

@@ -3,3 +3,4 @@ __pycache__/
 .env
 venv/
 .venv/
+.streamlit/secrets.toml

app.py

@@ -7,6 +7,7 @@ electronics) exporting to the United States.
 from __future__ import annotations
 
 import json
+import os
 import re
 from html.parser import HTMLParser
 from pathlib import Path
@@ -16,23 +17,36 @@ import pandas as pd
 import requests
 import streamlit as st
 
+try:
+    from dotenv import load_dotenv
+    load_dotenv(Path(__file__).parent / ".env")
+except ImportError:
+    pass
+
 
 # =============================================================================
 # Section A — Configuration
 # =============================================================================
 
 st.set_page_config(
-    page_title="Customs Compass",
+    page_title="Customs Compass — AI Trade Compliance",
     page_icon="🧭",
     layout="wide",
     initial_sidebar_state="expanded",
 )
 
-OLLAMA_URL = "http://localhost:11434"
-OLLAMA_MODEL = "llama3.2:3b"
+OLLAMA_URL = os.getenv("OLLAMA_URL", "http://localhost:11434")
+OLLAMA_MODEL = os.getenv("OLLAMA_MODEL", "llama3.2:3b")
 OLLAMA_TIMEOUT = 30
 OLLAMA_PROBE_TIMEOUT = 2
 
+# OrbitAI — used for premium agent features (market intel, localization,
+# GTM roadmap). The main Q&A flow still uses local Ollama for privacy/cost.
+ORBITAI_API_KEY = os.getenv("ORBITAI_API_KEY", "")
+ORBITAI_BASE_URL = os.getenv("ORBITAI_BASE_URL", "https://api.orbitai.global/v1")
+ORBITAI_MODEL = os.getenv("ORBITAI_MODEL", "gpt-5.4")
+ORBITAI_TIMEOUT = 60
+
 CBP_NEWSROOM_URL = "https://www.cbp.gov/newsroom"
 CBP_TRADE_URL = "https://www.cbp.gov/trade"
 NEWS_FETCH_TIMEOUT = 5
@@ -42,6 +56,15 @@ USER_AGENT = "CustomsCompass/1.0 (Educational)"
 
 DATA_DIR = Path(__file__).parent
 
+# US customs fees (FY2026 rates)
+MPF_RATE = 0.003464   # Merchandise Processing Fee: 0.3464% ad valorem
+MPF_MIN = 32.71       # USD
+MPF_MAX = 634.62      # USD
+HMF_RATE = 0.00125    # Harbor Maintenance Fee: 0.125% (sea freight only)
+
+# Section 301 List-4A surcharge on most Chinese electronics
+SECTION_301_RATE = 0.25  # 25% additional ad valorem
+
 SYSTEM_PROMPT = (
     "You are Customs Compass, an AI assistant specialized in US sales tax, "
     "nexus thresholds, customs duties, and product compliance. Rules: "
@@ -697,6 +720,55 @@ def call_ollama(system_prompt: str, user_prompt: str) -> str:
     return data.get("message", {}).get("content", "").strip()
 
 
+# --- OrbitAI client (OpenAI-compatible) ---------------------------------------
+# Used for premium agent features that need a stronger model than llama3.2:3b
+# (market intelligence, localization, GTM roadmap generation, document analysis).
+
+def is_orbitai_configured() -> bool:
+    return bool(ORBITAI_API_KEY) and ORBITAI_API_KEY.startswith("sk-")
+
+
+def call_orbitai(
+    system_prompt: str,
+    user_prompt: str,
+    model: Optional[str] = None,
+    temperature: float = 0.4,
+) -> str:
+    """Call OrbitAI's OpenAI-compatible chat completions endpoint.
+
+    Raises requests.RequestException on network errors. Callers should catch
+    and fall back gracefully (typically to Ollama or a deterministic template).
+    """
+    if not is_orbitai_configured():
+        raise RuntimeError("ORBITAI_API_KEY is not set; cannot call OrbitAI.")
+    payload = {
+        "model": model or ORBITAI_MODEL,
+        "messages": [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
+        ],
+        "temperature": temperature,
+        "stream": False,
+    }
+    headers = {
+        "Authorization": f"Bearer {ORBITAI_API_KEY}",
+        "Content-Type": "application/json",
+        "User-Agent": USER_AGENT,
+    }
+    resp = requests.post(
+        f"{ORBITAI_BASE_URL.rstrip('/')}/chat/completions",
+        json=payload,
+        headers=headers,
+        timeout=ORBITAI_TIMEOUT,
+    )
+    resp.raise_for_status()
+    data = resp.json()
+    choices = data.get("choices") or []
+    if not choices:
+        return ""
+    return (choices[0].get("message") or {}).get("content", "").strip()
+
+
 # --- Fallback engine -----------------------------------------------------------
 
 RISK_BADGE = {
@@ -1031,39 +1103,49 @@ def render_response(answer: str, mode: str, payload: dict, news_items: list[dict
         })
 
     if payload.get("alerts"):
-        with st.expander(f"⚠️ CBP alerts triggered ({len(payload['alerts'])})", expanded=True):
-            for alert in payload["alerts"]:
-                st.markdown(
-                    f"**[{alert['severity']}] {alert['category']} — {alert['title']}**\n\n"
-                    f"{alert['summary']}\n\n"
-                    f"_Action required:_ {alert['action_required']}\n\n"
-                    f"[Source]({alert['source_url']})"
-                )
-                st.markdown("---")
+        st.markdown(f"#### ⚠️ CBP alerts triggered ({len(payload['alerts'])})")
+        import html as _html
+        for alert in payload["alerts"]:
+            sev = alert["severity"].lower()
+            st.markdown(
+                f"""
+<div class="cc-alert cc-alert-{sev}">
+  <div class="cc-alert-head">
+    <span class="cc-badge cc-badge-{sev}">{alert['severity']}</span>
+    <span>{alert['category']} — {alert['title']}</span>
+  </div>
+  <div class="cc-alert-body">{_html.escape(alert['summary'])}</div>
+  <div class="cc-alert-action">✅ <b>Action:</b> {_html.escape(alert['action_required'])}</div>
+  <div style="margin-top:6px"><a href="{alert['source_url']}" target="_blank">🔗 Source on cbp.gov</a></div>
+</div>
+                """,
+                unsafe_allow_html=True,
+            )
 
     if payload.get("chunks"):
-        with st.expander(f"📚 CBP knowledge base excerpts ({len(payload['chunks'])})", expanded=True):
-            st.caption(
-                "Full text from the most relevant CBP pages — no need to click out. "
-                "The link goes to the original source page on cbp.gov."
+        st.markdown(f"#### 📚 CBP knowledge base excerpts ({len(payload['chunks'])})")
+        st.caption(
+            "Full text from the most relevant CBP pages — no need to click out. The link goes to the source page."
+        )
+        import html as _html
+        for c in payload["chunks"]:
+            published = c.get("published_date", "")
+            meta = f"{c['section']} · {c['page_type']} · relevance {c['score']}"
+            if published:
+                meta += f" · {published}"
+            full = c.get("full_text") or c.get("excerpt", "")
+            safe = _html.escape(full)
+            st.markdown(
+                f"""
+<div class="cc-card">
+  <div class="cc-card-title">{_html.escape(c['title'])}</div>
+  <div class="cc-card-meta">{meta}</div>
+  <div class="cc-chunk">{safe}</div>
+  <div style="margin-top:10px"><a href="{c['url']}" target="_blank">🔗 View original page on cbp.gov</a></div>
+</div>
+                """,
+                unsafe_allow_html=True,
             )
-            for c in payload["chunks"]:
-                published = c.get("published_date", "")
-                meta = f"section: {c['section']} · type: {c['page_type']} · relevance: {c['score']}"
-                if published:
-                    meta += f" · {published}"
-                st.markdown(f"**{c['title']}**  \n_{meta}_")
-                full = c.get("full_text") or c.get("excerpt", "")
-                import html as _html
-                safe = _html.escape(full)
-                st.markdown(
-                    f"<div style='background:#f6f8fa;padding:12px;border-left:4px solid #4a90e2;"
-                    f"border-radius:4px;font-size:0.92em;line-height:1.5;white-space:pre-wrap'>"
-                    f"{safe}</div>",
-                    unsafe_allow_html=True,
-                )
-                st.markdown(f"🔗 [View original page on cbp.gov]({c['url']})")
-                st.markdown("---")
 
     if payload.get("news"):
         with st.expander(f"📰 Relevant CBP news ({len(payload['news'])})"):
@@ -1074,10 +1156,586 @@ def render_response(answer: str, mode: str, payload: dict, news_items: list[dict
 
 
 # =============================================================================
-# Section H — Main Entry Point
+# Section H — Premium Visual Polish (custom CSS + hero)
+# =============================================================================
+
+_CSS = """
+<style>
+/* ----------- Fonts & base ----------- */
+@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&family=JetBrains+Mono:wght@400;500&display=swap');
+
+html, body, [class*="css"], .stApp {
+    font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif;
+}
+
+code, pre, .stCode {
+    font-family: 'JetBrains Mono', monospace !important;
+}
+
+/* ----------- Hide default Streamlit chrome ----------- */
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+header[data-testid="stHeader"] {background: transparent;}
+
+/* ----------- Color tokens ----------- */
+:root {
+    --cc-primary: #6366F1;
+    --cc-primary-dark: #4F46E5;
+    --cc-accent: #EC4899;
+    --cc-surface: #F8FAFC;
+    --cc-border: #E2E8F0;
+    --cc-text: #0F172A;
+    --cc-muted: #64748B;
+    --cc-success: #10B981;
+    --cc-warning: #F59E0B;
+    --cc-danger: #EF4444;
+    --cc-critical: #DC2626;
+}
+
+/* ----------- Hero header ----------- */
+.cc-hero {
+    background: linear-gradient(135deg, #6366F1 0%, #8B5CF6 50%, #EC4899 100%);
+    border-radius: 16px;
+    padding: 28px 32px;
+    margin-bottom: 20px;
+    color: white;
+    box-shadow: 0 10px 30px -10px rgba(99,102,241,0.45);
+}
+.cc-hero h1 {
+    font-size: 2.0rem;
+    font-weight: 800;
+    margin: 0 0 4px 0;
+    color: white;
+    letter-spacing: -0.02em;
+}
+.cc-hero p {
+    margin: 0;
+    font-size: 1.0rem;
+    opacity: 0.95;
+    font-weight: 400;
+}
+.cc-hero-stats {
+    display: flex;
+    gap: 12px;
+    margin-top: 16px;
+    flex-wrap: wrap;
+}
+.cc-stat {
+    background: rgba(255,255,255,0.18);
+    backdrop-filter: blur(8px);
+    -webkit-backdrop-filter: blur(8px);
+    padding: 8px 14px;
+    border-radius: 100px;
+    font-size: 0.85rem;
+    font-weight: 500;
+    border: 1px solid rgba(255,255,255,0.25);
+}
+.cc-stat b { font-weight: 700; }
+
+/* ----------- Tabs ----------- */
+.stTabs [data-baseweb="tab-list"] {
+    gap: 4px;
+    border-bottom: 2px solid var(--cc-border);
+}
+.stTabs [data-baseweb="tab"] {
+    padding: 10px 18px;
+    border-radius: 8px 8px 0 0;
+    font-weight: 600;
+    color: var(--cc-muted);
+    background: transparent;
+}
+.stTabs [data-baseweb="tab"][aria-selected="true"] {
+    color: var(--cc-primary);
+    background: linear-gradient(180deg, rgba(99,102,241,0.06), transparent);
+    border-bottom: 2px solid var(--cc-primary);
+}
+
+/* ----------- Buttons ----------- */
+.stButton > button[kind="primary"] {
+    background: linear-gradient(135deg, #6366F1, #8B5CF6);
+    border: 0;
+    font-weight: 600;
+    padding: 0.6rem 1.3rem;
+    box-shadow: 0 4px 14px rgba(99,102,241,0.35);
+    transition: transform 0.15s ease, box-shadow 0.15s ease;
+}
+.stButton > button[kind="primary"]:hover {
+    transform: translateY(-1px);
+    box-shadow: 0 8px 24px rgba(99,102,241,0.45);
+}
+
+/* ----------- Card components (use via st.markdown + html) ----------- */
+.cc-card {
+    background: white;
+    border: 1px solid var(--cc-border);
+    border-radius: 12px;
+    padding: 18px 20px;
+    box-shadow: 0 1px 3px rgba(0,0,0,0.04);
+    margin-bottom: 12px;
+}
+.cc-card-title {
+    font-weight: 700;
+    font-size: 1rem;
+    margin-bottom: 4px;
+    color: var(--cc-text);
+}
+.cc-card-meta {
+    font-size: 0.78rem;
+    color: var(--cc-muted);
+    margin-bottom: 10px;
+    font-weight: 500;
+}
+.cc-card-body {
+    color: #334155;
+    font-size: 0.92rem;
+    line-height: 1.55;
+}
+
+/* ----------- Risk badges ----------- */
+.cc-badge {
+    display: inline-block;
+    padding: 4px 10px;
+    border-radius: 100px;
+    font-size: 0.78rem;
+    font-weight: 600;
+    letter-spacing: 0.02em;
+    text-transform: uppercase;
+}
+.cc-badge-low      { background: #D1FAE5; color: #047857; }
+.cc-badge-medium   { background: #FEF3C7; color: #92400E; }
+.cc-badge-high     { background: #FEE2E2; color: #B91C1C; }
+.cc-badge-critical { background: #DC2626; color: white; }
+.cc-badge-info     { background: #DBEAFE; color: #1E40AF; }
+.cc-badge-unknown  { background: #E2E8F0; color: #475569; }
+
+/* ----------- Alert cards (color-coded by severity) ----------- */
+.cc-alert {
+    border-left: 4px solid;
+    padding: 14px 18px;
+    border-radius: 8px;
+    margin-bottom: 12px;
+    background: white;
+    box-shadow: 0 1px 2px rgba(0,0,0,0.04);
+}
+.cc-alert-critical { border-color: var(--cc-critical); background: #FEF2F2; }
+.cc-alert-high     { border-color: var(--cc-danger);   background: #FFF7ED; }
+.cc-alert-medium   { border-color: var(--cc-warning);  background: #FFFBEB; }
+.cc-alert-info     { border-color: var(--cc-primary);  background: #EEF2FF; }
+
+.cc-alert-head {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    margin-bottom: 6px;
+    font-weight: 700;
+    color: var(--cc-text);
+}
+.cc-alert-body {
+    font-size: 0.9rem;
+    color: #334155;
+    line-height: 1.5;
+}
+.cc-alert-action {
+    margin-top: 8px;
+    padding: 8px 12px;
+    background: rgba(99,102,241,0.07);
+    border-radius: 6px;
+    font-size: 0.85rem;
+    color: #1E293B;
+}
+
+/* ----------- Big-number stat card for cost calculator ----------- */
+.cc-total-card {
+    background: linear-gradient(135deg, #6366F1, #8B5CF6);
+    color: white;
+    padding: 24px;
+    border-radius: 14px;
+    text-align: center;
+    box-shadow: 0 10px 25px -8px rgba(99,102,241,0.5);
+}
+.cc-total-card .label {
+    font-size: 0.85rem;
+    opacity: 0.9;
+    text-transform: uppercase;
+    letter-spacing: 0.08em;
+    font-weight: 500;
+}
+.cc-total-card .amount {
+    font-size: 2.4rem;
+    font-weight: 800;
+    margin: 4px 0;
+    letter-spacing: -0.02em;
+}
+.cc-total-card .markup {
+    font-size: 0.85rem;
+    opacity: 0.92;
+}
+
+/* ----------- Chunk excerpts ----------- */
+.cc-chunk {
+    background: #F8FAFC;
+    border-left: 4px solid var(--cc-primary);
+    border-radius: 6px;
+    padding: 12px 16px;
+    font-size: 0.9em;
+    line-height: 1.55;
+    white-space: pre-wrap;
+    color: #1E293B;
+    margin: 8px 0;
+}
+
+/* ----------- Sidebar improvements ----------- */
+[data-testid="stSidebar"] {
+    background: linear-gradient(180deg, #0F172A 0%, #1E293B 100%);
+}
+[data-testid="stSidebar"] * { color: #E2E8F0 !important; }
+[data-testid="stSidebar"] h1, [data-testid="stSidebar"] h2, [data-testid="stSidebar"] h3 {
+    color: white !important;
+}
+[data-testid="stSidebar"] a { color: #A5B4FC !important; }
+[data-testid="stSidebar"] .stButton > button {
+    background: rgba(255,255,255,0.08);
+    color: white !important;
+    border: 1px solid rgba(255,255,255,0.12);
+    font-weight: 500;
+}
+[data-testid="stSidebar"] .stButton > button:hover {
+    background: rgba(99,102,241,0.3);
+    border-color: var(--cc-primary);
+}
+
+/* ----------- Inputs ----------- */
+.stTextInput input, .stTextArea textarea, .stNumberInput input, .stSelectbox > div > div {
+    border-radius: 8px !important;
+    border: 1px solid var(--cc-border) !important;
+}
+
+/* ----------- Misc polish ----------- */
+.cc-divider {
+    height: 1px;
+    background: var(--cc-border);
+    margin: 18px 0;
+}
+.cc-pill {
+    display: inline-block;
+    padding: 3px 10px;
+    background: var(--cc-surface);
+    border: 1px solid var(--cc-border);
+    border-radius: 100px;
+    font-size: 0.75rem;
+    color: var(--cc-muted);
+    font-weight: 500;
+}
+</style>
+"""
+
+
+def inject_custom_css() -> None:
+    st.markdown(_CSS, unsafe_allow_html=True)
+
+
+def render_hero(nexus_count: int, alerts_count: int, chunks_count: int, ollama_ok: bool) -> None:
+    ai_chip = "🟢 AI online" if ollama_ok else "🟡 Fallback mode"
+    st.markdown(
+        f"""
+<div class="cc-hero">
+  <h1>🧭 Customs Compass</h1>
+  <p>AI compliance copilot for Chinese exporters entering the US market —
+     sales tax, customs duties, federal certifications, and CBP enforcement.</p>
+  <div class="cc-hero-stats">
+    <span class="cc-stat">🇺🇸 <b>{nexus_count}</b> states + DC</span>
+    <span class="cc-stat">⚠️ <b>{alerts_count}</b> CBP alerts</span>
+    <span class="cc-stat">📚 <b>{chunks_count}</b> RAG chunks</span>
+    <span class="cc-stat">{ai_chip}</span>
+  </div>
+</div>
+        """,
+        unsafe_allow_html=True,
+    )
+
+
+# =============================================================================
+# Section I — Landed Cost Calculator
+# =============================================================================
+
+def _parse_duty_rate(rate_str: str) -> float:
+    """Parse '3.4%', 'Free', '2.6%' → 0.034, 0.0, 0.026."""
+    if not rate_str:
+        return 0.0
+    rate_str = str(rate_str).strip().lower()
+    if rate_str in ("free", "0", "0%", "n/a", "none"):
+        return 0.0
+    m = re.search(r"(\d+(?:\.\d+)?)", rate_str)
+    if not m:
+        return 0.0
+    val = float(m.group(1))
+    return val / 100.0 if "%" in rate_str or val > 1 else val
+
+
+def compute_landed_cost(
+    customs_value_usd: float,
+    duty_rate_str: str,
+    apply_section_301: bool,
+    shipping_usd: float,
+    insurance_usd: float,
+    destination_state: str,
+    tax_rates: dict,
+    use_sea_freight: bool = True,
+) -> dict:
+    """Compute the full landed cost breakdown for a Chinese export to the US.
+
+    Returns a dict with every line item plus the final total.
+    """
+    duty_pct = _parse_duty_rate(duty_rate_str)
+    base_duty = customs_value_usd * duty_pct
+    s301_duty = customs_value_usd * SECTION_301_RATE if apply_section_301 else 0.0
+
+    mpf = max(MPF_MIN, min(MPF_MAX, customs_value_usd * MPF_RATE))
+    hmf = customs_value_usd * HMF_RATE if use_sea_freight else 0.0
+
+    customs_total = base_duty + s301_duty + mpf + hmf
+
+    cif = customs_value_usd + shipping_usd + insurance_usd
+    state_tax_pct = float(tax_rates.get(destination_state, 0) or 0) / 100.0
+    sales_tax = (cif + customs_total) * state_tax_pct
+
+    total = cif + customs_total + sales_tax
+    markup = (total / customs_value_usd - 1) * 100 if customs_value_usd > 0 else 0.0
+
+    return {
+        "customs_value": customs_value_usd,
+        "base_duty": base_duty,
+        "base_duty_pct": duty_pct,
+        "section_301_duty": s301_duty,
+        "section_301_applied": apply_section_301,
+        "mpf": mpf,
+        "hmf": hmf,
+        "shipping": shipping_usd,
+        "insurance": insurance_usd,
+        "cif": cif,
+        "customs_total": customs_total,
+        "sales_tax": sales_tax,
+        "sales_tax_pct": state_tax_pct,
+        "destination_state": destination_state,
+        "total_landed": total,
+        "markup_pct": markup,
+    }
+
+
+def render_cost_calculator_tab(hts_df: pd.DataFrame, tax_rates: dict, alerts_df: pd.DataFrame) -> None:
+    st.markdown("### 💰 Landed Cost Calculator")
+    st.caption(
+        "Compute the complete US import cost: customs duties + Section 301 + MPF + HMF + shipping + state sales tax."
+    )
+
+    col1, col2 = st.columns([1, 1])
+
+    with col1:
+        st.markdown("#### Product")
+        categories = hts_df["product_category"].tolist() if not hts_df.empty else []
+        category = st.selectbox(
+            "Product category",
+            options=categories,
+            index=0 if categories else None,
+            help="Picks the HTS code & duty rate from hts_duty_codes.csv",
+        )
+        customs_value = st.number_input(
+            "Customs value (FOB, USD)",
+            min_value=0.0,
+            value=10000.0,
+            step=500.0,
+            help="The declared price of goods at the port of export (before shipping).",
+        )
+        units = st.number_input(
+            "Number of units (optional)", min_value=1, value=100, step=10,
+            help="Used to display per-unit landed cost.",
+        )
+
+    with col2:
+        st.markdown("#### Shipping & destination")
+        origin_china = st.toggle(
+            "Origin: China 🇨🇳",
+            value=True,
+            help="If ON, applies +25% Section 301 surcharge to electronics-class HTS codes.",
+        )
+        use_sea = st.toggle(
+            "Sea freight (adds Harbor Maintenance Fee)",
+            value=True,
+            help="0.125% HMF applies to sea/water imports; air shipments are exempt.",
+        )
+        shipping = st.number_input("Shipping cost (USD)", min_value=0.0, value=800.0, step=50.0)
+        insurance = st.number_input("Insurance (USD)", min_value=0.0, value=100.0, step=25.0)
+        state_options = sorted(tax_rates.keys()) if tax_rates else ["Texas"]
+        destination = st.selectbox(
+            "Destination state",
+            options=state_options,
+            index=state_options.index("Texas") if "Texas" in state_options else 0,
+        )
+
+    # Look up duty rate for selected category
+    duty_rate_str = "0%"
+    notes = ""
+    fcc = ul_ = fda = ""
+    if category and not hts_df.empty:
+        row = hts_df[hts_df["product_category"] == category]
+        if not row.empty:
+            r = row.iloc[0]
+            duty_rate_str = r["duty_rate"]
+            notes = r["notes"]
+            fcc, ul_, fda = r["fcc_needed"], r["ul_needed"], r["fda_needed"]
+
+    breakdown = compute_landed_cost(
+        customs_value_usd=customs_value,
+        duty_rate_str=duty_rate_str,
+        apply_section_301=origin_china,
+        shipping_usd=shipping,
+        insurance_usd=insurance,
+        destination_state=destination,
+        tax_rates=tax_rates,
+        use_sea_freight=use_sea,
+    )
+
+    st.markdown('<div class="cc-divider"></div>', unsafe_allow_html=True)
+
+    # --- Big total card ---
+    per_unit = breakdown["total_landed"] / units if units > 0 else 0
+    st.markdown(
+        f"""
+<div class="cc-total-card">
+  <div class="label">Estimated total landed cost</div>
+  <div class="amount">${breakdown['total_landed']:,.2f}</div>
+  <div class="markup">{breakdown['markup_pct']:+.1f}% over FOB · ~${per_unit:,.2f} per unit ({units:,} units)</div>
+</div>
+        """,
+        unsafe_allow_html=True,
+    )
+
+    # --- Breakdown table ---
+    st.markdown("#### Breakdown")
+    rows = [
+        ("Customs value (FOB)", breakdown["customs_value"], ""),
+        ("Shipping", breakdown["shipping"], ""),
+        ("Insurance", breakdown["insurance"], ""),
+        ("→ CIF subtotal", breakdown["cif"], ""),
+        (f"Base customs duty ({breakdown['base_duty_pct']*100:.2f}%)", breakdown["base_duty"], f"HTS {category}"),
+    ]
+    if breakdown["section_301_applied"]:
+        rows.append(("Section 301 surcharge (+25%)", breakdown["section_301_duty"], "China-origin electronics"))
+    rows.extend([
+        ("Merchandise Processing Fee (MPF)", breakdown["mpf"], f"0.3464%, min $32.71 max $634.62"),
+    ])
+    if use_sea:
+        rows.append(("Harbor Maintenance Fee (HMF)", breakdown["hmf"], "0.125% (sea freight only)"))
+    rows.extend([
+        ("→ Customs total", breakdown["customs_total"], ""),
+        (f"State sales tax — {destination} ({breakdown['sales_tax_pct']*100:.2f}%)", breakdown["sales_tax"], ""),
+        ("→ TOTAL LANDED COST", breakdown["total_landed"], ""),
+    ])
+
+    df_show = pd.DataFrame(
+        [{"Line item": r[0], "Amount (USD)": f"${r[1]:,.2f}", "Notes": r[2]} for r in rows]
+    )
+    st.dataframe(df_show, use_container_width=True, hide_index=True)
+
+    # --- Compliance flags ---
+    compliance_flags = []
+    if str(fcc).strip().lower() == "yes":
+        compliance_flags.append("📡 FCC certification required")
+    if str(ul_).strip().lower() == "yes":
+        compliance_flags.append("⚡ UL listing required")
+    if str(fda).strip().lower() == "yes":
+        compliance_flags.append("🩺 FDA clearance required")
+    if origin_china:
+        compliance_flags.append("⚠️ UFLPA documentation required (supply chain affidavits)")
+
+    if compliance_flags:
+        st.markdown("#### Compliance flags")
+        for f in compliance_flags:
+            st.markdown(f"- {f}")
+
+    if notes:
+        st.caption(f"📝 HTS notes: {notes}")
+
+    st.caption(
+        "💡 Estimates are educational. Final duties depend on the exact 10-digit HTS code, "
+        "current Federal Register tariff actions, and CBP classification rulings."
+    )
+
+
+# =============================================================================
+# Section J — Knowledge Base browser tab
+# =============================================================================
+
+def render_knowledge_tab(alerts_df: pd.DataFrame, chunk_index: dict) -> None:
+    st.markdown("### 📚 Knowledge Base Browser")
+    st.caption(
+        "Browse the full curated alerts list and the underlying CBP RAG corpus that powers the assistant."
+    )
+
+    sub1, sub2 = st.tabs(["⚠️ CBP Alerts", "📄 CBP Pages (RAG corpus)"])
+
+    with sub1:
+        if alerts_df.empty:
+            st.info("No alerts loaded.")
+        else:
+            severity_filter = st.multiselect(
+                "Filter by severity",
+                options=["Critical", "High", "Medium", "Info"],
+                default=["Critical", "High", "Medium"],
+            )
+            filtered = alerts_df[alerts_df["severity"].isin(severity_filter)]
+            st.caption(f"Showing {len(filtered)} / {len(alerts_df)} alerts.")
+            for _, row in filtered.iterrows():
+                sev = row["severity"].lower()
+                st.markdown(
+                    f"""
+<div class="cc-alert cc-alert-{sev}">
+  <div class="cc-alert-head">
+    <span class="cc-badge cc-badge-{sev}">{row['severity']}</span>
+    <span>{row['category']} — {row['title']}</span>
+  </div>
+  <div class="cc-alert-body">{row['summary']}</div>
+  <div class="cc-alert-action">✅ <b>Action:</b> {row['action_required']}</div>
+  <div style="margin-top:6px"><a href="{row['source_url']}" target="_blank">🔗 Source on cbp.gov</a></div>
+</div>
+                    """,
+                    unsafe_allow_html=True,
+                )
+
+    with sub2:
+        chunks = chunk_index.get("chunks") or []
+        st.caption(f"{len(chunks)} substantive CBP chunks indexed ({chunk_index.get('skipped_noise', 0)} noise chunks filtered out).")
+        # Group by parent page
+        by_parent: dict[str, list[dict]] = {}
+        for c in chunks:
+            pid = c.get("parent_id", c.get("chunk_id"))
+            by_parent.setdefault(pid, []).append(c)
+        # Sort by title for browsability
+        sorted_parents = sorted(by_parent.items(), key=lambda kv: (kv[1][0].get("title") or "").lower())
+        query = st.text_input("Filter pages by title", placeholder="e.g. UFLPA, Section 301, IPR")
+        for pid, parent_chunks in sorted_parents:
+            title = parent_chunks[0].get("title", pid)
+            url = parent_chunks[0].get("url", "")
+            if query and query.lower() not in title.lower():
+                continue
+            with st.expander(f"📄 {title} ({len(parent_chunks)} chunks)"):
+                st.markdown(f"🔗 [{url}]({url})")
+                for c in parent_chunks[:3]:
+                    import html as _html
+                    safe = _html.escape((c.get("text") or "")[:1200])
+                    st.markdown(f'<div class="cc-chunk">{safe}…</div>', unsafe_allow_html=True)
+                if len(parent_chunks) > 3:
+                    st.caption(f"… and {len(parent_chunks)-3} more chunks (collapsed)")
+
+
+# =============================================================================
+# Section K — Main Entry Point
 # =============================================================================
 
 def main() -> None:
+    # Inject premium CSS first so everything renders polished
+    inject_custom_css()
+
     # Load data
     try:
         nexus_df = load_nexus_thresholds()
@@ -1128,29 +1786,54 @@ def main() -> None:
 
     if sidebar_state["example_clicked"]:
         st.session_state["prefilled"] = sidebar_state["example_clicked"]
+        # Switch to Analyze tab when clicking an example
+        st.session_state["active_tab"] = "analyze"
         st.rerun()
 
-    form = render_main_form(prefilled_question=st.session_state.get("prefilled", ""))
-
-    if form["submitted"]:
-        combined = (form["product_desc"] + "\n" + form["question"]).strip()
-        if not combined:
-            st.warning("Please enter a product description or a question.")
-            return
-
-        with st.spinner("Analyzing… (consulting knowledge base and LLM)"):
-            answer, mode, payload = get_answer(
-                question=combined,
-                nexus_df=nexus_df,
-                hts_df=hts_df,
-                tax_rates=tax_rates,
-                news_items=news_items,
-                alerts_df=alerts_df,
-                chunk_index=chunk_index,
-                force_fallback=sidebar_state["force_fallback"],
-            )
+    # Hero header
+    render_hero(
+        nexus_count=len(nexus_df),
+        alerts_count=len(alerts_df),
+        chunks_count=chunk_index.get("N", 0),
+        ollama_ok=ollama_ok,
+    )
+
+    tab_analyze, tab_cost, tab_kb = st.tabs([
+        "🔍  Analyze",
+        "💰  Landed Cost Calculator",
+        "📚  Knowledge Base",
+    ])
+
+    # ---- Tab 1: Analyze (Q&A flow) ----
+    with tab_analyze:
+        form = render_main_form(prefilled_question=st.session_state.get("prefilled", ""))
+
+        if form["submitted"]:
+            combined = (form["product_desc"] + "\n" + form["question"]).strip()
+            if not combined:
+                st.warning("Please enter a product description or a question.")
+            else:
+                with st.spinner("Analyzing… (consulting knowledge base and LLM)"):
+                    answer, mode, payload = get_answer(
+                        question=combined,
+                        nexus_df=nexus_df,
+                        hts_df=hts_df,
+                        tax_rates=tax_rates,
+                        news_items=news_items,
+                        alerts_df=alerts_df,
+                        chunk_index=chunk_index,
+                        force_fallback=sidebar_state["force_fallback"],
+                    )
+
+                render_response(answer, mode, payload, news_items)
+
+    # ---- Tab 2: Cost Calculator ----
+    with tab_cost:
+        render_cost_calculator_tab(hts_df, tax_rates, alerts_df)
 
-        render_response(answer, mode, payload, news_items)
+    # ---- Tab 3: Knowledge Base ----
+    with tab_kb:
+        render_knowledge_tab(alerts_df, chunk_index)
 
 
 if __name__ == "__main__":

requirements.txt

@@ -2,3 +2,4 @@ streamlit>=1.30
 pandas>=2.0
 requests>=2.31
 beautifulsoup4
+python-dotenv>=1.0