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Updates/app_hf_fixed_v7.py

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+import os
+import re
+import json
+import math
+import hashlib
+import tempfile
+from dataclasses import dataclass
+from datetime import datetime, date
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+
+import fitz  # PyMuPDF
+import faiss
+from sentence_transformers import SentenceTransformer
+from rapidfuzz import fuzz, process
+
+import gradio as gr
+from openai import OpenAI
+
+
+# ============================
+# Settings
+# ============================
+TODAY = date(2026, 1, 18)
+OPENAI_MODEL = "gpt-5.2"
+OPENAI_REASONING = {"effort": "high"}
+MATCH_OK = 80
+
+EMBED_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
+PARSEC_CONTEXT_BEFORE = 900
+PARSEC_CONTEXT_AFTER = 1600
+
+
+# ============================
+# OpenAI client (HF Space secret: OPENAI_API_KEY)
+# ============================
+API_KEY = os.getenv("OPENAI_API_KEY", "").strip()
+client = OpenAI(api_key=API_KEY) if API_KEY else None
+
+# ----------------------------
+# Gradio state helpers
+# Keep state as a JSON STRING to avoid schema issues on Hugging Face.
+# ----------------------------
+def state_load(st_json: str) -> Dict[str, Any]:
+    try:
+        if not st_json:
+            return {}
+        return json.loads(st_json) if isinstance(st_json, str) else {}
+    except Exception:
+        return {}
+
+def state_dump(st: Dict[str, Any]) -> str:
+    try:
+        return json.dumps(st or {}, ensure_ascii=False)
+    except Exception:
+        return "{}"
+
+
+
+# ============================
+# Helpers
+# ============================
+def norm_text(s: Any) -> str:
+    try:
+        if s is None or (isinstance(s, float) and math.isnan(s)) or pd.isna(s):
+            return ""
+    except Exception:
+        pass
+    s = str(s).strip().lower()
+    s = re.sub(r"[^a-z0-9\s\-\/]", " ", s)
+    s = re.sub(r"\s+", " ", s).strip()
+    return s
+
+def safe_str(v: Any) -> str:
+    if v is None or (isinstance(v, float) and pd.isna(v)) or pd.isna(v):
+        return ""
+    return str(v).strip()
+
+def is_5g(modem_type: Any) -> bool:
+    s = norm_text(modem_type)
+    return ("5g" in s) or ("nr" in s)
+
+def json_load_safe(s: str) -> Dict[str, Any]:
+    try:
+        return json.loads(s)
+    except Exception:
+        return {}
+
+def gpt_json(system: str, payload: Dict[str, Any], max_tokens: int = 600) -> Dict[str, Any]:
+    if client is None:
+        return {}
+    resp = client.responses.create(
+        model=OPENAI_MODEL,
+        reasoning=OPENAI_REASONING,
+        input=[{"role":"system","content":system},{"role":"user","content":json.dumps(payload)}],
+        max_output_tokens=max_tokens,
+    )
+    return json_load_safe(getattr(resp, "output_text", "") or "")
+
+
+# ============================
+# Load data
+# ============================
+EOS_PATH = "routers_eos_eol_by_sku.csv"
+DEC_PATH = "dec2025routers.csv"
+PARSEC_PDF = "ParsecCatalog.pdf"
+
+if not os.path.exists(EOS_PATH):
+    raise FileNotFoundError(f"Missing {EOS_PATH} in repo.")
+if not os.path.exists(DEC_PATH):
+    raise FileNotFoundError(f"Missing {DEC_PATH} in repo.")
+if not os.path.exists(PARSEC_PDF):
+    raise FileNotFoundError(f"Missing {PARSEC_PDF} in repo.")
+
+df_eos = pd.read_csv(EOS_PATH).copy()
+df_dec = pd.read_csv(DEC_PATH).copy()
+
+
+def _canonize_eos_columns(df: pd.DataFrame) -> pd.DataFrame:
+    """Normalize lifecycle CSV column names (case-insensitive) and create expected columns."""
+    # Map various header spellings to canonical names used by the app
+    mapping = {}
+    for c in df.columns:
+        k = str(c).strip().lower().replace(" ", "_")
+        if k in {"sku", "model", "device", "device_sku"}:
+            mapping[c] = "sku"
+        elif k in {"manufacturer", "make", "vendor"}:
+            mapping[c] = "manufacturer"
+        elif k in {"device_type", "type"}:
+            mapping[c] = "device_type"
+        elif k in {"end_of_sale", "eos", "end_sale", "end_of_sales"}:
+            mapping[c] = "end_of_sale"
+        elif k in {"end_of_life", "eol", "end_life"}:
+            mapping[c] = "end_of_life"
+        elif k in {"suggested_replacement", "replacement_4g", "lte_replacement", "replacement_lte", "replacement"}:
+            mapping[c] = "suggested_replacement"
+        elif k in {"advanced_5g_option", "replacement_5g", "fiveg_replacement", "5g_replacement", "upgrade_5g"}:
+            mapping[c] = "advanced_5g_option"
+        elif k in {"region", "market"}:
+            mapping[c] = "region"
+        elif k in {"notes", "note"}:
+            mapping[c] = "notes"
+        elif k in {"description", "device_description", "name"}:
+            mapping[c] = "description"
+
+    df = df.rename(columns=mapping).copy()
+
+    # Create expected columns if missing
+    if "sku" not in df.columns:
+        # Try the common capitalized header as a fallback
+        if "SKU" in df.columns:
+            df["sku"] = df["SKU"].astype(str)
+        else:
+            df["sku"] = ""
+
+    if "manufacturer" not in df.columns:
+        df["manufacturer"] = ""
+
+    if "device_type" not in df.columns:
+        df["device_type"] = ""
+
+    if "description" not in df.columns:
+        # If the simplified file removed description, use SKU as description (still searchable)
+        df["description"] = df["sku"].astype(str)
+
+    if "notes" not in df.columns:
+        df["notes"] = ""
+
+    if "region" not in df.columns:
+        df["region"] = ""
+
+    if "suggested_replacement" not in df.columns:
+        df["suggested_replacement"] = ""
+
+    if "advanced_5g_option" not in df.columns:
+        df["advanced_5g_option"] = ""
+
+    if "end_of_sale" not in df.columns:
+        df["end_of_sale"] = ""
+
+    if "end_of_life" not in df.columns:
+        df["end_of_life"] = ""
+
+    return df
+
+df_eos = _canonize_eos_columns(df_eos)
+
+
+def region_ok(x: Any) -> bool:
+    s = str(x or "").strip().lower()
+    if not s:
+        return True
+    if "not specified" in s:
+        return True
+    if "north america" in s:
+        return True
+    if re.search(r"\busa\b", s):
+        return True
+    if re.search(r"\bunited\s+states\b", s):
+        return True
+    if re.search(r"\bu\.?s\.?\b", s):
+        return True
+    return False
+
+if "region" in df_eos.columns:
+    df_eos = df_eos[df_eos["region"].apply(region_ok)].reset_index(drop=True)
+
+# Maker mapping (includes Teltonika)
+CANON_MAKER = {
+    "CRADLEPOINT": {"cradlepoint", "ericsson", "ericsson enterprise wireless"},
+    "SIERRA": {"sierra", "sierra wireless", "semtech", "airlink"},
+    "FEENEY": {"feeney", "feeney wireless", "inseego"},
+    "DIGI": {"digi", "accelerated", "accelerated concepts"},
+    "CISCO_MERAKI": {"meraki", "cisco meraki"},
+    "CISCO": {"cisco"},
+    "TELTONIKA": {"teltonika"},
+}
+
+def canon_maker_from_text(s: Any) -> str:
+    t = norm_text(s)
+    for canon, terms in CANON_MAKER.items():
+        for term in terms:
+            if term in t:
+                return canon
+    return "UNKNOWN"
+
+df_eos["_canon_make"] = df_eos["manufacturer"].apply(canon_maker_from_text) if "manufacturer" in df_eos.columns else "UNKNOWN"
+df_eos["_norm_sku"] = df_eos["sku"].apply(norm_text) if "sku" in df_eos.columns else ""
+df_eos["_norm_desc"] = df_eos["description"].apply(norm_text) if "description" in df_eos.columns else ""
+df_eos["_norm_notes"] = df_eos["notes"].apply(norm_text) if "notes" in df_eos.columns else ""
+
+df_dec["_canon_make"] = df_dec["Make"].apply(canon_maker_from_text) if "Make" in df_dec.columns else "UNKNOWN"
+df_dec["_norm_model"] = df_dec["Model"].apply(norm_text) if "Model" in df_dec.columns else ""
+df_dec["_is5g"] = df_dec["Modem Type"].apply(is_5g) if "Modem Type" in df_dec.columns else False
+
+
+# ============================
+# Date helpers
+# ============================
+@dataclass
+class ParsedDate:
+    raw: str
+    kind: str
+    value: Optional[date]
+
+def parse_date_field(x: Any) -> ParsedDate:
+    raw = str(x or "").strip()
+    if not raw:
+        return ParsedDate(raw="", kind="missing", value=None)
+
+    # Common US formats: M/D/YY or M/D/YYYY (e.g., 6/24/24, 9/30/21)
+    for fmt in ("%m/%d/%y", "%m/%d/%Y", "%-m/%-d/%y", "%-m/%-d/%Y"):
+        try:
+            dt = datetime.strptime(raw, fmt).date()
+            return ParsedDate(raw=raw, kind="full", value=dt)
+        except Exception:
+            pass
+
+    # ISO-ish: YYYY
+    if re.fullmatch(r"\d{4}", raw):
+        y = int(raw)
+        if y == TODAY.year:
+            return ParsedDate(raw=raw, kind="year", value=date(y, 1, 1))
+        if y < TODAY.year:
+            return ParsedDate(raw=raw, kind="year", value=date(y, 1, 1))
+        return ParsedDate(raw=raw, kind="year", value=date(y, 12, 31))
+
+    # YYYY-MM
+    if re.fullmatch(r"\d{4}-\d{2}", raw):
+        try:
+            y, m = raw.split("-")
+            return ParsedDate(raw=raw, kind="year_month", value=date(int(y), int(m), 1))
+        except Exception:
+            return ParsedDate(raw=raw, kind="bad", value=None)
+
+    # YYYY-MM-DD
+    if re.fullmatch(r"\d{4}-\d{2}-\d{2}", raw):
+        try:
+            dt = datetime.strptime(raw, "%Y-%m-%d").date()
+            return ParsedDate(raw=raw, kind="full", value=dt)
+        except Exception:
+            return ParsedDate(raw=raw, kind="bad", value=None)
+
+    # Last resort: leave as raw (unparsed)
+    return ParsedDate(raw=raw, kind="bad", value=None)
+
+    if re.fullmatch(r"\d{4}-\d{2}-\d{2}", raw):
+        try:
+            dt = datetime.strptime(raw, "%Y-%m-%d").date()
+            return ParsedDate(raw=raw, kind="full", value=dt)
+        except Exception:
+            return ParsedDate(raw=raw, kind="bad", value=None)
+
+    return ParsedDate(raw=raw, kind="bad", value=None)
+
+def display_date(pd_: ParsedDate) -> str:
+    if pd_.kind == "missing":
+        return "Not listed"
+    if pd_.kind == "bad":
+        return pd_.raw or "Not listed"
+    return pd_.raw
+
+def status_from_eos_eol(eos: ParsedDate, eol: ParsedDate) -> str:
+    if eos.value is None and eol.value is None:
+        return "Unknown"
+    if eol.value is not None and eol.value <= TODAY:
+        return "End of Life"
+    if eos.value is not None and eos.value <= TODAY:
+        return "End of Sale"
+    return "Active"
+
+def row_to_dates_and_status(row: pd.Series) -> Tuple[str, str, str]:
+    eos = parse_date_field(row.get("end_of_sale"))
+    eol = parse_date_field(row.get("end_of_life"))
+    return display_date(eos), display_date(eol), status_from_eos_eol(eos, eol)
+
+
+# ============================
+# Embeddings + Parsec index
+# ============================
+embedder = SentenceTransformer(EMBED_MODEL_NAME)
+
+def extract_pdf_text_pages(path: str) -> List[str]:
+    doc = fitz.open(path)
+    return [doc[i].get_text("text") for i in range(len(doc))]
+
+def build_parsec_cards(pages: List[str]) -> List[str]:
+    cards = []
+    for p in pages:
+        for m in re.finditer(r"Standard\s+SKU:", p):
+            start = max(0, m.start() - PARSEC_CONTEXT_BEFORE)
+            end = min(len(p), m.start() + PARSEC_CONTEXT_AFTER)
+            c = p[start:end].strip()
+            if len(c) >= 200:
+                cards.append(c)
+    out, seen = [], set()
+    for c in cards:
+        h = hashlib.sha1(c.encode("utf-8")).hexdigest()
+        if h not in seen:
+            seen.add(h); out.append(c)
+    return out
+
+parsec_cards = build_parsec_cards(extract_pdf_text_pages(PARSEC_PDF))
+parsec_emb = embedder.encode(parsec_cards, batch_size=64, show_progress_bar=False, normalize_embeddings=True)
+parsec_emb = np.asarray(parsec_emb, dtype=np.float32)
+parsec_index = faiss.IndexFlatIP(parsec_emb.shape[1])
+parsec_index.add(parsec_emb)
+
+
+# ============================
+# Device resolution
+# ============================
+def label_for_row(i: int) -> str:
+    r = df_eos.iloc[i]
+    return f"{r.get('sku','')} — {r.get('manufacturer','')} — {r.get('description','')}"[:220]
+
+EOS_LABELS = [label_for_row(i) for i in range(len(df_eos))]
+EOS_CORPUS = []
+for _, r in df_eos.iterrows():
+    EOS_CORPUS.append(" ".join([r.get("_norm_sku",""), r.get("_canon_make",""), r.get("_norm_desc",""), r.get("_norm_notes","")]))
+
+def local_candidates(query: str, top_k: int = 6) -> List[Tuple[int, int, str]]:
+    q = norm_text(query)
+    hits = process.extract(q, EOS_CORPUS, scorer=fuzz.WRatio, limit=top_k)
+    return [(int(idx), int(score), EOS_LABELS[int(idx)]) for _, score, idx in hits]
+
+def gpt_choose_device(user_text: str, candidates: List[Tuple[int,int,str]]) -> Dict[str, Any]:
+    if client is None:
+        return {}
+    sys = "Pick which router the user meant. Never invent. Return strict JSON only."
+    payload = {
+        "user_input": user_text,
+        "candidates": [{"row_idx": i, "score": s, "label": lbl} for (i,s,lbl) in candidates],
+        "rules": [
+            "If one is clearly correct, return mode='ok' with row_idx.",
+            "If two are plausible, return mode='pick' with top 2 options."
+        ],
+        "output_schema": {"mode":"ok|pick","row_idx":"int","options":[{"row_idx":"int","label":"string"}]}
+    }
+    return gpt_json(sys, payload, max_tokens=280)
+
+def resolve_device(user_text: str) -> Dict[str, Any]:
+    q = norm_text(user_text)
+    exact = df_eos.index[df_eos["_norm_sku"] == q].tolist()
+    if len(exact) == 1:
+        return {"mode":"ok","row_idx": int(exact[0])}
+    if len(exact) > 1:
+        opts = [{"row_idx": int(i), "label": EOS_LABELS[int(i)]} for i in exact[:2]]
+        return {"mode":"pick","options": opts}
+
+    cands = local_candidates(user_text, top_k=6)
+    if not cands:
+        return {"mode":"not_found"}
+
+    if cands[0][1] >= 95 and (len(cands) == 1 or (cands[0][1] - cands[1][1]) >= 8):
+        return {"mode":"ok","row_idx": cands[0][0]}
+
+    g = gpt_choose_device(user_text, cands)
+    if g.get("mode") == "ok" and isinstance(g.get("row_idx"), int):
+        return {"mode":"ok","row_idx": int(g["row_idx"])}
+
+    if g.get("mode") == "pick":
+        opts = g.get("options", []) or []
+        opts2 = [{"row_idx": int(o["row_idx"]), "label": str(o["label"])} for o in opts[:2] if "row_idx" in o]
+        if opts2:
+            return {"mode":"pick","options": opts2}
+
+    if len(cands) > 1:
+        return {"mode":"pick","options":[{"row_idx":cands[0][0],"label":cands[0][2]},{"row_idx":cands[1][0],"label":cands[1][2]}]}
+    return {"mode":"pick","options":[{"row_idx":cands[0][0],"label":cands[0][2]}]}
+
+
+# ============================
+# Replacements — lifecycle CSV source of truth
+# ============================
+def extract_model_token(text: str) -> str:
+    s = safe_str(text)
+    if not s:
+        return ""
+    parts = [p.strip() for p in s.split("|") if p.strip()]
+    candidates = parts[::-1] if parts else [s]
+    for cand in candidates:
+        m = re.search(r"\bRUT[A-Z]?\d{2,4}\b", cand.upper())
+        if m:
+            return m.group(0).upper()
+        m = re.search(r"\bIX\d{2}\b", cand, flags=re.IGNORECASE)
+        if m:
+            return m.group(0).upper()
+        m = re.search(r"\b(R\d{3,4}|E\d{3,4}|S\d{3,4})\b", cand, flags=re.IGNORECASE)
+        if m:
+            return m.group(0).upper()
+        m = re.search(r"\b[A-Z]{1,6}\d{2,4}[A-Z]?\b", cand.upper())
+        if m:
+            return m.group(0).upper()
+    return candidates[0][:60]
+
+def device_is_4g(row: pd.Series) -> bool:
+    # Detect LTE/4G even when the description uses "Cat 4 / Cat6 / Cat 12" without saying "LTE"
+    t = norm_text(row.get("description","")) + " " + norm_text(row.get("notes","")) + " " + norm_text(row.get("sku",""))
+
+    # If it explicitly says 5G/NR, treat as not 4G-only
+    if ("5g" in t) or ("nr" in t):
+        return False
+
+    # Classic signals
+    if ("lte" in t) or ("4g" in t):
+        return True
+
+    # LTE category signals (Cat 1..20 are LTE categories; Cat M1/M2 are LTE-M)
+    if re.search(r"\bcat\s*[-]?\s*(m1|m2)\b", t):
+        return True
+
+    m = re.search(r"\bcat\s*[-]?\s*(\d{1,2})\b", t)
+    if m:
+        try:
+            cat = int(m.group(1))
+            if 0 < cat <= 20:
+                return True
+        except Exception:
+            pass
+
+    # If "cat" appears at all, it's almost always LTE-family
+    if "cat" in t:
+        return True
+
+    return False
+
+    # If it explicitly says 5G/NR, treat as not 4G-only
+    if ("5g" in t) or ("nr" in t):
+        return False
+
+    # Classic signals
+    if ("lte" in t) or ("4g" in t):
+        return True
+
+    # LTE category signals (Cat 1..20 are LTE categories; Cat M1/M2 are LTE-M)
+    if re.search(r"\bcat\s*[-]?\s*(m1|m2)\b", t):
+        return True
+
+    m = re.search(r"\bcat\s*[-]?\s*(\d{1,2})\b", t)
+    if m:
+        try:
+            cat = int(m.group(1))
+            if 0 < cat <= 20:
+                return True
+        except Exception:
+            pass
+
+    # If "cat" appears at all, it's almost always LTE-family
+    if "cat" in t:
+        return True
+
+    return False
+
+
+def candidate_5g_models_from_lifecycle(manufacturer: str) -> List[str]:
+    mfr = norm_text(manufacturer)
+    pool = df_eos[df_eos["manufacturer"].astype(str).str.lower().eq(mfr)].copy() if "manufacturer" in df_eos.columns else df_eos.copy()
+    vals = pool["advanced_5g_option"].tolist() if "advanced_5g_option" in pool.columns else []
+    out, seen = [], set()
+    for v in vals:
+        tok = extract_model_token(v)
+        if tok and tok.lower() != "nan" and tok not in seen:
+            seen.add(tok); out.append(tok)
+    return out
+
+def candidate_4g_models_from_lifecycle(manufacturer: str) -> List[str]:
+    mfr = norm_text(manufacturer)
+    pool = df_eos[df_eos["manufacturer"].astype(str).str.lower().eq(mfr)].copy() if "manufacturer" in df_eos.columns else df_eos.copy()
+    vals = pool["suggested_replacement"].tolist() if "suggested_replacement" in pool.columns else []
+    out, seen = [], set()
+    for v in vals:
+        tok = extract_model_token(v)
+        if tok and tok.lower() != "nan" and tok not in seen:
+            seen.add(tok); out.append(tok)
+    return out
+
+def gpt_pick_from_candidates(old_row: pd.Series, candidates: List[str], need: str) -> str:
+    if client is None or not candidates:
+        return ""
+    sys = "Pick the best replacement model. Choose only from candidates. Return strict JSON only."
+    payload = {
+        "old_device": {
+            "sku": str(old_row.get("sku","")),
+            "manufacturer": str(old_row.get("manufacturer","")),
+            "description": str(old_row.get("description","")),
+            "need": need,
+        },
+        "candidates": candidates[:40],
+        "output_schema": {"choice":"string"}
+    }
+    out = gpt_json(sys, payload, max_tokens=240) or {}
+    choice = str(out.get("choice","") or "").strip()
+    return choice if choice in candidates else ""
+
+def fallback_5g_from_dec(canon_make: str) -> str:
+    pool5 = df_dec[(df_dec["_canon_make"] == canon_make) & (df_dec["_is5g"] == True)]
+    return str(pool5.iloc[0]["Model"]).strip() if not pool5.empty else ""
+
+def pick_replacements_lifecycle(row: pd.Series, status: str, use_gpt: bool = True) -> Dict[str, Any]:
+    canon = str(row.get("_canon_make","UNKNOWN"))
+    manufacturer = str(row.get("manufacturer","") or "")
+
+    sug_raw = safe_str(row.get("suggested_replacement",""))
+    adv_raw = safe_str(row.get("advanced_5g_option",""))
+
+    has_4g_alt = bool(sug_raw.strip())
+    has_5g_alt = bool(adv_raw.strip())
+
+    # Treat as 4G if the description indicates LTE OR lifecycle provides a 4G suggested replacement
+    is_4g = device_is_4g(row) or has_4g_alt
+
+    # Provide 5G option if the unit is 4G, EOS/EOL, or lifecycle explicitly provides advanced_5g_option
+    want_5g = is_4g or (status in {"End of Sale","End of Life"}) or has_5g_alt
+
+    # 4G alternative: show whenever lifecycle provides it (or device appears 4G)
+    repl_4g = "Not applicable"
+    if is_4g or has_4g_alt:
+        repl_4g = extract_model_token(sug_raw)
+        if not repl_4g:
+            cand4 = candidate_4g_models_from_lifecycle(manufacturer)
+            repl_4g = (gpt_pick_from_candidates(row, cand4, "4G alternative") if (use_gpt and client) else "") or (cand4[0] if cand4 else "")
+        if not repl_4g:
+            repl_4g = "Not applicable"
+
+    # 5G replacement: prefer lifecycle advanced_5g_option whenever present
+    repl_5g = "Not listed"
+    if want_5g:
+        repl_5g = extract_model_token(adv_raw)
+        if not repl_5g:
+            cand5 = candidate_5g_models_from_lifecycle(manufacturer)
+            repl_5g = (gpt_pick_from_candidates(row, cand5, "5G replacement/upgrade") if (use_gpt and client) else "") or (cand5[0] if cand5 else "")
+        if not repl_5g:
+            repl_5g = fallback_5g_from_dec(canon) or "Not listed"
+
+    if repl_5g.lower() == "nan":
+        repl_5g = "Not listed"
+
+    return {"repl_4g": repl_4g, "repl_5g": repl_5g, "sources": ["lifecycle_csv"] + (["gpt"] if (use_gpt and client) else [])}
+
+
+# ============================
+# Antennas (Parsec-only)
+# ============================
+PARSEC_FAMILY_WORDS = {"chinook","labrador","boxer","bloodhound","husky","beagle","mastiff","collie","shepherd","belgian","australian","terrier","pyrenees"}
+BAD_NAME_MARKERS = {"customization","standard connectors","connectors","features","benefits","specifications","mechanical","electrical","mounting","accessories","description:","standard sku"}
+
+def clean_line(s: str) -> str:
+    s = re.sub(r"\s+", " ", str(s or "").strip())
+    if re.fullmatch(r"-[a-z0-9]+", s.lower()):
+        return ""
+    return s
+
+def is_bad_name_line(line: str) -> bool:
+    low = line.lower()
+    if any(m in low for m in BAD_NAME_MARKERS):
+        return True
+    if re.search(r"\b-[a-z0-9]{1,4}\b", low) and len(low) <= 25:
+        return True
+    return False
+
+def family_from_line(line: str) -> str:
+    low = line.lower()
+    for fam in PARSEC_FAMILY_WORDS:
+        if fam in low:
+            return fam.capitalize()
+    return ""
+
+def parsec_connectors_from_card(t: str) -> str:
+    m = re.search(r"Standard\s+Connectors:\s*(.+)", t, flags=re.IGNORECASE)
+    if m:
+        return re.sub(r"\s+", " ", m.group(1).strip())[:80]
+    return ""
+
+def parsec_mounts_from_card(t: str) -> List[str]:
+    mounts = []
+    for m in re.finditer(r"Mount:\s*(.+)", t, flags=re.IGNORECASE):
+        val = re.sub(r"\s+", " ", m.group(1).strip())
+        parts = [p.strip().lower() for p in val.split(",") if p.strip()]
+        mounts.extend(parts)
+    out = []
+    seen = set()
+    for x in mounts:
+        if x not in seen:
+            seen.add(x); out.append(x)
+    return out
+
+def parsec_name_from_card(card_text: str) -> str:
+    lines = [clean_line(ln) for ln in str(card_text or "").splitlines()]
+    lines = [ln for ln in lines if ln]
+
+    for ln in lines:
+        if is_bad_name_line(ln):
+            continue
+        fam = family_from_line(ln)
+        if fam:
+            return fam
+
+    sku_i = None
+    for i, ln in enumerate(lines):
+        if "standard sku" in ln.lower():
+            sku_i = i
+            break
+    if sku_i is not None:
+        window = lines[max(0, sku_i - 12):sku_i]
+        for ln in reversed(window):
+            if is_bad_name_line(ln):
+                continue
+            if 3 <= len(ln) <= 40 and re.search(r"[A-Za-z]", ln):
+                return ln.split()[0].capitalize()
+
+    return "Parsec antenna"
+
+def parsec_part_from_card(t: str) -> str:
+    m = re.search(r"Standard\s+SKU:\s*([A-Z0-9]+)", t)
+    return m.group(1).strip() if m else ""
+
+def parsec_desc_from_card(t: str) -> str:
+    m = re.search(r"Description:\s*(.+?)(?:\n|$)", t, flags=re.IGNORECASE)
+    return re.sub(r"\s+"," ",m.group(1).strip())[:220] if m else ""
+
+def parsec_retrieve(query: str, top_k: int = 12) -> List[Dict[str, Any]]:
+    qv = embedder.encode([query], normalize_embeddings=True)
+    qv = np.asarray(qv, dtype=np.float32)
+    scores, ids = parsec_index.search(qv, top_k)
+    out: List[Dict[str, Any]] = []
+    for sc, i in zip(scores[0].tolist(), ids[0].tolist()):
+        if 0 <= int(i) < len(parsec_cards):
+            card = parsec_cards[int(i)]
+            out.append({
+                "score": float(sc),
+                "name": parsec_name_from_card(card),
+                "part_number": parsec_part_from_card(card),
+                "description": parsec_desc_from_card(card),
+                "connectors": parsec_connectors_from_card(card),
+                "mounts": parsec_mounts_from_card(card),
+                "_card": card.lower(),
+            })
+    return out
+
+def choose_best_parsec(cands: List[Dict[str, Any]], mode: str) -> Dict[str, Any]:
+    best = None
+    best_score = -1e9
+
+    for c in cands:
+        card = c.get("_card","")
+        mounts = c.get("mounts", []) or []
+        score = float(c.get("score", 0.0))
+
+        if "omni" in card:
+            score += 0.6
+        if "directional" in card:
+            score -= 1.5
+
+        if mode == "vehicle":
+            if any("magnetic" in m for m in mounts):
+                score += 3.0
+            if any("through" in m for m in mounts):
+                score += 2.0
+            if any("wall" in m for m in mounts) or any("pole" in m for m in mounts):
+                score -= 1.2
+            if "app: fixed" in card and "mobile" not in card:
+                score -= 2.0
+
+        if mode == "stationary":
+            if any("wall" in m for m in mounts):
+                score += 2.0
+            if any("pole" in m for m in mounts):
+                score += 1.8
+
+        if score > best_score:
+            best_score = score
+            best = c
+
+    if not best:
+        return {"name":"Parsec antenna","part_number":"","description":"","connectors":"","mounts":[]}
+
+    best = dict(best)
+    best.pop("_card", None)
+    return best
+
+
+def infer_mimo_for_5g(repl_5g_model: str) -> str:
+    """Rule: every 5G router uses a 4x4 antenna."""
+    return "4x4"
+
+    # If the model name hints 5G, lean 4x4
+    if "5g" in model.lower() or model.upper().startswith(("R", "E", "S", "IX", "RUTM")):
+        default = "4x4"
+    else:
+        default = "2x2"
+
+    # Use dec2025routers.csv if we can match the model under the same maker family
+    try:
+        pool = df_dec[df_dec["_canon_make"] == canon_make].copy()
+        if pool.empty:
+            return default
+        hit = process.extractOne(norm_text(model), pool["_norm_model"].tolist(), scorer=fuzz.WRatio)
+        if not hit or hit[1] < MATCH_OK:
+            return default
+        row = pool.iloc[int(hit[2])]
+        txt2 = (str(row.get("Antennas (internal/external/both)", "")) + " " + str(row.get("Modem Type", "")) + " " + str(row.get("Special notes",""))).lower()
+        if "4x4" in txt2 or "4 x 4" in txt2 or "4x 4" in txt2:
+            return "4x4"
+        if "2x2" in txt2 or "2 x 2" in txt2:
+            return "2x2"
+        # If modem type includes 5G, lean 4x4
+        if "5g" in txt2 or "nr" in txt2:
+            return "4x4"
+        return default
+    except Exception:
+        return default
+
+def antenna_options_for(router_model: str, tech: str, mimo: str) -> Dict[str, Any]:
+    q_stationary = f"{router_model} {tech} {mimo} omni stationary pole wall fixed site Parsec"
+    q_vehicle = f"{router_model} {tech} {mimo} omni vehicle mobile magnetic through-bolt Parsec"
+
+    cand_stationary = parsec_retrieve(q_stationary, top_k=12)
+    cand_vehicle = parsec_retrieve(q_vehicle, top_k=12)
+
+    s = choose_best_parsec(cand_stationary, mode="stationary")
+    v = choose_best_parsec(cand_vehicle, mode="vehicle")
+
+    s.update({"mimo": mimo, "why": "Stationary omni best match."})
+    v.update({"mimo": mimo, "why": "Vehicle omni best match."})
+
+    return {"stationary_omni": s, "vehicle_omni": v, "sources":["parsec_rag"]}
+
+
+# ============================
+# Install-ready checklist
+# ============================
+def install_ready_checklist(current_sku: str, repl: Dict[str,Any], ant: Dict[str,Any]) -> str:
+    st = ant.get("stationary_omni", {})
+    vh = ant.get("vehicle_omni", {})
+    if client is not None:
+        sys = "Create a short, install-ready checklist for a Verizon rep. Return markdown only."
+        payload = {"current_device": current_sku, "replacements": repl, "antennas": {"stationary": st, "vehicle": vh}}
+        resp = client.responses.create(
+            model=OPENAI_MODEL,
+            reasoning=OPENAI_REASONING,
+            input=[{"role":"system","content":sys},{"role":"user","content":json.dumps(payload)}],
+            max_output_tokens=520,
+        )
+        return (getattr(resp, "output_text", "") or "").strip()
+    return "\n".join([
+        "### Install-ready checklist",
+        f"- Current device: {current_sku}",
+        f"- 5G replacement: {repl.get('repl_5g','')}",
+        f"- 4G alternative: {repl.get('repl_4g','Not applicable')}",
+        f"- Stationary omni antenna: {st.get('name','')} (PN {st.get('part_number','')})",
+        f"- Vehicle omni antenna: {vh.get('name','')} (PN {vh.get('part_number','')})",
+        "- Next steps: confirm mounting + cable lengths + power; place order; schedule install.",
+    ])
+
+
+# ============================
+# Batch mode (NO GPT)
+# ============================
+def parse_batch_inputs(text_blob: str, file_obj: Any) -> List[str]:
+    items: List[str] = []
+    if file_obj is not None:
+        try:
+            path = file_obj.name if hasattr(file_obj, "name") else str(file_obj)
+            df = pd.read_csv(path)
+            col = df.columns[0]
+            items.extend([str(x).strip() for x in df[col].tolist() if str(x).strip()])
+        except Exception:
+            pass
+    if text_blob:
+        for ln in str(text_blob).splitlines():
+            ln = ln.strip()
+            if ln:
+                items.append(ln)
+    seen=set()
+    out=[]
+    for x in items:
+        k=norm_text(x)
+        if k and k not in seen:
+            seen.add(k); out.append(x)
+    return out
+
+def run_batch(text_blob: str, file_obj: Any, include_antennas: bool):
+    inputs = parse_batch_inputs(text_blob, file_obj)
+    if not inputs:
+        return "", None, None, ""
+
+    rows=[]
+    for item in inputs:
+        res = resolve_device(item)
+        if res.get("mode") != "ok":
+            rows.append({"Input": item, "Matched":"", "Status":"Needs review", "EOS":"", "EOL":"", "4G alternative":"", "5G replacement":"", "Notes":"Not found/ambiguous"})
+            continue
+
+        life_row = df_eos.iloc[int(res["row_idx"])]
+        eos, eol, status = row_to_dates_and_status(life_row)
+        repl = pick_replacements_lifecycle(life_row, status, use_gpt=False)
+
+        rows.append({
+            "Input": item,
+            "Matched": str(life_row.get("sku","")),
+            "Status": status,
+            "EOS": eos,
+            "EOL": eol,
+            "4G alternative": repl.get("repl_4g",""),
+            "5G replacement": repl.get("repl_5g",""),
+            "Notes": "",
+        })
+
+    out_df = pd.DataFrame(rows)
+    counts = out_df["Status"].value_counts(dropna=False).to_dict()
+    top_5g = out_df["5G replacement"].value_counts(dropna=False).head(5).to_dict()
+    summary = f"Rows: {len(out_df)} | " + " | ".join([f"{k}: {v}" for k,v in counts.items()])
+    rollup = "Top 5G recommendations:\n" + "\n".join([f"- {k}: {v}" for k,v in top_5g.items() if str(k).strip()])
+
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
+    out_df.to_csv(tmp.name, index=False)
+
+    return summary, out_df, tmp.name, rollup
+
+
+# ============================
+# Replacement feature table + manufacturer link (5G device)
+# ============================
+
+FEATURE_COLS = ["Device", "Modem technology", "WiFi", "Ports", "Antennas", "Ruggedness", "Use case"]
+
+# Manufacturer domains used for best-effort link resolution (no non-maker domains).
+MAKER_DOMAINS = {
+    "CRADLEPOINT": ["cradlepoint.com", "ericsson.com"],
+    "SIERRA": ["semtech.com", "airlink.com"],
+    "FEENEY": ["inseego.com"],
+    "DIGI": ["digi.com"],
+    "CISCO_MERAKI": ["meraki.cisco.com", "cisco.com"],
+    "CISCO": ["cisco.com"],
+    "TELTONIKA": ["teltonika-networks.com"],
+    "UNKNOWN": [],
+}
+
+HTTP_HEADERS = {
+    "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 "
+                  "(KHTML, like Gecko) Chrome/120.0 Safari/537.36"
+}
+HTTP_TIMEOUT = 12
+
+def _best_effort_manufacturer_url(model: str, canon_make: str) -> str:
+    \"\"\"Try to find a manufacturer page or datasheet link using simple on-domain searches.
+    If we can't confirm a page, return the manufacturer homepage for the maker family.
+    \"\"\"
+    model = str(model or "").strip()
+    if not model or model in {"Not listed", "Not applicable"}:
+        return ""
+
+    domains = MAKER_DOMAINS.get(canon_make, []) or []
+    if not domains:
+        return ""
+
+    # Candidate on-domain search URLs (common patterns across sites).
+    # We keep these on the manufacturer domain (no Google/Bing).
+    q = re.sub(r"\s+", "+", model)
+    url_candidates = []
+    for d in domains:
+        url_candidates += [
+            f"https://{d}/search?q={q}",
+            f"https://{d}/search?query={q}",
+            f"https://{d}/?s={q}",
+            f"https://www.{d}/search?q={q}",
+            f"https://www.{d}/search?query={q}",
+            f"https://www.{d}/?s={q}",
+        ]
+
+    # Also try a few direct product patterns for known makers (best effort).
+    if canon_make == "TELTONIKA":
+        slug = model.lower()
+        url_candidates += [
+            f"https://teltonika-networks.com/products/routers/{slug}",
+            f"https://teltonika-networks.com/product/{slug}",
+            "https://teltonika-networks.com/products/routers/",
+        ]
+    if canon_make == "DIGI":
+        url_candidates += [
+            "https://www.digi.com/products/networking/cellular-routers",
+            f"https://www.digi.com/search?q={q}",
+        ]
+    if canon_make == "CRADLEPOINT":
+        url_candidates += [
+            "https://cradlepoint.com/products/",
+            f"https://cradlepoint.com/?s={q}",
+        ]
+    if canon_make in {"CISCO", "CISCO_MERAKI"}:
+        url_candidates += [
+            f"https://www.cisco.com/c/en/us/search.html?q={q}",
+        ]
+
+    # Try to confirm a working page (HTTP 200 and model string somewhere in HTML).
+    for u in url_candidates[:18]:
+        try:
+            import requests
+            r = requests.get(u, headers=HTTP_HEADERS, timeout=HTTP_TIMEOUT, allow_redirects=True)
+            if r.status_code != 200:
+                continue
+            html = (r.text or "").lower()
+            if model.lower() in html or "datasheet" in html or "data sheet" in html:
+                return r.url
+        except Exception:
+            continue
+
+    # Fallback: maker homepage
+    d0 = domains[0]
+    return f"https://{d0}"
+
+def _features_from_dec(model: str, canon_make: str) -> Dict[str, str]:
+    \"\"\"Lookup a router model in dec2025routers.csv and return the key feature fields.\"\"\"
+    if not model or model in {"Not listed", "Not applicable"}:
+        return {k: "Not listed" for k in FEATURE_COLS[1:]}
+
+    pool = df_dec[df_dec["_canon_make"] == canon_make].copy()
+    if pool.empty:
+        return {k: "Not listed" for k in FEATURE_COLS[1:]}
+
+    hit = process.extractOne(norm_text(model), pool["_norm_model"].tolist(), scorer=fuzz.WRatio)
+    if not hit or hit[1] < MATCH_OK:
+        return {k: "Not listed" for k in FEATURE_COLS[1:]}
+
+    r = pool.iloc[int(hit[2])]
+    ports = f"WAN: {r.get('WAN ports and speed','')} | LAN: {r.get('LAN ports and speed','')}"
+    return {
+        "Modem technology": str(r.get("Modem Type","")) or "Not listed",
+        "WiFi": str(r.get("WiFi type","")) or "Not listed",
+        "Ports": ports.strip() if ports.strip() else "Not listed",
+        "Antennas": str(r.get("Antennas (internal/external/both)","")) or "Not listed",
+        "Ruggedness": str(r.get("Ruggedization","")) or "Not listed",
+        "Use case": str(r.get("Primary use case","")) or "Not listed",
+    }
+
+def _gpt_fill_feature_row(device_label: str, model: str, canon_make: str, row: Dict[str, str]) -> Dict[str, str]:
+    \"\"\"If dec can't supply values, ask GPT to fill missing ones (best guess).\"\"\"
+    if client is None:
+        return row
+
+    missing = [k for k,v in row.items() if (not v) or str(v).strip().lower() in {"not listed","nan",""}]
+    if not missing:
+        return row
+
+    sys = "Fill missing router feature fields for a Verizon rep. Return strict JSON only."
+    payload = {
+        "device_label": device_label,
+        "model": model,
+        "maker_family": canon_make,
+        "known": row,
+        "fill_only": missing,
+        "rules": [
+            "Fill only the requested fields.",
+            "Best guess if needed. Short phrases only.",
+            "Return JSON only."
+        ],
+        "output_schema": {k: "string" for k in missing}
+    }
+    out = gpt_json(sys, payload, max_tokens=260) or {}
+    for k in missing:
+        val = str(out.get(k, "") or "").strip()
+        if val:
+            row[k] = val
+    return row
+
+def build_replacement_features_table(repl_4g: str, repl_5g: str, canon_make: str) -> pd.DataFrame:
+    rows = []
+
+    # 4G
+    row4 = _features_from_dec(repl_4g, canon_make)
+    row4 = _gpt_fill_feature_row("4G alternative", repl_4g, canon_make, row4)
+    rows.append({"Device": "4G alternative", **row4})
+
+    # 5G
+    row5 = _features_from_dec(repl_5g, canon_make)
+    row5 = _gpt_fill_feature_row("5G replacement", repl_5g, canon_make, row5)
+    rows.append({"Device": "5G replacement", **row5})
+
+    df = pd.DataFrame(rows, columns=FEATURE_COLS)
+    return df
+
+# ============================
+# Output
+# ============================
+def assemble_output(life_row: pd.Series, status: str, eos: str, eol: str, repl: Dict[str,Any], ant: Dict[str,Any]) -> str:
+    current_name = f"{life_row.get('sku','')} — {life_row.get('description','')}".strip(" —")
+    st = ant.get("stationary_omni", {})
+    vh = ant.get("vehicle_omni", {})
+
+    lines = []
+    lines.append(f"1. Current device: **{current_name}**")
+    lines.append(f"2. Status: **{status}**")
+    lines.append(f"3. End of Sale date: **{eos}**")
+    lines.append(f"4. End of Life date: **{eol}**")
+    lines.append(f"5. 4G alternative (lifecycle): **{repl.get('repl_4g','Not applicable')}**")
+    lines.append(f"6. 5G replacement (lifecycle): **{repl.get('repl_5g','Not listed')}**")
+    lines.append("7. Antenna options (Parsec-only):")
+    conn_s = f" | Conn: {st.get('connectors','')}" if st.get("connectors") else ""
+    conn_v = f" | Conn: {vh.get('connectors','')}" if vh.get("connectors") else ""
+    lines.append(f"   - Stationary (Omni): **{st.get('name','')}** (Part #: {st.get('part_number','')}) — {st.get('description','')} — MIMO: {st.get('mimo','')}{conn_s}")
+    lines.append(f"   - Vehicle (Omni): **{vh.get('name','')}** (Part #: {vh.get('part_number','')}) — {vh.get('description','')} — MIMO: {vh.get('mimo','')}{conn_v}")
+
+    lines.append("\nSources (debug):")
+    for s in repl.get("sources", []) if isinstance(repl.get("sources"), list) else []:
+        lines.append(f"- {s}")
+    lines.append("- ParsecCatalog.pdf (local RAG)")
+    lines.append("- routers_eos_eol_by_sku.csv (replacements)")
+    return "\n".join(lines)
+
+
+# ============================
+# Gradio callbacks
+# IMPORTANT: no dict state and ALL events have api_name=False (prevents api_info schema generation)
+# ============================
+def run_lookup(user_text: str, st_json: str):
+    user_text = str(user_text or "").strip()
+    if not user_text:
+        return "Enter a router SKU/model.", "", None, gr.update(visible=False), gr.update(visible=False), "{}", ""
+
+    res = resolve_device(user_text)
+
+    if res.get("mode") == "pick":
+        opts = res.get("options", [])
+        choices = [o["label"] for o in opts]
+        st2 = {"mode":"pick","options": opts, "raw": user_text}
+        return "Did you mean A or B? Pick one, then click Use selection.", "", None, gr.update(choices=choices, value=None, visible=True), gr.update(visible=True), state_dump(st2), ""
+
+    if res.get("mode") != "ok":
+        return "Not found.", "", None, gr.update(visible=False), gr.update(visible=False), "{}", ""
+
+    life_row = df_eos.iloc[int(res["row_idx"])]
+    eos, eol, status = row_to_dates_and_status(life_row)
+
+    repl = pick_replacements_lifecycle(life_row, status, use_gpt=True)
+    canon_make = str(life_row.get("_canon_make","UNKNOWN"))
+    mimo = infer_mimo_for_5g(repl.get("repl_5g",""))
+    tech = "5G" if repl.get("repl_5g") and repl.get("repl_5g") != "Not listed" else ("4G" if device_is_4g(life_row) else "Unknown")
+    ant = antenna_options_for(repl.get("repl_5g") or str(life_row.get("sku","")), tech, mimo)
+
+    output = assemble_output(life_row, status, eos, eol, repl, ant)
+    st_out = {"row_idx": int(res["row_idx"]), "repl": repl, "ant": ant, "raw": user_text}
+    url5 = _best_effort_manufacturer_url(repl.get('repl_5g',''), canon_make)
+    link = f"**5G manufacturer page (best effort):** {url5}" if url5 else ""
+    feat_df = build_replacement_features_table(repl.get('repl_4g',''), repl.get('repl_5g',''), canon_make)
+    return output, link, feat_df, gr.update(visible=False), gr.update(visible=False), state_dump(st_out), ""
+
+def use_selection(selected_label: str, st_json: str):
+    st = state_load(st_json)
+    if not st or st.get("mode") != "pick":
+        return "Run a search first.", "", None, gr.update(visible=False), gr.update(visible=False), "{}", ""
+
+    if not selected_label:
+        return "Pick A or B first.", "", None, gr.update(visible=True), gr.update(visible=True), st_json, ""
+
+    chosen_row = None
+    for o in st.get("options", []):
+        if o.get("label") == selected_label:
+            chosen_row = int(o["row_idx"])
+            break
+    if chosen_row is None:
+        return "Pick a valid option.", "", None, gr.update(visible=True), gr.update(visible=True), st_json, ""
+
+    life_row = df_eos.iloc[int(chosen_row)]
+    eos, eol, status = row_to_dates_and_status(life_row)
+
+    repl = pick_replacements_lifecycle(life_row, status, use_gpt=True)
+    canon_make = str(life_row.get("_canon_make","UNKNOWN"))
+    mimo = infer_mimo_for_5g(repl.get("repl_5g",""))
+    tech = "5G" if repl.get("repl_5g") and repl.get("repl_5g") != "Not listed" else ("4G" if device_is_4g(life_row) else "Unknown")
+    ant = antenna_options_for(repl.get("repl_5g") or str(life_row.get("sku","")), tech, mimo)
+
+    output = assemble_output(life_row, status, eos, eol, repl, ant)
+    st_out = {"row_idx": int(chosen_row), "repl": repl, "ant": ant, "raw": st.get("raw","")}
+    url5 = _best_effort_manufacturer_url(repl.get('repl_5g',''), canon_make)
+    link = f"**5G manufacturer page (best effort):** {url5}" if url5 else ""
+    feat_df = build_replacement_features_table(repl.get('repl_4g',''), repl.get('repl_5g',''), canon_make)
+    return output, link, feat_df, gr.update(visible=False), gr.update(visible=False), state_dump(st_out), ""
+
+def make_install_ready(st_json: str):
+    st = state_load(st_json)
+    if not st or "row_idx" not in st:
+        return "Run a lookup first."
+    life_row = df_eos.iloc[int(st["row_idx"])]
+    current_sku = str(life_row.get("sku","") or "")
+    return install_ready_checklist(current_sku, st.get("repl", {}) or {}, st.get("ant", {}) or {})
+
+
+# ============================
+# UI
+# ============================
+with gr.Blocks(title="Only-Routers") as demo:
+    gr.Markdown("## Only-Routers\nSingle lookup + Batch upload for Verizon reps.")
+
+    with gr.Tabs():
+        with gr.Tab("Single"):
+            user_text = gr.Textbox(label="Router SKU or model", placeholder="Examples: IBR650B, AER1600, ES450, WR21, RUT240", lines=1)
+            st = gr.State("{}")  # JSON string
+
+            check_btn = gr.Button("Check", variant="primary")
+            pick_dd = gr.Dropdown(label="Pick A or B", choices=[], visible=False)
+            use_btn = gr.Button("Use selection", visible=False)
+
+            output_md = gr.Markdown()
+
+            link_md = gr.Markdown()
+            features_df = gr.Dataframe(headers=FEATURE_COLS, interactive=False, wrap=True)
+
+
+            install_btn = gr.Button("Make install-ready checklist")
+            install_md = gr.Markdown()
+
+            check_btn.click(fn=run_lookup, inputs=[user_text, st], outputs=[output_md, link_md, features_df, pick_dd, use_btn, st, install_md], api_name=False)
+            use_btn.click(fn=use_selection, inputs=[pick_dd, st], outputs=[output_md, link_md, features_df, pick_dd, use_btn, st, install_md], api_name=False)
+            install_btn.click(fn=make_install_ready, inputs=[st], outputs=[install_md], api_name=False)
+
+        with gr.Tab("Batch"):
+            gr.Markdown("Paste one per line or upload a CSV (first column). Batch runs fast (no GPT).")
+            batch_text = gr.Textbox(label="Paste devices (one per line)", lines=8, placeholder="WR21\nRUT240\nIBR650B")
+            batch_file = gr.File(label="Upload CSV", file_types=[".csv"])
+            include_ant = gr.Checkbox(label="Include antenna picks (slower)", value=False)
+            run_btn = gr.Button("Run batch", variant="primary")
+
+            summary_md = gr.Markdown()
+            rollup_md = gr.Markdown()
+            table = gr.Dataframe(interactive=False, wrap=True)
+            dl = gr.File(label="Download results CSV")
+
+            run_btn.click(fn=run_batch, inputs=[batch_text, batch_file, include_ant], outputs=[summary_md, table, dl, rollup_md], api_name=False)
+
+# IMPORTANT: On Spaces, demo.launch() is correct; do NOT use share=True.
+demo.launch(show_api=False)

Updates/app_working.py

@@ -0,0 +1,1005 @@
+import os
+import re
+import json
+import math
+import hashlib
+import tempfile
+from dataclasses import dataclass
+from datetime import datetime, date
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+
+import fitz  # PyMuPDF
+import faiss
+from sentence_transformers import SentenceTransformer
+from rapidfuzz import fuzz, process
+
+import gradio as gr
+from openai import OpenAI
+
+
+# ============================
+# Settings
+# ============================
+TODAY = date(2026, 1, 18)
+OPENAI_MODEL = "gpt-5.2"
+OPENAI_REASONING = {"effort": "high"}
+MATCH_OK = 80
+
+EMBED_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
+PARSEC_CONTEXT_BEFORE = 900
+PARSEC_CONTEXT_AFTER = 1600
+
+
+# ============================
+# OpenAI client (HF Space secret: OPENAI_API_KEY)
+# ============================
+API_KEY = os.getenv("OPENAI_API_KEY", "").strip()
+client = OpenAI(api_key=API_KEY) if API_KEY else None
+
+# ----------------------------
+# Gradio state helpers
+# Keep state as a JSON STRING to avoid schema issues on Hugging Face.
+# ----------------------------
+def state_load(st_json: str) -> Dict[str, Any]:
+    try:
+        if not st_json:
+            return {}
+        return json.loads(st_json) if isinstance(st_json, str) else {}
+    except Exception:
+        return {}
+
+def state_dump(st: Dict[str, Any]) -> str:
+    try:
+        return json.dumps(st or {}, ensure_ascii=False)
+    except Exception:
+        return "{}"
+
+
+
+# ============================
+# Helpers
+# ============================
+def norm_text(s: Any) -> str:
+    try:
+        if s is None or (isinstance(s, float) and math.isnan(s)) or pd.isna(s):
+            return ""
+    except Exception:
+        pass
+    s = str(s).strip().lower()
+    s = re.sub(r"[^a-z0-9\s\-\/]", " ", s)
+    s = re.sub(r"\s+", " ", s).strip()
+    return s
+
+def safe_str(v: Any) -> str:
+    if v is None or (isinstance(v, float) and pd.isna(v)) or pd.isna(v):
+        return ""
+    return str(v).strip()
+
+def is_5g(modem_type: Any) -> bool:
+    s = norm_text(modem_type)
+    return ("5g" in s) or ("nr" in s)
+
+def json_load_safe(s: str) -> Dict[str, Any]:
+    try:
+        return json.loads(s)
+    except Exception:
+        return {}
+
+def gpt_json(system: str, payload: Dict[str, Any], max_tokens: int = 600) -> Dict[str, Any]:
+    if client is None:
+        return {}
+    resp = client.responses.create(
+        model=OPENAI_MODEL,
+        reasoning=OPENAI_REASONING,
+        input=[{"role":"system","content":system},{"role":"user","content":json.dumps(payload)}],
+        max_output_tokens=max_tokens,
+    )
+    return json_load_safe(getattr(resp, "output_text", "") or "")
+
+
+# ============================
+# Load data
+# ============================
+EOS_PATH = "routers_eos_eol_by_sku.csv"
+DEC_PATH = "dec2025routers.csv"
+PARSEC_PDF = "ParsecCatalog.pdf"
+
+if not os.path.exists(EOS_PATH):
+    raise FileNotFoundError(f"Missing {EOS_PATH} in repo.")
+if not os.path.exists(DEC_PATH):
+    raise FileNotFoundError(f"Missing {DEC_PATH} in repo.")
+if not os.path.exists(PARSEC_PDF):
+    raise FileNotFoundError(f"Missing {PARSEC_PDF} in repo.")
+
+df_eos = pd.read_csv(EOS_PATH).copy()
+df_dec = pd.read_csv(DEC_PATH).copy()
+
+
+def _canonize_eos_columns(df: pd.DataFrame) -> pd.DataFrame:
+    """Normalize lifecycle CSV column names (case-insensitive) and create expected columns."""
+    # Map various header spellings to canonical names used by the app
+    mapping = {}
+    for c in df.columns:
+        k = str(c).strip().lower().replace(" ", "_")
+        if k in {"sku", "model", "device", "device_sku"}:
+            mapping[c] = "sku"
+        elif k in {"manufacturer", "make", "vendor"}:
+            mapping[c] = "manufacturer"
+        elif k in {"device_type", "type"}:
+            mapping[c] = "device_type"
+        elif k in {"end_of_sale", "eos", "end_sale", "end_of_sales"}:
+            mapping[c] = "end_of_sale"
+        elif k in {"end_of_life", "eol", "end_life"}:
+            mapping[c] = "end_of_life"
+        elif k in {"suggested_replacement", "replacement_4g", "lte_replacement", "replacement_lte", "replacement"}:
+            mapping[c] = "suggested_replacement"
+        elif k in {"advanced_5g_option", "replacement_5g", "fiveg_replacement", "5g_replacement", "upgrade_5g"}:
+            mapping[c] = "advanced_5g_option"
+        elif k in {"region", "market"}:
+            mapping[c] = "region"
+        elif k in {"notes", "note"}:
+            mapping[c] = "notes"
+        elif k in {"description", "device_description", "name"}:
+            mapping[c] = "description"
+
+    df = df.rename(columns=mapping).copy()
+
+    # Create expected columns if missing
+    if "sku" not in df.columns:
+        # Try the common capitalized header as a fallback
+        if "SKU" in df.columns:
+            df["sku"] = df["SKU"].astype(str)
+        else:
+            df["sku"] = ""
+
+    if "manufacturer" not in df.columns:
+        df["manufacturer"] = ""
+
+    if "device_type" not in df.columns:
+        df["device_type"] = ""
+
+    if "description" not in df.columns:
+        # If the simplified file removed description, use SKU as description (still searchable)
+        df["description"] = df["sku"].astype(str)
+
+    if "notes" not in df.columns:
+        df["notes"] = ""
+
+    if "region" not in df.columns:
+        df["region"] = ""
+
+    if "suggested_replacement" not in df.columns:
+        df["suggested_replacement"] = ""
+
+    if "advanced_5g_option" not in df.columns:
+        df["advanced_5g_option"] = ""
+
+    if "end_of_sale" not in df.columns:
+        df["end_of_sale"] = ""
+
+    if "end_of_life" not in df.columns:
+        df["end_of_life"] = ""
+
+    return df
+
+df_eos = _canonize_eos_columns(df_eos)
+
+
+def region_ok(x: Any) -> bool:
+    s = str(x or "").strip().lower()
+    if not s:
+        return True
+    if "not specified" in s:
+        return True
+    if "north america" in s:
+        return True
+    if re.search(r"\busa\b", s):
+        return True
+    if re.search(r"\bunited\s+states\b", s):
+        return True
+    if re.search(r"\bu\.?s\.?\b", s):
+        return True
+    return False
+
+if "region" in df_eos.columns:
+    df_eos = df_eos[df_eos["region"].apply(region_ok)].reset_index(drop=True)
+
+# Maker mapping (includes Teltonika)
+CANON_MAKER = {
+    "CRADLEPOINT": {"cradlepoint", "ericsson", "ericsson enterprise wireless"},
+    "SIERRA": {"sierra", "sierra wireless", "semtech", "airlink"},
+    "FEENEY": {"feeney", "feeney wireless", "inseego"},
+    "DIGI": {"digi", "accelerated", "accelerated concepts"},
+    "CISCO_MERAKI": {"meraki", "cisco meraki"},
+    "CISCO": {"cisco"},
+    "TELTONIKA": {"teltonika"},
+}
+
+def canon_maker_from_text(s: Any) -> str:
+    t = norm_text(s)
+    for canon, terms in CANON_MAKER.items():
+        for term in terms:
+            if term in t:
+                return canon
+    return "UNKNOWN"
+
+df_eos["_canon_make"] = df_eos["manufacturer"].apply(canon_maker_from_text) if "manufacturer" in df_eos.columns else "UNKNOWN"
+df_eos["_norm_sku"] = df_eos["sku"].apply(norm_text) if "sku" in df_eos.columns else ""
+df_eos["_norm_desc"] = df_eos["description"].apply(norm_text) if "description" in df_eos.columns else ""
+df_eos["_norm_notes"] = df_eos["notes"].apply(norm_text) if "notes" in df_eos.columns else ""
+
+df_dec["_canon_make"] = df_dec["Make"].apply(canon_maker_from_text) if "Make" in df_dec.columns else "UNKNOWN"
+df_dec["_norm_model"] = df_dec["Model"].apply(norm_text) if "Model" in df_dec.columns else ""
+df_dec["_is5g"] = df_dec["Modem Type"].apply(is_5g) if "Modem Type" in df_dec.columns else False
+
+
+# ============================
+# Date helpers
+# ============================
+@dataclass
+class ParsedDate:
+    raw: str
+    kind: str
+    value: Optional[date]
+
+def parse_date_field(x: Any) -> ParsedDate:
+    raw = str(x or "").strip()
+    if not raw:
+        return ParsedDate(raw="", kind="missing", value=None)
+
+    # Common US formats: M/D/YY or M/D/YYYY (e.g., 6/24/24, 9/30/21)
+    for fmt in ("%m/%d/%y", "%m/%d/%Y", "%-m/%-d/%y", "%-m/%-d/%Y"):
+        try:
+            dt = datetime.strptime(raw, fmt).date()
+            return ParsedDate(raw=raw, kind="full", value=dt)
+        except Exception:
+            pass
+
+    # ISO-ish: YYYY
+    if re.fullmatch(r"\d{4}", raw):
+        y = int(raw)
+        if y == TODAY.year:
+            return ParsedDate(raw=raw, kind="year", value=date(y, 1, 1))
+        if y < TODAY.year:
+            return ParsedDate(raw=raw, kind="year", value=date(y, 1, 1))
+        return ParsedDate(raw=raw, kind="year", value=date(y, 12, 31))
+
+    # YYYY-MM
+    if re.fullmatch(r"\d{4}-\d{2}", raw):
+        try:
+            y, m = raw.split("-")
+            return ParsedDate(raw=raw, kind="year_month", value=date(int(y), int(m), 1))
+        except Exception:
+            return ParsedDate(raw=raw, kind="bad", value=None)
+
+    # YYYY-MM-DD
+    if re.fullmatch(r"\d{4}-\d{2}-\d{2}", raw):
+        try:
+            dt = datetime.strptime(raw, "%Y-%m-%d").date()
+            return ParsedDate(raw=raw, kind="full", value=dt)
+        except Exception:
+            return ParsedDate(raw=raw, kind="bad", value=None)
+
+    # Last resort: leave as raw (unparsed)
+    return ParsedDate(raw=raw, kind="bad", value=None)
+
+    if re.fullmatch(r"\d{4}-\d{2}-\d{2}", raw):
+        try:
+            dt = datetime.strptime(raw, "%Y-%m-%d").date()
+            return ParsedDate(raw=raw, kind="full", value=dt)
+        except Exception:
+            return ParsedDate(raw=raw, kind="bad", value=None)
+
+    return ParsedDate(raw=raw, kind="bad", value=None)
+
+def display_date(pd_: ParsedDate) -> str:
+    if pd_.kind == "missing":
+        return "Not listed"
+    if pd_.kind == "bad":
+        return pd_.raw or "Not listed"
+    return pd_.raw
+
+def status_from_eos_eol(eos: ParsedDate, eol: ParsedDate) -> str:
+    if eos.value is None and eol.value is None:
+        return "Unknown"
+    if eol.value is not None and eol.value <= TODAY:
+        return "End of Life"
+    if eos.value is not None and eos.value <= TODAY:
+        return "End of Sale"
+    return "Active"
+
+def row_to_dates_and_status(row: pd.Series) -> Tuple[str, str, str]:
+    eos = parse_date_field(row.get("end_of_sale"))
+    eol = parse_date_field(row.get("end_of_life"))
+    return display_date(eos), display_date(eol), status_from_eos_eol(eos, eol)
+
+
+# ============================
+# Embeddings + Parsec index
+# ============================
+embedder = SentenceTransformer(EMBED_MODEL_NAME)
+
+def extract_pdf_text_pages(path: str) -> List[str]:
+    doc = fitz.open(path)
+    return [doc[i].get_text("text") for i in range(len(doc))]
+
+def build_parsec_cards(pages: List[str]) -> List[str]:
+    cards = []
+    for p in pages:
+        for m in re.finditer(r"Standard\s+SKU:", p):
+            start = max(0, m.start() - PARSEC_CONTEXT_BEFORE)
+            end = min(len(p), m.start() + PARSEC_CONTEXT_AFTER)
+            c = p[start:end].strip()
+            if len(c) >= 200:
+                cards.append(c)
+    out, seen = [], set()
+    for c in cards:
+        h = hashlib.sha1(c.encode("utf-8")).hexdigest()
+        if h not in seen:
+            seen.add(h); out.append(c)
+    return out
+
+parsec_cards = build_parsec_cards(extract_pdf_text_pages(PARSEC_PDF))
+parsec_emb = embedder.encode(parsec_cards, batch_size=64, show_progress_bar=False, normalize_embeddings=True)
+parsec_emb = np.asarray(parsec_emb, dtype=np.float32)
+parsec_index = faiss.IndexFlatIP(parsec_emb.shape[1])
+parsec_index.add(parsec_emb)
+
+
+# ============================
+# Device resolution
+# ============================
+def label_for_row(i: int) -> str:
+    r = df_eos.iloc[i]
+    return f"{r.get('sku','')} — {r.get('manufacturer','')} — {r.get('description','')}"[:220]
+
+EOS_LABELS = [label_for_row(i) for i in range(len(df_eos))]
+EOS_CORPUS = []
+for _, r in df_eos.iterrows():
+    EOS_CORPUS.append(" ".join([r.get("_norm_sku",""), r.get("_canon_make",""), r.get("_norm_desc",""), r.get("_norm_notes","")]))
+
+def local_candidates(query: str, top_k: int = 6) -> List[Tuple[int, int, str]]:
+    q = norm_text(query)
+    hits = process.extract(q, EOS_CORPUS, scorer=fuzz.WRatio, limit=top_k)
+    return [(int(idx), int(score), EOS_LABELS[int(idx)]) for _, score, idx in hits]
+
+def gpt_choose_device(user_text: str, candidates: List[Tuple[int,int,str]]) -> Dict[str, Any]:
+    if client is None:
+        return {}
+    sys = "Pick which router the user meant. Never invent. Return strict JSON only."
+    payload = {
+        "user_input": user_text,
+        "candidates": [{"row_idx": i, "score": s, "label": lbl} for (i,s,lbl) in candidates],
+        "rules": [
+            "If one is clearly correct, return mode='ok' with row_idx.",
+            "If two are plausible, return mode='pick' with top 2 options."
+        ],
+        "output_schema": {"mode":"ok|pick","row_idx":"int","options":[{"row_idx":"int","label":"string"}]}
+    }
+    return gpt_json(sys, payload, max_tokens=280)
+
+def resolve_device(user_text: str) -> Dict[str, Any]:
+    q = norm_text(user_text)
+    exact = df_eos.index[df_eos["_norm_sku"] == q].tolist()
+    if len(exact) == 1:
+        return {"mode":"ok","row_idx": int(exact[0])}
+    if len(exact) > 1:
+        opts = [{"row_idx": int(i), "label": EOS_LABELS[int(i)]} for i in exact[:2]]
+        return {"mode":"pick","options": opts}
+
+    cands = local_candidates(user_text, top_k=6)
+    if not cands:
+        return {"mode":"not_found"}
+
+    if cands[0][1] >= 95 and (len(cands) == 1 or (cands[0][1] - cands[1][1]) >= 8):
+        return {"mode":"ok","row_idx": cands[0][0]}
+
+    g = gpt_choose_device(user_text, cands)
+    if g.get("mode") == "ok" and isinstance(g.get("row_idx"), int):
+        return {"mode":"ok","row_idx": int(g["row_idx"])}
+
+    if g.get("mode") == "pick":
+        opts = g.get("options", []) or []
+        opts2 = [{"row_idx": int(o["row_idx"]), "label": str(o["label"])} for o in opts[:2] if "row_idx" in o]
+        if opts2:
+            return {"mode":"pick","options": opts2}
+
+    if len(cands) > 1:
+        return {"mode":"pick","options":[{"row_idx":cands[0][0],"label":cands[0][2]},{"row_idx":cands[1][0],"label":cands[1][2]}]}
+    return {"mode":"pick","options":[{"row_idx":cands[0][0],"label":cands[0][2]}]}
+
+
+# ============================
+# Replacements — lifecycle CSV source of truth
+# ============================
+def extract_model_token(text: str) -> str:
+    s = safe_str(text)
+    if not s:
+        return ""
+    parts = [p.strip() for p in s.split("|") if p.strip()]
+    candidates = parts[::-1] if parts else [s]
+    for cand in candidates:
+        m = re.search(r"\bRUT[A-Z]?\d{2,4}\b", cand.upper())
+        if m:
+            return m.group(0).upper()
+        m = re.search(r"\bIX\d{2}\b", cand, flags=re.IGNORECASE)
+        if m:
+            return m.group(0).upper()
+        m = re.search(r"\b(R\d{3,4}|E\d{3,4}|S\d{3,4})\b", cand, flags=re.IGNORECASE)
+        if m:
+            return m.group(0).upper()
+        m = re.search(r"\b[A-Z]{1,6}\d{2,4}[A-Z]?\b", cand.upper())
+        if m:
+            return m.group(0).upper()
+    return candidates[0][:60]
+
+def device_is_4g(row: pd.Series) -> bool:
+    # Detect LTE/4G even when the description uses "Cat 4 / Cat6 / Cat 12" without saying "LTE"
+    t = norm_text(row.get("description","")) + " " + norm_text(row.get("notes","")) + " " + norm_text(row.get("sku",""))
+
+    # If it explicitly says 5G/NR, treat as not 4G-only
+    if ("5g" in t) or ("nr" in t):
+        return False
+
+    # Classic signals
+    if ("lte" in t) or ("4g" in t):
+        return True
+
+    # LTE category signals (Cat 1..20 are LTE categories; Cat M1/M2 are LTE-M)
+    if re.search(r"\bcat\s*[-]?\s*(m1|m2)\b", t):
+        return True
+
+    m = re.search(r"\bcat\s*[-]?\s*(\d{1,2})\b", t)
+    if m:
+        try:
+            cat = int(m.group(1))
+            if 0 < cat <= 20:
+                return True
+        except Exception:
+            pass
+
+    # If "cat" appears at all, it's almost always LTE-family
+    if "cat" in t:
+        return True
+
+    return False
+
+    # If it explicitly says 5G/NR, treat as not 4G-only
+    if ("5g" in t) or ("nr" in t):
+        return False
+
+    # Classic signals
+    if ("lte" in t) or ("4g" in t):
+        return True
+
+    # LTE category signals (Cat 1..20 are LTE categories; Cat M1/M2 are LTE-M)
+    if re.search(r"\bcat\s*[-]?\s*(m1|m2)\b", t):
+        return True
+
+    m = re.search(r"\bcat\s*[-]?\s*(\d{1,2})\b", t)
+    if m:
+        try:
+            cat = int(m.group(1))
+            if 0 < cat <= 20:
+                return True
+        except Exception:
+            pass
+
+    # If "cat" appears at all, it's almost always LTE-family
+    if "cat" in t:
+        return True
+
+    return False
+
+
+def candidate_5g_models_from_lifecycle(manufacturer: str) -> List[str]:
+    mfr = norm_text(manufacturer)
+    pool = df_eos[df_eos["manufacturer"].astype(str).str.lower().eq(mfr)].copy() if "manufacturer" in df_eos.columns else df_eos.copy()
+    vals = pool["advanced_5g_option"].tolist() if "advanced_5g_option" in pool.columns else []
+    out, seen = [], set()
+    for v in vals:
+        tok = extract_model_token(v)
+        if tok and tok.lower() != "nan" and tok not in seen:
+            seen.add(tok); out.append(tok)
+    return out
+
+def candidate_4g_models_from_lifecycle(manufacturer: str) -> List[str]:
+    mfr = norm_text(manufacturer)
+    pool = df_eos[df_eos["manufacturer"].astype(str).str.lower().eq(mfr)].copy() if "manufacturer" in df_eos.columns else df_eos.copy()
+    vals = pool["suggested_replacement"].tolist() if "suggested_replacement" in pool.columns else []
+    out, seen = [], set()
+    for v in vals:
+        tok = extract_model_token(v)
+        if tok and tok.lower() != "nan" and tok not in seen:
+            seen.add(tok); out.append(tok)
+    return out
+
+def gpt_pick_from_candidates(old_row: pd.Series, candidates: List[str], need: str) -> str:
+    if client is None or not candidates:
+        return ""
+    sys = "Pick the best replacement model. Choose only from candidates. Return strict JSON only."
+    payload = {
+        "old_device": {
+            "sku": str(old_row.get("sku","")),
+            "manufacturer": str(old_row.get("manufacturer","")),
+            "description": str(old_row.get("description","")),
+            "need": need,
+        },
+        "candidates": candidates[:40],
+        "output_schema": {"choice":"string"}
+    }
+    out = gpt_json(sys, payload, max_tokens=240) or {}
+    choice = str(out.get("choice","") or "").strip()
+    return choice if choice in candidates else ""
+
+def fallback_5g_from_dec(canon_make: str) -> str:
+    pool5 = df_dec[(df_dec["_canon_make"] == canon_make) & (df_dec["_is5g"] == True)]
+    return str(pool5.iloc[0]["Model"]).strip() if not pool5.empty else ""
+
+def pick_replacements_lifecycle(row: pd.Series, status: str, use_gpt: bool = True) -> Dict[str, Any]:
+    canon = str(row.get("_canon_make","UNKNOWN"))
+    manufacturer = str(row.get("manufacturer","") or "")
+
+    sug_raw = safe_str(row.get("suggested_replacement",""))
+    adv_raw = safe_str(row.get("advanced_5g_option",""))
+
+    has_4g_alt = bool(sug_raw.strip())
+    has_5g_alt = bool(adv_raw.strip())
+
+    # Treat as 4G if the description indicates LTE OR lifecycle provides a 4G suggested replacement
+    is_4g = device_is_4g(row) or has_4g_alt
+
+    # Provide 5G option if the unit is 4G, EOS/EOL, or lifecycle explicitly provides advanced_5g_option
+    want_5g = is_4g or (status in {"End of Sale","End of Life"}) or has_5g_alt
+
+    # 4G alternative: show whenever lifecycle provides it (or device appears 4G)
+    repl_4g = "Not applicable"
+    if is_4g or has_4g_alt:
+        repl_4g = extract_model_token(sug_raw)
+        if not repl_4g:
+            cand4 = candidate_4g_models_from_lifecycle(manufacturer)
+            repl_4g = (gpt_pick_from_candidates(row, cand4, "4G alternative") if (use_gpt and client) else "") or (cand4[0] if cand4 else "")
+        if not repl_4g:
+            repl_4g = "Not applicable"
+
+    # 5G replacement: prefer lifecycle advanced_5g_option whenever present
+    repl_5g = "Not listed"
+    if want_5g:
+        repl_5g = extract_model_token(adv_raw)
+        if not repl_5g:
+            cand5 = candidate_5g_models_from_lifecycle(manufacturer)
+            repl_5g = (gpt_pick_from_candidates(row, cand5, "5G replacement/upgrade") if (use_gpt and client) else "") or (cand5[0] if cand5 else "")
+        if not repl_5g:
+            repl_5g = fallback_5g_from_dec(canon) or "Not listed"
+
+    if repl_5g.lower() == "nan":
+        repl_5g = "Not listed"
+
+    return {"repl_4g": repl_4g, "repl_5g": repl_5g, "sources": ["lifecycle_csv"] + (["gpt"] if (use_gpt and client) else [])}
+
+
+# ============================
+# Antennas (Parsec-only)
+# ============================
+PARSEC_FAMILY_WORDS = {"chinook","labrador","boxer","bloodhound","husky","beagle","mastiff","collie","shepherd","belgian","australian","terrier","pyrenees"}
+BAD_NAME_MARKERS = {"customization","standard connectors","connectors","features","benefits","specifications","mechanical","electrical","mounting","accessories","description:","standard sku"}
+
+def clean_line(s: str) -> str:
+    s = re.sub(r"\s+", " ", str(s or "").strip())
+    if re.fullmatch(r"-[a-z0-9]+", s.lower()):
+        return ""
+    return s
+
+def is_bad_name_line(line: str) -> bool:
+    low = line.lower()
+    if any(m in low for m in BAD_NAME_MARKERS):
+        return True
+    if re.search(r"\b-[a-z0-9]{1,4}\b", low) and len(low) <= 25:
+        return True
+    return False
+
+def family_from_line(line: str) -> str:
+    low = line.lower()
+    for fam in PARSEC_FAMILY_WORDS:
+        if fam in low:
+            return fam.capitalize()
+    return ""
+
+def parsec_connectors_from_card(t: str) -> str:
+    m = re.search(r"Standard\s+Connectors:\s*(.+)", t, flags=re.IGNORECASE)
+    if m:
+        return re.sub(r"\s+", " ", m.group(1).strip())[:80]
+    return ""
+
+def parsec_mounts_from_card(t: str) -> List[str]:
+    mounts = []
+    for m in re.finditer(r"Mount:\s*(.+)", t, flags=re.IGNORECASE):
+        val = re.sub(r"\s+", " ", m.group(1).strip())
+        parts = [p.strip().lower() for p in val.split(",") if p.strip()]
+        mounts.extend(parts)
+    out = []
+    seen = set()
+    for x in mounts:
+        if x not in seen:
+            seen.add(x); out.append(x)
+    return out
+
+def parsec_name_from_card(card_text: str) -> str:
+    lines = [clean_line(ln) for ln in str(card_text or "").splitlines()]
+    lines = [ln for ln in lines if ln]
+
+    for ln in lines:
+        if is_bad_name_line(ln):
+            continue
+        fam = family_from_line(ln)
+        if fam:
+            return fam
+
+    sku_i = None
+    for i, ln in enumerate(lines):
+        if "standard sku" in ln.lower():
+            sku_i = i
+            break
+    if sku_i is not None:
+        window = lines[max(0, sku_i - 12):sku_i]
+        for ln in reversed(window):
+            if is_bad_name_line(ln):
+                continue
+            if 3 <= len(ln) <= 40 and re.search(r"[A-Za-z]", ln):
+                return ln.split()[0].capitalize()
+
+    return "Parsec antenna"
+
+def parsec_part_from_card(t: str) -> str:
+    m = re.search(r"Standard\s+SKU:\s*([A-Z0-9]+)", t)
+    return m.group(1).strip() if m else ""
+
+def parsec_desc_from_card(t: str) -> str:
+    m = re.search(r"Description:\s*(.+?)(?:\n|$)", t, flags=re.IGNORECASE)
+    return re.sub(r"\s+"," ",m.group(1).strip())[:220] if m else ""
+
+def parsec_retrieve(query: str, top_k: int = 12) -> List[Dict[str, Any]]:
+    qv = embedder.encode([query], normalize_embeddings=True)
+    qv = np.asarray(qv, dtype=np.float32)
+    scores, ids = parsec_index.search(qv, top_k)
+    out: List[Dict[str, Any]] = []
+    for sc, i in zip(scores[0].tolist(), ids[0].tolist()):
+        if 0 <= int(i) < len(parsec_cards):
+            card = parsec_cards[int(i)]
+            out.append({
+                "score": float(sc),
+                "name": parsec_name_from_card(card),
+                "part_number": parsec_part_from_card(card),
+                "description": parsec_desc_from_card(card),
+                "connectors": parsec_connectors_from_card(card),
+                "mounts": parsec_mounts_from_card(card),
+                "_card": card.lower(),
+            })
+    return out
+
+def choose_best_parsec(cands: List[Dict[str, Any]], mode: str) -> Dict[str, Any]:
+    best = None
+    best_score = -1e9
+
+    for c in cands:
+        card = c.get("_card","")
+        mounts = c.get("mounts", []) or []
+        score = float(c.get("score", 0.0))
+
+        if "omni" in card:
+            score += 0.6
+        if "directional" in card:
+            score -= 1.5
+
+        if mode == "vehicle":
+            if any("magnetic" in m for m in mounts):
+                score += 3.0
+            if any("through" in m for m in mounts):
+                score += 2.0
+            if any("wall" in m for m in mounts) or any("pole" in m for m in mounts):
+                score -= 1.2
+            if "app: fixed" in card and "mobile" not in card:
+                score -= 2.0
+
+        if mode == "stationary":
+            if any("wall" in m for m in mounts):
+                score += 2.0
+            if any("pole" in m for m in mounts):
+                score += 1.8
+
+        if score > best_score:
+            best_score = score
+            best = c
+
+    if not best:
+        return {"name":"Parsec antenna","part_number":"","description":"","connectors":"","mounts":[]}
+
+    best = dict(best)
+    best.pop("_card", None)
+    return best
+
+
+def infer_mimo_for_5g(model: str, canon_make: str) -> str:
+    """Best-effort MIMO guess for antenna selection (2x2 vs 4x4)."""
+    # If model is unknown, default to 2x2 (safer ordering)
+    if not model or model in {"Not applicable", "Not listed"}:
+        return "2x2"
+
+    # If the model name hints 5G, lean 4x4
+    if "5g" in model.lower() or model.upper().startswith(("R", "E", "S", "IX", "RUTM")):
+        default = "4x4"
+    else:
+        default = "2x2"
+
+    # Use dec2025routers.csv if we can match the model under the same maker family
+    try:
+        pool = df_dec[df_dec["_canon_make"] == canon_make].copy()
+        if pool.empty:
+            return default
+        hit = process.extractOne(norm_text(model), pool["_norm_model"].tolist(), scorer=fuzz.WRatio)
+        if not hit or hit[1] < MATCH_OK:
+            return default
+        row = pool.iloc[int(hit[2])]
+        txt2 = (str(row.get("Antennas (internal/external/both)", "")) + " " + str(row.get("Modem Type", "")) + " " + str(row.get("Special notes",""))).lower()
+        if "4x4" in txt2 or "4 x 4" in txt2 or "4x 4" in txt2:
+            return "4x4"
+        if "2x2" in txt2 or "2 x 2" in txt2:
+            return "2x2"
+        # If modem type includes 5G, lean 4x4
+        if "5g" in txt2 or "nr" in txt2:
+            return "4x4"
+        return default
+    except Exception:
+        return default
+
+def antenna_options_for(router_model: str, tech: str, mimo: str) -> Dict[str, Any]:
+    q_stationary = f"{router_model} {tech} {mimo} omni stationary pole wall fixed site Parsec"
+    q_vehicle = f"{router_model} {tech} {mimo} omni vehicle mobile magnetic through-bolt Parsec"
+
+    cand_stationary = parsec_retrieve(q_stationary, top_k=12)
+    cand_vehicle = parsec_retrieve(q_vehicle, top_k=12)
+
+    s = choose_best_parsec(cand_stationary, mode="stationary")
+    v = choose_best_parsec(cand_vehicle, mode="vehicle")
+
+    s.update({"mimo": mimo, "why": "Stationary omni best match."})
+    v.update({"mimo": mimo, "why": "Vehicle omni best match."})
+
+    return {"stationary_omni": s, "vehicle_omni": v, "sources":["parsec_rag"]}
+
+
+# ============================
+# Install-ready checklist
+# ============================
+def install_ready_checklist(current_sku: str, repl: Dict[str,Any], ant: Dict[str,Any]) -> str:
+    st = ant.get("stationary_omni", {})
+    vh = ant.get("vehicle_omni", {})
+    if client is not None:
+        sys = "Create a short, install-ready checklist for a Verizon rep. Return markdown only."
+        payload = {"current_device": current_sku, "replacements": repl, "antennas": {"stationary": st, "vehicle": vh}}
+        resp = client.responses.create(
+            model=OPENAI_MODEL,
+            reasoning=OPENAI_REASONING,
+            input=[{"role":"system","content":sys},{"role":"user","content":json.dumps(payload)}],
+            max_output_tokens=520,
+        )
+        return (getattr(resp, "output_text", "") or "").strip()
+    return "\n".join([
+        "### Install-ready checklist",
+        f"- Current device: {current_sku}",
+        f"- 5G replacement: {repl.get('repl_5g','')}",
+        f"- 4G alternative: {repl.get('repl_4g','Not applicable')}",
+        f"- Stationary omni antenna: {st.get('name','')} (PN {st.get('part_number','')})",
+        f"- Vehicle omni antenna: {vh.get('name','')} (PN {vh.get('part_number','')})",
+        "- Next steps: confirm mounting + cable lengths + power; place order; schedule install.",
+    ])
+
+
+# ============================
+# Batch mode (NO GPT)
+# ============================
+def parse_batch_inputs(text_blob: str, file_obj: Any) -> List[str]:
+    items: List[str] = []
+    if file_obj is not None:
+        try:
+            path = file_obj.name if hasattr(file_obj, "name") else str(file_obj)
+            df = pd.read_csv(path)
+            col = df.columns[0]
+            items.extend([str(x).strip() for x in df[col].tolist() if str(x).strip()])
+        except Exception:
+            pass
+    if text_blob:
+        for ln in str(text_blob).splitlines():
+            ln = ln.strip()
+            if ln:
+                items.append(ln)
+    seen=set()
+    out=[]
+    for x in items:
+        k=norm_text(x)
+        if k and k not in seen:
+            seen.add(k); out.append(x)
+    return out
+
+def run_batch(text_blob: str, file_obj: Any, include_antennas: bool):
+    inputs = parse_batch_inputs(text_blob, file_obj)
+    if not inputs:
+        return "", None, None, ""
+
+    rows=[]
+    for item in inputs:
+        res = resolve_device(item)
+        if res.get("mode") != "ok":
+            rows.append({"Input": item, "Matched":"", "Status":"Needs review", "EOS":"", "EOL":"", "4G alternative":"", "5G replacement":"", "Notes":"Not found/ambiguous"})
+            continue
+
+        life_row = df_eos.iloc[int(res["row_idx"])]
+        eos, eol, status = row_to_dates_and_status(life_row)
+        repl = pick_replacements_lifecycle(life_row, status, use_gpt=False)
+
+        rows.append({
+            "Input": item,
+            "Matched": str(life_row.get("sku","")),
+            "Status": status,
+            "EOS": eos,
+            "EOL": eol,
+            "4G alternative": repl.get("repl_4g",""),
+            "5G replacement": repl.get("repl_5g",""),
+            "Notes": "",
+        })
+
+    out_df = pd.DataFrame(rows)
+    counts = out_df["Status"].value_counts(dropna=False).to_dict()
+    top_5g = out_df["5G replacement"].value_counts(dropna=False).head(5).to_dict()
+    summary = f"Rows: {len(out_df)} | " + " | ".join([f"{k}: {v}" for k,v in counts.items()])
+    rollup = "Top 5G recommendations:\n" + "\n".join([f"- {k}: {v}" for k,v in top_5g.items() if str(k).strip()])
+
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
+    out_df.to_csv(tmp.name, index=False)
+
+    return summary, out_df, tmp.name, rollup
+
+
+# ============================
+# Output
+# ============================
+def assemble_output(life_row: pd.Series, status: str, eos: str, eol: str, repl: Dict[str,Any], ant: Dict[str,Any]) -> str:
+    current_name = f"{life_row.get('sku','')} — {life_row.get('description','')}".strip(" —")
+    st = ant.get("stationary_omni", {})
+    vh = ant.get("vehicle_omni", {})
+
+    lines = []
+    lines.append(f"1. Current device: **{current_name}**")
+    lines.append(f"2. Status: **{status}**")
+    lines.append(f"3. End of Sale date: **{eos}**")
+    lines.append(f"4. End of Life date: **{eol}**")
+    lines.append(f"5. 4G alternative (lifecycle): **{repl.get('repl_4g','Not applicable')}**")
+    lines.append(f"6. 5G replacement (lifecycle): **{repl.get('repl_5g','Not listed')}**")
+    lines.append("7. Antenna options (Parsec-only):")
+    conn_s = f" | Conn: {st.get('connectors','')}" if st.get("connectors") else ""
+    conn_v = f" | Conn: {vh.get('connectors','')}" if vh.get("connectors") else ""
+    lines.append(f"   - Stationary (Omni): **{st.get('name','')}** (Part #: {st.get('part_number','')}) — {st.get('description','')} — MIMO: {st.get('mimo','')}{conn_s}")
+    lines.append(f"   - Vehicle (Omni): **{vh.get('name','')}** (Part #: {vh.get('part_number','')}) — {vh.get('description','')} — MIMO: {vh.get('mimo','')}{conn_v}")
+
+    lines.append("\nSources (debug):")
+    for s in repl.get("sources", []) if isinstance(repl.get("sources"), list) else []:
+        lines.append(f"- {s}")
+    lines.append("- ParsecCatalog.pdf (local RAG)")
+    lines.append("- routers_eos_eol_by_sku.csv (replacements)")
+    return "\n".join(lines)
+
+
+# ============================
+# Gradio callbacks
+# IMPORTANT: no dict state and ALL events have api_name=False (prevents api_info schema generation)
+# ============================
+def run_lookup(user_text: str, st_json: str):
+    user_text = str(user_text or "").strip()
+    if not user_text:
+        return "Enter a router SKU/model.", gr.update(visible=False), gr.update(visible=False), "{}", ""
+
+    res = resolve_device(user_text)
+
+    if res.get("mode") == "pick":
+        opts = res.get("options", [])
+        choices = [o["label"] for o in opts]
+        st2 = {"mode":"pick","options": opts, "raw": user_text}
+        return "Did you mean A or B? Pick one, then click Use selection.", gr.update(choices=choices, value=None, visible=True), gr.update(visible=True), state_dump(st2), ""
+
+    if res.get("mode") != "ok":
+        return "Not found.", gr.update(visible=False), gr.update(visible=False), "{}", ""
+
+    life_row = df_eos.iloc[int(res["row_idx"])]
+    eos, eol, status = row_to_dates_and_status(life_row)
+
+    repl = pick_replacements_lifecycle(life_row, status, use_gpt=True)
+    canon_make = str(life_row.get("_canon_make","UNKNOWN"))
+    mimo = infer_mimo_for_5g(repl.get("repl_5g",""), canon_make)
+    tech = "5G" if repl.get("repl_5g") and repl.get("repl_5g") != "Not listed" else ("4G" if device_is_4g(life_row) else "Unknown")
+    ant = antenna_options_for(repl.get("repl_5g") or str(life_row.get("sku","")), tech, mimo)
+
+    output = assemble_output(life_row, status, eos, eol, repl, ant)
+    st_out = {"row_idx": int(res["row_idx"]), "repl": repl, "ant": ant, "raw": user_text}
+    return output, gr.update(visible=False), gr.update(visible=False), state_dump(st_out), ""
+
+def use_selection(selected_label: str, st_json: str):
+    st = state_load(st_json)
+    if not st or st.get("mode") != "pick":
+        return "Run a search first.", gr.update(visible=False), gr.update(visible=False), "{}", ""
+
+    if not selected_label:
+        return "Pick A or B first.", gr.update(visible=True), gr.update(visible=True), st_json, ""
+
+    chosen_row = None
+    for o in st.get("options", []):
+        if o.get("label") == selected_label:
+            chosen_row = int(o["row_idx"])
+            break
+    if chosen_row is None:
+        return "Pick a valid option.", gr.update(visible=True), gr.update(visible=True), st_json, ""
+
+    life_row = df_eos.iloc[int(chosen_row)]
+    eos, eol, status = row_to_dates_and_status(life_row)
+
+    repl = pick_replacements_lifecycle(life_row, status, use_gpt=True)
+    canon_make = str(life_row.get("_canon_make","UNKNOWN"))
+    mimo = infer_mimo_for_5g(repl.get("repl_5g",""), canon_make)
+    tech = "5G" if repl.get("repl_5g") and repl.get("repl_5g") != "Not listed" else ("4G" if device_is_4g(life_row) else "Unknown")
+    ant = antenna_options_for(repl.get("repl_5g") or str(life_row.get("sku","")), tech, mimo)
+
+    output = assemble_output(life_row, status, eos, eol, repl, ant)
+    st_out = {"row_idx": int(chosen_row), "repl": repl, "ant": ant, "raw": st.get("raw","")}
+    return output, gr.update(visible=False), gr.update(visible=False), state_dump(st_out), ""
+
+def make_install_ready(st_json: str):
+    st = state_load(st_json)
+    if not st or "row_idx" not in st:
+        return "Run a lookup first."
+    life_row = df_eos.iloc[int(st["row_idx"])]
+    current_sku = str(life_row.get("sku","") or "")
+    return install_ready_checklist(current_sku, st.get("repl", {}) or {}, st.get("ant", {}) or {})
+
+
+# ============================
+# UI
+# ============================
+with gr.Blocks(title="Only-Routers") as demo:
+    gr.Markdown("## Only-Routers\nSingle lookup + Batch upload for Verizon reps.")
+
+    with gr.Tabs():
+        with gr.Tab("Single"):
+            user_text = gr.Textbox(label="Router SKU or model", placeholder="Examples: IBR650B, AER1600, ES450, WR21, RUT240", lines=1)
+            st = gr.State("{}")  # JSON string
+
+            check_btn = gr.Button("Check", variant="primary")
+            pick_dd = gr.Dropdown(label="Pick A or B", choices=[], visible=False)
+            use_btn = gr.Button("Use selection", visible=False)
+
+            output_md = gr.Markdown()
+
+            install_btn = gr.Button("Make install-ready checklist")
+            install_md = gr.Markdown()
+
+            check_btn.click(fn=run_lookup, inputs=[user_text, st], outputs=[output_md, pick_dd, use_btn, st, install_md], api_name=False)
+            use_btn.click(fn=use_selection, inputs=[pick_dd, st], outputs=[output_md, pick_dd, use_btn, st, install_md], api_name=False)
+            install_btn.click(fn=make_install_ready, inputs=[st], outputs=[install_md], api_name=False)
+
+        with gr.Tab("Batch"):
+            gr.Markdown("Paste one per line or upload a CSV (first column). Batch runs fast (no GPT).")
+            batch_text = gr.Textbox(label="Paste devices (one per line)", lines=8, placeholder="WR21\nRUT240\nIBR650B")
+            batch_file = gr.File(label="Upload CSV", file_types=[".csv"])
+            include_ant = gr.Checkbox(label="Include antenna picks (slower)", value=False)
+            run_btn = gr.Button("Run batch", variant="primary")
+
+            summary_md = gr.Markdown()
+            rollup_md = gr.Markdown()
+            table = gr.Dataframe(interactive=False, wrap=True)
+            dl = gr.File(label="Download results CSV")
+
+            run_btn.click(fn=run_batch, inputs=[batch_text, batch_file, include_ant], outputs=[summary_md, table, dl, rollup_md], api_name=False)
+
+# IMPORTANT: On Spaces, demo.launch() is correct; do NOT use share=True.
+demo.launch(show_api=False)

Updates/only-routers_ai_poc_hf_fixed_v7.ipynb

@@ -0,0 +1,1207 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b10cd58a",
+   "metadata": {},
+   "source": [
+    "# Only-Routers (HF fixed v7)\n",
+    "\n",
+    "Adds replacement feature table + manufacturer link; enforces 5G->4x4 antenna.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a7e8886",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import re\n",
+    "import json\n",
+    "import math\n",
+    "import hashlib\n",
+    "import tempfile\n",
+    "from dataclasses import dataclass\n",
+    "from datetime import datetime, date\n",
+    "from typing import Any, Dict, List, Optional, Tuple\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import fitz  # PyMuPDF\n",
+    "import faiss\n",
+    "from sentence_transformers import SentenceTransformer\n",
+    "from rapidfuzz import fuzz, process\n",
+    "\n",
+    "import gradio as gr\n",
+    "from openai import OpenAI\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Settings\n",
+    "# ============================\n",
+    "TODAY = date(2026, 1, 18)\n",
+    "OPENAI_MODEL = \"gpt-5.2\"\n",
+    "OPENAI_REASONING = {\"effort\": \"high\"}\n",
+    "MATCH_OK = 80\n",
+    "\n",
+    "EMBED_MODEL_NAME = \"sentence-transformers/all-MiniLM-L6-v2\"\n",
+    "PARSEC_CONTEXT_BEFORE = 900\n",
+    "PARSEC_CONTEXT_AFTER = 1600\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# OpenAI client (HF Space secret: OPENAI_API_KEY)\n",
+    "# ============================\n",
+    "API_KEY = os.getenv(\"OPENAI_API_KEY\", \"\").strip()\n",
+    "client = OpenAI(api_key=API_KEY) if API_KEY else None\n",
+    "\n",
+    "# ----------------------------\n",
+    "# Gradio state helpers\n",
+    "# Keep state as a JSON STRING to avoid schema issues on Hugging Face.\n",
+    "# ----------------------------\n",
+    "def state_load(st_json: str) -> Dict[str, Any]:\n",
+    "    try:\n",
+    "        if not st_json:\n",
+    "            return {}\n",
+    "        return json.loads(st_json) if isinstance(st_json, str) else {}\n",
+    "    except Exception:\n",
+    "        return {}\n",
+    "\n",
+    "def state_dump(st: Dict[str, Any]) -> str:\n",
+    "    try:\n",
+    "        return json.dumps(st or {}, ensure_ascii=False)\n",
+    "    except Exception:\n",
+    "        return \"{}\"\n",
+    "\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Helpers\n",
+    "# ============================\n",
+    "def norm_text(s: Any) -> str:\n",
+    "    try:\n",
+    "        if s is None or (isinstance(s, float) and math.isnan(s)) or pd.isna(s):\n",
+    "            return \"\"\n",
+    "    except Exception:\n",
+    "        pass\n",
+    "    s = str(s).strip().lower()\n",
+    "    s = re.sub(r\"[^a-z0-9\\s\\-\\/]\", \" \", s)\n",
+    "    s = re.sub(r\"\\s+\", \" \", s).strip()\n",
+    "    return s\n",
+    "\n",
+    "def safe_str(v: Any) -> str:\n",
+    "    if v is None or (isinstance(v, float) and pd.isna(v)) or pd.isna(v):\n",
+    "        return \"\"\n",
+    "    return str(v).strip()\n",
+    "\n",
+    "def is_5g(modem_type: Any) -> bool:\n",
+    "    s = norm_text(modem_type)\n",
+    "    return (\"5g\" in s) or (\"nr\" in s)\n",
+    "\n",
+    "def json_load_safe(s: str) -> Dict[str, Any]:\n",
+    "    try:\n",
+    "        return json.loads(s)\n",
+    "    except Exception:\n",
+    "        return {}\n",
+    "\n",
+    "def gpt_json(system: str, payload: Dict[str, Any], max_tokens: int = 600) -> Dict[str, Any]:\n",
+    "    if client is None:\n",
+    "        return {}\n",
+    "    resp = client.responses.create(\n",
+    "        model=OPENAI_MODEL,\n",
+    "        reasoning=OPENAI_REASONING,\n",
+    "        input=[{\"role\":\"system\",\"content\":system},{\"role\":\"user\",\"content\":json.dumps(payload)}],\n",
+    "        max_output_tokens=max_tokens,\n",
+    "    )\n",
+    "    return json_load_safe(getattr(resp, \"output_text\", \"\") or \"\")\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Load data\n",
+    "# ============================\n",
+    "EOS_PATH = \"routers_eos_eol_by_sku.csv\"\n",
+    "DEC_PATH = \"dec2025routers.csv\"\n",
+    "PARSEC_PDF = \"ParsecCatalog.pdf\"\n",
+    "\n",
+    "if not os.path.exists(EOS_PATH):\n",
+    "    raise FileNotFoundError(f\"Missing {EOS_PATH} in repo.\")\n",
+    "if not os.path.exists(DEC_PATH):\n",
+    "    raise FileNotFoundError(f\"Missing {DEC_PATH} in repo.\")\n",
+    "if not os.path.exists(PARSEC_PDF):\n",
+    "    raise FileNotFoundError(f\"Missing {PARSEC_PDF} in repo.\")\n",
+    "\n",
+    "df_eos = pd.read_csv(EOS_PATH).copy()\n",
+    "df_dec = pd.read_csv(DEC_PATH).copy()\n",
+    "\n",
+    "\n",
+    "def _canonize_eos_columns(df: pd.DataFrame) -> pd.DataFrame:\n",
+    "    \"\"\"Normalize lifecycle CSV column names (case-insensitive) and create expected columns.\"\"\"\n",
+    "    # Map various header spellings to canonical names used by the app\n",
+    "    mapping = {}\n",
+    "    for c in df.columns:\n",
+    "        k = str(c).strip().lower().replace(\" \", \"_\")\n",
+    "        if k in {\"sku\", \"model\", \"device\", \"device_sku\"}:\n",
+    "            mapping[c] = \"sku\"\n",
+    "        elif k in {\"manufacturer\", \"make\", \"vendor\"}:\n",
+    "            mapping[c] = \"manufacturer\"\n",
+    "        elif k in {\"device_type\", \"type\"}:\n",
+    "            mapping[c] = \"device_type\"\n",
+    "        elif k in {\"end_of_sale\", \"eos\", \"end_sale\", \"end_of_sales\"}:\n",
+    "            mapping[c] = \"end_of_sale\"\n",
+    "        elif k in {\"end_of_life\", \"eol\", \"end_life\"}:\n",
+    "            mapping[c] = \"end_of_life\"\n",
+    "        elif k in {\"suggested_replacement\", \"replacement_4g\", \"lte_replacement\", \"replacement_lte\", \"replacement\"}:\n",
+    "            mapping[c] = \"suggested_replacement\"\n",
+    "        elif k in {\"advanced_5g_option\", \"replacement_5g\", \"fiveg_replacement\", \"5g_replacement\", \"upgrade_5g\"}:\n",
+    "            mapping[c] = \"advanced_5g_option\"\n",
+    "        elif k in {\"region\", \"market\"}:\n",
+    "            mapping[c] = \"region\"\n",
+    "        elif k in {\"notes\", \"note\"}:\n",
+    "            mapping[c] = \"notes\"\n",
+    "        elif k in {\"description\", \"device_description\", \"name\"}:\n",
+    "            mapping[c] = \"description\"\n",
+    "\n",
+    "    df = df.rename(columns=mapping).copy()\n",
+    "\n",
+    "    # Create expected columns if missing\n",
+    "    if \"sku\" not in df.columns:\n",
+    "        # Try the common capitalized header as a fallback\n",
+    "        if \"SKU\" in df.columns:\n",
+    "            df[\"sku\"] = df[\"SKU\"].astype(str)\n",
+    "        else:\n",
+    "            df[\"sku\"] = \"\"\n",
+    "\n",
+    "    if \"manufacturer\" not in df.columns:\n",
+    "        df[\"manufacturer\"] = \"\"\n",
+    "\n",
+    "    if \"device_type\" not in df.columns:\n",
+    "        df[\"device_type\"] = \"\"\n",
+    "\n",
+    "    if \"description\" not in df.columns:\n",
+    "        # If the simplified file removed description, use SKU as description (still searchable)\n",
+    "        df[\"description\"] = df[\"sku\"].astype(str)\n",
+    "\n",
+    "    if \"notes\" not in df.columns:\n",
+    "        df[\"notes\"] = \"\"\n",
+    "\n",
+    "    if \"region\" not in df.columns:\n",
+    "        df[\"region\"] = \"\"\n",
+    "\n",
+    "    if \"suggested_replacement\" not in df.columns:\n",
+    "        df[\"suggested_replacement\"] = \"\"\n",
+    "\n",
+    "    if \"advanced_5g_option\" not in df.columns:\n",
+    "        df[\"advanced_5g_option\"] = \"\"\n",
+    "\n",
+    "    if \"end_of_sale\" not in df.columns:\n",
+    "        df[\"end_of_sale\"] = \"\"\n",
+    "\n",
+    "    if \"end_of_life\" not in df.columns:\n",
+    "        df[\"end_of_life\"] = \"\"\n",
+    "\n",
+    "    return df\n",
+    "\n",
+    "df_eos = _canonize_eos_columns(df_eos)\n",
+    "\n",
+    "\n",
+    "def region_ok(x: Any) -> bool:\n",
+    "    s = str(x or \"\").strip().lower()\n",
+    "    if not s:\n",
+    "        return True\n",
+    "    if \"not specified\" in s:\n",
+    "        return True\n",
+    "    if \"north america\" in s:\n",
+    "        return True\n",
+    "    if re.search(r\"\\busa\\b\", s):\n",
+    "        return True\n",
+    "    if re.search(r\"\\bunited\\s+states\\b\", s):\n",
+    "        return True\n",
+    "    if re.search(r\"\\bu\\.?s\\.?\\b\", s):\n",
+    "        return True\n",
+    "    return False\n",
+    "\n",
+    "if \"region\" in df_eos.columns:\n",
+    "    df_eos = df_eos[df_eos[\"region\"].apply(region_ok)].reset_index(drop=True)\n",
+    "\n",
+    "# Maker mapping (includes Teltonika)\n",
+    "CANON_MAKER = {\n",
+    "    \"CRADLEPOINT\": {\"cradlepoint\", \"ericsson\", \"ericsson enterprise wireless\"},\n",
+    "    \"SIERRA\": {\"sierra\", \"sierra wireless\", \"semtech\", \"airlink\"},\n",
+    "    \"FEENEY\": {\"feeney\", \"feeney wireless\", \"inseego\"},\n",
+    "    \"DIGI\": {\"digi\", \"accelerated\", \"accelerated concepts\"},\n",
+    "    \"CISCO_MERAKI\": {\"meraki\", \"cisco meraki\"},\n",
+    "    \"CISCO\": {\"cisco\"},\n",
+    "    \"TELTONIKA\": {\"teltonika\"},\n",
+    "}\n",
+    "\n",
+    "def canon_maker_from_text(s: Any) -> str:\n",
+    "    t = norm_text(s)\n",
+    "    for canon, terms in CANON_MAKER.items():\n",
+    "        for term in terms:\n",
+    "            if term in t:\n",
+    "                return canon\n",
+    "    return \"UNKNOWN\"\n",
+    "\n",
+    "df_eos[\"_canon_make\"] = df_eos[\"manufacturer\"].apply(canon_maker_from_text) if \"manufacturer\" in df_eos.columns else \"UNKNOWN\"\n",
+    "df_eos[\"_norm_sku\"] = df_eos[\"sku\"].apply(norm_text) if \"sku\" in df_eos.columns else \"\"\n",
+    "df_eos[\"_norm_desc\"] = df_eos[\"description\"].apply(norm_text) if \"description\" in df_eos.columns else \"\"\n",
+    "df_eos[\"_norm_notes\"] = df_eos[\"notes\"].apply(norm_text) if \"notes\" in df_eos.columns else \"\"\n",
+    "\n",
+    "df_dec[\"_canon_make\"] = df_dec[\"Make\"].apply(canon_maker_from_text) if \"Make\" in df_dec.columns else \"UNKNOWN\"\n",
+    "df_dec[\"_norm_model\"] = df_dec[\"Model\"].apply(norm_text) if \"Model\" in df_dec.columns else \"\"\n",
+    "df_dec[\"_is5g\"] = df_dec[\"Modem Type\"].apply(is_5g) if \"Modem Type\" in df_dec.columns else False\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Date helpers\n",
+    "# ============================\n",
+    "@dataclass\n",
+    "class ParsedDate:\n",
+    "    raw: str\n",
+    "    kind: str\n",
+    "    value: Optional[date]\n",
+    "\n",
+    "def parse_date_field(x: Any) -> ParsedDate:\n",
+    "    raw = str(x or \"\").strip()\n",
+    "    if not raw:\n",
+    "        return ParsedDate(raw=\"\", kind=\"missing\", value=None)\n",
+    "\n",
+    "    # Common US formats: M/D/YY or M/D/YYYY (e.g., 6/24/24, 9/30/21)\n",
+    "    for fmt in (\"%m/%d/%y\", \"%m/%d/%Y\", \"%-m/%-d/%y\", \"%-m/%-d/%Y\"):\n",
+    "        try:\n",
+    "            dt = datetime.strptime(raw, fmt).date()\n",
+    "            return ParsedDate(raw=raw, kind=\"full\", value=dt)\n",
+    "        except Exception:\n",
+    "            pass\n",
+    "\n",
+    "    # ISO-ish: YYYY\n",
+    "    if re.fullmatch(r\"\\d{4}\", raw):\n",
+    "        y = int(raw)\n",
+    "        if y == TODAY.year:\n",
+    "            return ParsedDate(raw=raw, kind=\"year\", value=date(y, 1, 1))\n",
+    "        if y < TODAY.year:\n",
+    "            return ParsedDate(raw=raw, kind=\"year\", value=date(y, 1, 1))\n",
+    "        return ParsedDate(raw=raw, kind=\"year\", value=date(y, 12, 31))\n",
+    "\n",
+    "    # YYYY-MM\n",
+    "    if re.fullmatch(r\"\\d{4}-\\d{2}\", raw):\n",
+    "        try:\n",
+    "            y, m = raw.split(\"-\")\n",
+    "            return ParsedDate(raw=raw, kind=\"year_month\", value=date(int(y), int(m), 1))\n",
+    "        except Exception:\n",
+    "            return ParsedDate(raw=raw, kind=\"bad\", value=None)\n",
+    "\n",
+    "    # YYYY-MM-DD\n",
+    "    if re.fullmatch(r\"\\d{4}-\\d{2}-\\d{2}\", raw):\n",
+    "        try:\n",
+    "            dt = datetime.strptime(raw, \"%Y-%m-%d\").date()\n",
+    "            return ParsedDate(raw=raw, kind=\"full\", value=dt)\n",
+    "        except Exception:\n",
+    "            return ParsedDate(raw=raw, kind=\"bad\", value=None)\n",
+    "\n",
+    "    # Last resort: leave as raw (unparsed)\n",
+    "    return ParsedDate(raw=raw, kind=\"bad\", value=None)\n",
+    "\n",
+    "    if re.fullmatch(r\"\\d{4}-\\d{2}-\\d{2}\", raw):\n",
+    "        try:\n",
+    "            dt = datetime.strptime(raw, \"%Y-%m-%d\").date()\n",
+    "            return ParsedDate(raw=raw, kind=\"full\", value=dt)\n",
+    "        except Exception:\n",
+    "            return ParsedDate(raw=raw, kind=\"bad\", value=None)\n",
+    "\n",
+    "    return ParsedDate(raw=raw, kind=\"bad\", value=None)\n",
+    "\n",
+    "def display_date(pd_: ParsedDate) -> str:\n",
+    "    if pd_.kind == \"missing\":\n",
+    "        return \"Not listed\"\n",
+    "    if pd_.kind == \"bad\":\n",
+    "        return pd_.raw or \"Not listed\"\n",
+    "    return pd_.raw\n",
+    "\n",
+    "def status_from_eos_eol(eos: ParsedDate, eol: ParsedDate) -> str:\n",
+    "    if eos.value is None and eol.value is None:\n",
+    "        return \"Unknown\"\n",
+    "    if eol.value is not None and eol.value <= TODAY:\n",
+    "        return \"End of Life\"\n",
+    "    if eos.value is not None and eos.value <= TODAY:\n",
+    "        return \"End of Sale\"\n",
+    "    return \"Active\"\n",
+    "\n",
+    "def row_to_dates_and_status(row: pd.Series) -> Tuple[str, str, str]:\n",
+    "    eos = parse_date_field(row.get(\"end_of_sale\"))\n",
+    "    eol = parse_date_field(row.get(\"end_of_life\"))\n",
+    "    return display_date(eos), display_date(eol), status_from_eos_eol(eos, eol)\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Embeddings + Parsec index\n",
+    "# ============================\n",
+    "embedder = SentenceTransformer(EMBED_MODEL_NAME)\n",
+    "\n",
+    "def extract_pdf_text_pages(path: str) -> List[str]:\n",
+    "    doc = fitz.open(path)\n",
+    "    return [doc[i].get_text(\"text\") for i in range(len(doc))]\n",
+    "\n",
+    "def build_parsec_cards(pages: List[str]) -> List[str]:\n",
+    "    cards = []\n",
+    "    for p in pages:\n",
+    "        for m in re.finditer(r\"Standard\\s+SKU:\", p):\n",
+    "            start = max(0, m.start() - PARSEC_CONTEXT_BEFORE)\n",
+    "            end = min(len(p), m.start() + PARSEC_CONTEXT_AFTER)\n",
+    "            c = p[start:end].strip()\n",
+    "            if len(c) >= 200:\n",
+    "                cards.append(c)\n",
+    "    out, seen = [], set()\n",
+    "    for c in cards:\n",
+    "        h = hashlib.sha1(c.encode(\"utf-8\")).hexdigest()\n",
+    "        if h not in seen:\n",
+    "            seen.add(h); out.append(c)\n",
+    "    return out\n",
+    "\n",
+    "parsec_cards = build_parsec_cards(extract_pdf_text_pages(PARSEC_PDF))\n",
+    "parsec_emb = embedder.encode(parsec_cards, batch_size=64, show_progress_bar=False, normalize_embeddings=True)\n",
+    "parsec_emb = np.asarray(parsec_emb, dtype=np.float32)\n",
+    "parsec_index = faiss.IndexFlatIP(parsec_emb.shape[1])\n",
+    "parsec_index.add(parsec_emb)\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Device resolution\n",
+    "# ============================\n",
+    "def label_for_row(i: int) -> str:\n",
+    "    r = df_eos.iloc[i]\n",
+    "    return f\"{r.get('sku','')} — {r.get('manufacturer','')} — {r.get('description','')}\"[:220]\n",
+    "\n",
+    "EOS_LABELS = [label_for_row(i) for i in range(len(df_eos))]\n",
+    "EOS_CORPUS = []\n",
+    "for _, r in df_eos.iterrows():\n",
+    "    EOS_CORPUS.append(\" \".join([r.get(\"_norm_sku\",\"\"), r.get(\"_canon_make\",\"\"), r.get(\"_norm_desc\",\"\"), r.get(\"_norm_notes\",\"\")]))\n",
+    "\n",
+    "def local_candidates(query: str, top_k: int = 6) -> List[Tuple[int, int, str]]:\n",
+    "    q = norm_text(query)\n",
+    "    hits = process.extract(q, EOS_CORPUS, scorer=fuzz.WRatio, limit=top_k)\n",
+    "    return [(int(idx), int(score), EOS_LABELS[int(idx)]) for _, score, idx in hits]\n",
+    "\n",
+    "def gpt_choose_device(user_text: str, candidates: List[Tuple[int,int,str]]) -> Dict[str, Any]:\n",
+    "    if client is None:\n",
+    "        return {}\n",
+    "    sys = \"Pick which router the user meant. Never invent. Return strict JSON only.\"\n",
+    "    payload = {\n",
+    "        \"user_input\": user_text,\n",
+    "        \"candidates\": [{\"row_idx\": i, \"score\": s, \"label\": lbl} for (i,s,lbl) in candidates],\n",
+    "        \"rules\": [\n",
+    "            \"If one is clearly correct, return mode='ok' with row_idx.\",\n",
+    "            \"If two are plausible, return mode='pick' with top 2 options.\"\n",
+    "        ],\n",
+    "        \"output_schema\": {\"mode\":\"ok|pick\",\"row_idx\":\"int\",\"options\":[{\"row_idx\":\"int\",\"label\":\"string\"}]}\n",
+    "    }\n",
+    "    return gpt_json(sys, payload, max_tokens=280)\n",
+    "\n",
+    "def resolve_device(user_text: str) -> Dict[str, Any]:\n",
+    "    q = norm_text(user_text)\n",
+    "    exact = df_eos.index[df_eos[\"_norm_sku\"] == q].tolist()\n",
+    "    if len(exact) == 1:\n",
+    "        return {\"mode\":\"ok\",\"row_idx\": int(exact[0])}\n",
+    "    if len(exact) > 1:\n",
+    "        opts = [{\"row_idx\": int(i), \"label\": EOS_LABELS[int(i)]} for i in exact[:2]]\n",
+    "        return {\"mode\":\"pick\",\"options\": opts}\n",
+    "\n",
+    "    cands = local_candidates(user_text, top_k=6)\n",
+    "    if not cands:\n",
+    "        return {\"mode\":\"not_found\"}\n",
+    "\n",
+    "    if cands[0][1] >= 95 and (len(cands) == 1 or (cands[0][1] - cands[1][1]) >= 8):\n",
+    "        return {\"mode\":\"ok\",\"row_idx\": cands[0][0]}\n",
+    "\n",
+    "    g = gpt_choose_device(user_text, cands)\n",
+    "    if g.get(\"mode\") == \"ok\" and isinstance(g.get(\"row_idx\"), int):\n",
+    "        return {\"mode\":\"ok\",\"row_idx\": int(g[\"row_idx\"])}\n",
+    "\n",
+    "    if g.get(\"mode\") == \"pick\":\n",
+    "        opts = g.get(\"options\", []) or []\n",
+    "        opts2 = [{\"row_idx\": int(o[\"row_idx\"]), \"label\": str(o[\"label\"])} for o in opts[:2] if \"row_idx\" in o]\n",
+    "        if opts2:\n",
+    "            return {\"mode\":\"pick\",\"options\": opts2}\n",
+    "\n",
+    "    if len(cands) > 1:\n",
+    "        return {\"mode\":\"pick\",\"options\":[{\"row_idx\":cands[0][0],\"label\":cands[0][2]},{\"row_idx\":cands[1][0],\"label\":cands[1][2]}]}\n",
+    "    return {\"mode\":\"pick\",\"options\":[{\"row_idx\":cands[0][0],\"label\":cands[0][2]}]}\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Replacements — lifecycle CSV source of truth\n",
+    "# ============================\n",
+    "def extract_model_token(text: str) -> str:\n",
+    "    s = safe_str(text)\n",
+    "    if not s:\n",
+    "        return \"\"\n",
+    "    parts = [p.strip() for p in s.split(\"|\") if p.strip()]\n",
+    "    candidates = parts[::-1] if parts else [s]\n",
+    "    for cand in candidates:\n",
+    "        m = re.search(r\"\\bRUT[A-Z]?\\d{2,4}\\b\", cand.upper())\n",
+    "        if m:\n",
+    "            return m.group(0).upper()\n",
+    "        m = re.search(r\"\\bIX\\d{2}\\b\", cand, flags=re.IGNORECASE)\n",
+    "        if m:\n",
+    "            return m.group(0).upper()\n",
+    "        m = re.search(r\"\\b(R\\d{3,4}|E\\d{3,4}|S\\d{3,4})\\b\", cand, flags=re.IGNORECASE)\n",
+    "        if m:\n",
+    "            return m.group(0).upper()\n",
+    "        m = re.search(r\"\\b[A-Z]{1,6}\\d{2,4}[A-Z]?\\b\", cand.upper())\n",
+    "        if m:\n",
+    "            return m.group(0).upper()\n",
+    "    return candidates[0][:60]\n",
+    "\n",
+    "def device_is_4g(row: pd.Series) -> bool:\n",
+    "    # Detect LTE/4G even when the description uses \"Cat 4 / Cat6 / Cat 12\" without saying \"LTE\"\n",
+    "    t = norm_text(row.get(\"description\",\"\")) + \" \" + norm_text(row.get(\"notes\",\"\")) + \" \" + norm_text(row.get(\"sku\",\"\"))\n",
+    "\n",
+    "    # If it explicitly says 5G/NR, treat as not 4G-only\n",
+    "    if (\"5g\" in t) or (\"nr\" in t):\n",
+    "        return False\n",
+    "\n",
+    "    # Classic signals\n",
+    "    if (\"lte\" in t) or (\"4g\" in t):\n",
+    "        return True\n",
+    "\n",
+    "    # LTE category signals (Cat 1..20 are LTE categories; Cat M1/M2 are LTE-M)\n",
+    "    if re.search(r\"\\bcat\\s*[-]?\\s*(m1|m2)\\b\", t):\n",
+    "        return True\n",
+    "\n",
+    "    m = re.search(r\"\\bcat\\s*[-]?\\s*(\\d{1,2})\\b\", t)\n",
+    "    if m:\n",
+    "        try:\n",
+    "            cat = int(m.group(1))\n",
+    "            if 0 < cat <= 20:\n",
+    "                return True\n",
+    "        except Exception:\n",
+    "            pass\n",
+    "\n",
+    "    # If \"cat\" appears at all, it's almost always LTE-family\n",
+    "    if \"cat\" in t:\n",
+    "        return True\n",
+    "\n",
+    "    return False\n",
+    "\n",
+    "    # If it explicitly says 5G/NR, treat as not 4G-only\n",
+    "    if (\"5g\" in t) or (\"nr\" in t):\n",
+    "        return False\n",
+    "\n",
+    "    # Classic signals\n",
+    "    if (\"lte\" in t) or (\"4g\" in t):\n",
+    "        return True\n",
+    "\n",
+    "    # LTE category signals (Cat 1..20 are LTE categories; Cat M1/M2 are LTE-M)\n",
+    "    if re.search(r\"\\bcat\\s*[-]?\\s*(m1|m2)\\b\", t):\n",
+    "        return True\n",
+    "\n",
+    "    m = re.search(r\"\\bcat\\s*[-]?\\s*(\\d{1,2})\\b\", t)\n",
+    "    if m:\n",
+    "        try:\n",
+    "            cat = int(m.group(1))\n",
+    "            if 0 < cat <= 20:\n",
+    "                return True\n",
+    "        except Exception:\n",
+    "            pass\n",
+    "\n",
+    "    # If \"cat\" appears at all, it's almost always LTE-family\n",
+    "    if \"cat\" in t:\n",
+    "        return True\n",
+    "\n",
+    "    return False\n",
+    "\n",
+    "\n",
+    "def candidate_5g_models_from_lifecycle(manufacturer: str) -> List[str]:\n",
+    "    mfr = norm_text(manufacturer)\n",
+    "    pool = df_eos[df_eos[\"manufacturer\"].astype(str).str.lower().eq(mfr)].copy() if \"manufacturer\" in df_eos.columns else df_eos.copy()\n",
+    "    vals = pool[\"advanced_5g_option\"].tolist() if \"advanced_5g_option\" in pool.columns else []\n",
+    "    out, seen = [], set()\n",
+    "    for v in vals:\n",
+    "        tok = extract_model_token(v)\n",
+    "        if tok and tok.lower() != \"nan\" and tok not in seen:\n",
+    "            seen.add(tok); out.append(tok)\n",
+    "    return out\n",
+    "\n",
+    "def candidate_4g_models_from_lifecycle(manufacturer: str) -> List[str]:\n",
+    "    mfr = norm_text(manufacturer)\n",
+    "    pool = df_eos[df_eos[\"manufacturer\"].astype(str).str.lower().eq(mfr)].copy() if \"manufacturer\" in df_eos.columns else df_eos.copy()\n",
+    "    vals = pool[\"suggested_replacement\"].tolist() if \"suggested_replacement\" in pool.columns else []\n",
+    "    out, seen = [], set()\n",
+    "    for v in vals:\n",
+    "        tok = extract_model_token(v)\n",
+    "        if tok and tok.lower() != \"nan\" and tok not in seen:\n",
+    "            seen.add(tok); out.append(tok)\n",
+    "    return out\n",
+    "\n",
+    "def gpt_pick_from_candidates(old_row: pd.Series, candidates: List[str], need: str) -> str:\n",
+    "    if client is None or not candidates:\n",
+    "        return \"\"\n",
+    "    sys = \"Pick the best replacement model. Choose only from candidates. Return strict JSON only.\"\n",
+    "    payload = {\n",
+    "        \"old_device\": {\n",
+    "            \"sku\": str(old_row.get(\"sku\",\"\")),\n",
+    "            \"manufacturer\": str(old_row.get(\"manufacturer\",\"\")),\n",
+    "            \"description\": str(old_row.get(\"description\",\"\")),\n",
+    "            \"need\": need,\n",
+    "        },\n",
+    "        \"candidates\": candidates[:40],\n",
+    "        \"output_schema\": {\"choice\":\"string\"}\n",
+    "    }\n",
+    "    out = gpt_json(sys, payload, max_tokens=240) or {}\n",
+    "    choice = str(out.get(\"choice\",\"\") or \"\").strip()\n",
+    "    return choice if choice in candidates else \"\"\n",
+    "\n",
+    "def fallback_5g_from_dec(canon_make: str) -> str:\n",
+    "    pool5 = df_dec[(df_dec[\"_canon_make\"] == canon_make) & (df_dec[\"_is5g\"] == True)]\n",
+    "    return str(pool5.iloc[0][\"Model\"]).strip() if not pool5.empty else \"\"\n",
+    "\n",
+    "def pick_replacements_lifecycle(row: pd.Series, status: str, use_gpt: bool = True) -> Dict[str, Any]:\n",
+    "    canon = str(row.get(\"_canon_make\",\"UNKNOWN\"))\n",
+    "    manufacturer = str(row.get(\"manufacturer\",\"\") or \"\")\n",
+    "\n",
+    "    sug_raw = safe_str(row.get(\"suggested_replacement\",\"\"))\n",
+    "    adv_raw = safe_str(row.get(\"advanced_5g_option\",\"\"))\n",
+    "\n",
+    "    has_4g_alt = bool(sug_raw.strip())\n",
+    "    has_5g_alt = bool(adv_raw.strip())\n",
+    "\n",
+    "    # Treat as 4G if the description indicates LTE OR lifecycle provides a 4G suggested replacement\n",
+    "    is_4g = device_is_4g(row) or has_4g_alt\n",
+    "\n",
+    "    # Provide 5G option if the unit is 4G, EOS/EOL, or lifecycle explicitly provides advanced_5g_option\n",
+    "    want_5g = is_4g or (status in {\"End of Sale\",\"End of Life\"}) or has_5g_alt\n",
+    "\n",
+    "    # 4G alternative: show whenever lifecycle provides it (or device appears 4G)\n",
+    "    repl_4g = \"Not applicable\"\n",
+    "    if is_4g or has_4g_alt:\n",
+    "        repl_4g = extract_model_token(sug_raw)\n",
+    "        if not repl_4g:\n",
+    "            cand4 = candidate_4g_models_from_lifecycle(manufacturer)\n",
+    "            repl_4g = (gpt_pick_from_candidates(row, cand4, \"4G alternative\") if (use_gpt and client) else \"\") or (cand4[0] if cand4 else \"\")\n",
+    "        if not repl_4g:\n",
+    "            repl_4g = \"Not applicable\"\n",
+    "\n",
+    "    # 5G replacement: prefer lifecycle advanced_5g_option whenever present\n",
+    "    repl_5g = \"Not listed\"\n",
+    "    if want_5g:\n",
+    "        repl_5g = extract_model_token(adv_raw)\n",
+    "        if not repl_5g:\n",
+    "            cand5 = candidate_5g_models_from_lifecycle(manufacturer)\n",
+    "            repl_5g = (gpt_pick_from_candidates(row, cand5, \"5G replacement/upgrade\") if (use_gpt and client) else \"\") or (cand5[0] if cand5 else \"\")\n",
+    "        if not repl_5g:\n",
+    "            repl_5g = fallback_5g_from_dec(canon) or \"Not listed\"\n",
+    "\n",
+    "    if repl_5g.lower() == \"nan\":\n",
+    "        repl_5g = \"Not listed\"\n",
+    "\n",
+    "    return {\"repl_4g\": repl_4g, \"repl_5g\": repl_5g, \"sources\": [\"lifecycle_csv\"] + ([\"gpt\"] if (use_gpt and client) else [])}\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Antennas (Parsec-only)\n",
+    "# ============================\n",
+    "PARSEC_FAMILY_WORDS = {\"chinook\",\"labrador\",\"boxer\",\"bloodhound\",\"husky\",\"beagle\",\"mastiff\",\"collie\",\"shepherd\",\"belgian\",\"australian\",\"terrier\",\"pyrenees\"}\n",
+    "BAD_NAME_MARKERS = {\"customization\",\"standard connectors\",\"connectors\",\"features\",\"benefits\",\"specifications\",\"mechanical\",\"electrical\",\"mounting\",\"accessories\",\"description:\",\"standard sku\"}\n",
+    "\n",
+    "def clean_line(s: str) -> str:\n",
+    "    s = re.sub(r\"\\s+\", \" \", str(s or \"\").strip())\n",
+    "    if re.fullmatch(r\"-[a-z0-9]+\", s.lower()):\n",
+    "        return \"\"\n",
+    "    return s\n",
+    "\n",
+    "def is_bad_name_line(line: str) -> bool:\n",
+    "    low = line.lower()\n",
+    "    if any(m in low for m in BAD_NAME_MARKERS):\n",
+    "        return True\n",
+    "    if re.search(r\"\\b-[a-z0-9]{1,4}\\b\", low) and len(low) <= 25:\n",
+    "        return True\n",
+    "    return False\n",
+    "\n",
+    "def family_from_line(line: str) -> str:\n",
+    "    low = line.lower()\n",
+    "    for fam in PARSEC_FAMILY_WORDS:\n",
+    "        if fam in low:\n",
+    "            return fam.capitalize()\n",
+    "    return \"\"\n",
+    "\n",
+    "def parsec_connectors_from_card(t: str) -> str:\n",
+    "    m = re.search(r\"Standard\\s+Connectors:\\s*(.+)\", t, flags=re.IGNORECASE)\n",
+    "    if m:\n",
+    "        return re.sub(r\"\\s+\", \" \", m.group(1).strip())[:80]\n",
+    "    return \"\"\n",
+    "\n",
+    "def parsec_mounts_from_card(t: str) -> List[str]:\n",
+    "    mounts = []\n",
+    "    for m in re.finditer(r\"Mount:\\s*(.+)\", t, flags=re.IGNORECASE):\n",
+    "        val = re.sub(r\"\\s+\", \" \", m.group(1).strip())\n",
+    "        parts = [p.strip().lower() for p in val.split(\",\") if p.strip()]\n",
+    "        mounts.extend(parts)\n",
+    "    out = []\n",
+    "    seen = set()\n",
+    "    for x in mounts:\n",
+    "        if x not in seen:\n",
+    "            seen.add(x); out.append(x)\n",
+    "    return out\n",
+    "\n",
+    "def parsec_name_from_card(card_text: str) -> str:\n",
+    "    lines = [clean_line(ln) for ln in str(card_text or \"\").splitlines()]\n",
+    "    lines = [ln for ln in lines if ln]\n",
+    "\n",
+    "    for ln in lines:\n",
+    "        if is_bad_name_line(ln):\n",
+    "            continue\n",
+    "        fam = family_from_line(ln)\n",
+    "        if fam:\n",
+    "            return fam\n",
+    "\n",
+    "    sku_i = None\n",
+    "    for i, ln in enumerate(lines):\n",
+    "        if \"standard sku\" in ln.lower():\n",
+    "            sku_i = i\n",
+    "            break\n",
+    "    if sku_i is not None:\n",
+    "        window = lines[max(0, sku_i - 12):sku_i]\n",
+    "        for ln in reversed(window):\n",
+    "            if is_bad_name_line(ln):\n",
+    "                continue\n",
+    "            if 3 <= len(ln) <= 40 and re.search(r\"[A-Za-z]\", ln):\n",
+    "                return ln.split()[0].capitalize()\n",
+    "\n",
+    "    return \"Parsec antenna\"\n",
+    "\n",
+    "def parsec_part_from_card(t: str) -> str:\n",
+    "    m = re.search(r\"Standard\\s+SKU:\\s*([A-Z0-9]+)\", t)\n",
+    "    return m.group(1).strip() if m else \"\"\n",
+    "\n",
+    "def parsec_desc_from_card(t: str) -> str:\n",
+    "    m = re.search(r\"Description:\\s*(.+?)(?:\\n|$)\", t, flags=re.IGNORECASE)\n",
+    "    return re.sub(r\"\\s+\",\" \",m.group(1).strip())[:220] if m else \"\"\n",
+    "\n",
+    "def parsec_retrieve(query: str, top_k: int = 12) -> List[Dict[str, Any]]:\n",
+    "    qv = embedder.encode([query], normalize_embeddings=True)\n",
+    "    qv = np.asarray(qv, dtype=np.float32)\n",
+    "    scores, ids = parsec_index.search(qv, top_k)\n",
+    "    out: List[Dict[str, Any]] = []\n",
+    "    for sc, i in zip(scores[0].tolist(), ids[0].tolist()):\n",
+    "        if 0 <= int(i) < len(parsec_cards):\n",
+    "            card = parsec_cards[int(i)]\n",
+    "            out.append({\n",
+    "                \"score\": float(sc),\n",
+    "                \"name\": parsec_name_from_card(card),\n",
+    "                \"part_number\": parsec_part_from_card(card),\n",
+    "                \"description\": parsec_desc_from_card(card),\n",
+    "                \"connectors\": parsec_connectors_from_card(card),\n",
+    "                \"mounts\": parsec_mounts_from_card(card),\n",
+    "                \"_card\": card.lower(),\n",
+    "            })\n",
+    "    return out\n",
+    "\n",
+    "def choose_best_parsec(cands: List[Dict[str, Any]], mode: str) -> Dict[str, Any]:\n",
+    "    best = None\n",
+    "    best_score = -1e9\n",
+    "\n",
+    "    for c in cands:\n",
+    "        card = c.get(\"_card\",\"\")\n",
+    "        mounts = c.get(\"mounts\", []) or []\n",
+    "        score = float(c.get(\"score\", 0.0))\n",
+    "\n",
+    "        if \"omni\" in card:\n",
+    "            score += 0.6\n",
+    "        if \"directional\" in card:\n",
+    "            score -= 1.5\n",
+    "\n",
+    "        if mode == \"vehicle\":\n",
+    "            if any(\"magnetic\" in m for m in mounts):\n",
+    "                score += 3.0\n",
+    "            if any(\"through\" in m for m in mounts):\n",
+    "                score += 2.0\n",
+    "            if any(\"wall\" in m for m in mounts) or any(\"pole\" in m for m in mounts):\n",
+    "                score -= 1.2\n",
+    "            if \"app: fixed\" in card and \"mobile\" not in card:\n",
+    "                score -= 2.0\n",
+    "\n",
+    "        if mode == \"stationary\":\n",
+    "            if any(\"wall\" in m for m in mounts):\n",
+    "                score += 2.0\n",
+    "            if any(\"pole\" in m for m in mounts):\n",
+    "                score += 1.8\n",
+    "\n",
+    "        if score > best_score:\n",
+    "            best_score = score\n",
+    "            best = c\n",
+    "\n",
+    "    if not best:\n",
+    "        return {\"name\":\"Parsec antenna\",\"part_number\":\"\",\"description\":\"\",\"connectors\":\"\",\"mounts\":[]}\n",
+    "\n",
+    "    best = dict(best)\n",
+    "    best.pop(\"_card\", None)\n",
+    "    return best\n",
+    "\n",
+    "\n",
+    "def infer_mimo_for_5g(repl_5g_model: str) -> str:\n",
+    "    \"\"\"Rule: every 5G router uses a 4x4 antenna.\"\"\"\n",
+    "    return \"4x4\"\n",
+    "\n",
+    "    # If the model name hints 5G, lean 4x4\n",
+    "    if \"5g\" in model.lower() or model.upper().startswith((\"R\", \"E\", \"S\", \"IX\", \"RUTM\")):\n",
+    "        default = \"4x4\"\n",
+    "    else:\n",
+    "        default = \"2x2\"\n",
+    "\n",
+    "    # Use dec2025routers.csv if we can match the model under the same maker family\n",
+    "    try:\n",
+    "        pool = df_dec[df_dec[\"_canon_make\"] == canon_make].copy()\n",
+    "        if pool.empty:\n",
+    "            return default\n",
+    "        hit = process.extractOne(norm_text(model), pool[\"_norm_model\"].tolist(), scorer=fuzz.WRatio)\n",
+    "        if not hit or hit[1] < MATCH_OK:\n",
+    "            return default\n",
+    "        row = pool.iloc[int(hit[2])]\n",
+    "        txt2 = (str(row.get(\"Antennas (internal/external/both)\", \"\")) + \" \" + str(row.get(\"Modem Type\", \"\")) + \" \" + str(row.get(\"Special notes\",\"\"))).lower()\n",
+    "        if \"4x4\" in txt2 or \"4 x 4\" in txt2 or \"4x 4\" in txt2:\n",
+    "            return \"4x4\"\n",
+    "        if \"2x2\" in txt2 or \"2 x 2\" in txt2:\n",
+    "            return \"2x2\"\n",
+    "        # If modem type includes 5G, lean 4x4\n",
+    "        if \"5g\" in txt2 or \"nr\" in txt2:\n",
+    "            return \"4x4\"\n",
+    "        return default\n",
+    "    except Exception:\n",
+    "        return default\n",
+    "\n",
+    "def antenna_options_for(router_model: str, tech: str, mimo: str) -> Dict[str, Any]:\n",
+    "    q_stationary = f\"{router_model} {tech} {mimo} omni stationary pole wall fixed site Parsec\"\n",
+    "    q_vehicle = f\"{router_model} {tech} {mimo} omni vehicle mobile magnetic through-bolt Parsec\"\n",
+    "\n",
+    "    cand_stationary = parsec_retrieve(q_stationary, top_k=12)\n",
+    "    cand_vehicle = parsec_retrieve(q_vehicle, top_k=12)\n",
+    "\n",
+    "    s = choose_best_parsec(cand_stationary, mode=\"stationary\")\n",
+    "    v = choose_best_parsec(cand_vehicle, mode=\"vehicle\")\n",
+    "\n",
+    "    s.update({\"mimo\": mimo, \"why\": \"Stationary omni best match.\"})\n",
+    "    v.update({\"mimo\": mimo, \"why\": \"Vehicle omni best match.\"})\n",
+    "\n",
+    "    return {\"stationary_omni\": s, \"vehicle_omni\": v, \"sources\":[\"parsec_rag\"]}\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Install-ready checklist\n",
+    "# ============================\n",
+    "def install_ready_checklist(current_sku: str, repl: Dict[str,Any], ant: Dict[str,Any]) -> str:\n",
+    "    st = ant.get(\"stationary_omni\", {})\n",
+    "    vh = ant.get(\"vehicle_omni\", {})\n",
+    "    if client is not None:\n",
+    "        sys = \"Create a short, install-ready checklist for a Verizon rep. Return markdown only.\"\n",
+    "        payload = {\"current_device\": current_sku, \"replacements\": repl, \"antennas\": {\"stationary\": st, \"vehicle\": vh}}\n",
+    "        resp = client.responses.create(\n",
+    "            model=OPENAI_MODEL,\n",
+    "            reasoning=OPENAI_REASONING,\n",
+    "            input=[{\"role\":\"system\",\"content\":sys},{\"role\":\"user\",\"content\":json.dumps(payload)}],\n",
+    "            max_output_tokens=520,\n",
+    "        )\n",
+    "        return (getattr(resp, \"output_text\", \"\") or \"\").strip()\n",
+    "    return \"\\n\".join([\n",
+    "        \"### Install-ready checklist\",\n",
+    "        f\"- Current device: {current_sku}\",\n",
+    "        f\"- 5G replacement: {repl.get('repl_5g','')}\",\n",
+    "        f\"- 4G alternative: {repl.get('repl_4g','Not applicable')}\",\n",
+    "        f\"- Stationary omni antenna: {st.get('name','')} (PN {st.get('part_number','')})\",\n",
+    "        f\"- Vehicle omni antenna: {vh.get('name','')} (PN {vh.get('part_number','')})\",\n",
+    "        \"- Next steps: confirm mounting + cable lengths + power; place order; schedule install.\",\n",
+    "    ])\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Batch mode (NO GPT)\n",
+    "# ============================\n",
+    "def parse_batch_inputs(text_blob: str, file_obj: Any) -> List[str]:\n",
+    "    items: List[str] = []\n",
+    "    if file_obj is not None:\n",
+    "        try:\n",
+    "            path = file_obj.name if hasattr(file_obj, \"name\") else str(file_obj)\n",
+    "            df = pd.read_csv(path)\n",
+    "            col = df.columns[0]\n",
+    "            items.extend([str(x).strip() for x in df[col].tolist() if str(x).strip()])\n",
+    "        except Exception:\n",
+    "            pass\n",
+    "    if text_blob:\n",
+    "        for ln in str(text_blob).splitlines():\n",
+    "            ln = ln.strip()\n",
+    "            if ln:\n",
+    "                items.append(ln)\n",
+    "    seen=set()\n",
+    "    out=[]\n",
+    "    for x in items:\n",
+    "        k=norm_text(x)\n",
+    "        if k and k not in seen:\n",
+    "            seen.add(k); out.append(x)\n",
+    "    return out\n",
+    "\n",
+    "def run_batch(text_blob: str, file_obj: Any, include_antennas: bool):\n",
+    "    inputs = parse_batch_inputs(text_blob, file_obj)\n",
+    "    if not inputs:\n",
+    "        return \"\", None, None, \"\"\n",
+    "\n",
+    "    rows=[]\n",
+    "    for item in inputs:\n",
+    "        res = resolve_device(item)\n",
+    "        if res.get(\"mode\") != \"ok\":\n",
+    "            rows.append({\"Input\": item, \"Matched\":\"\", \"Status\":\"Needs review\", \"EOS\":\"\", \"EOL\":\"\", \"4G alternative\":\"\", \"5G replacement\":\"\", \"Notes\":\"Not found/ambiguous\"})\n",
+    "            continue\n",
+    "\n",
+    "        life_row = df_eos.iloc[int(res[\"row_idx\"])]\n",
+    "        eos, eol, status = row_to_dates_and_status(life_row)\n",
+    "        repl = pick_replacements_lifecycle(life_row, status, use_gpt=False)\n",
+    "\n",
+    "        rows.append({\n",
+    "            \"Input\": item,\n",
+    "            \"Matched\": str(life_row.get(\"sku\",\"\")),\n",
+    "            \"Status\": status,\n",
+    "            \"EOS\": eos,\n",
+    "            \"EOL\": eol,\n",
+    "            \"4G alternative\": repl.get(\"repl_4g\",\"\"),\n",
+    "            \"5G replacement\": repl.get(\"repl_5g\",\"\"),\n",
+    "            \"Notes\": \"\",\n",
+    "        })\n",
+    "\n",
+    "    out_df = pd.DataFrame(rows)\n",
+    "    counts = out_df[\"Status\"].value_counts(dropna=False).to_dict()\n",
+    "    top_5g = out_df[\"5G replacement\"].value_counts(dropna=False).head(5).to_dict()\n",
+    "    summary = f\"Rows: {len(out_df)} | \" + \" | \".join([f\"{k}: {v}\" for k,v in counts.items()])\n",
+    "    rollup = \"Top 5G recommendations:\\n\" + \"\\n\".join([f\"- {k}: {v}\" for k,v in top_5g.items() if str(k).strip()])\n",
+    "\n",
+    "    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=\".csv\")\n",
+    "    out_df.to_csv(tmp.name, index=False)\n",
+    "\n",
+    "    return summary, out_df, tmp.name, rollup\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Replacement feature table + manufacturer link (5G device)\n",
+    "# ============================\n",
+    "\n",
+    "FEATURE_COLS = [\"Device\", \"Modem technology\", \"WiFi\", \"Ports\", \"Antennas\", \"Ruggedness\", \"Use case\"]\n",
+    "\n",
+    "# Manufacturer domains used for best-effort link resolution (no non-maker domains).\n",
+    "MAKER_DOMAINS = {\n",
+    "    \"CRADLEPOINT\": [\"cradlepoint.com\", \"ericsson.com\"],\n",
+    "    \"SIERRA\": [\"semtech.com\", \"airlink.com\"],\n",
+    "    \"FEENEY\": [\"inseego.com\"],\n",
+    "    \"DIGI\": [\"digi.com\"],\n",
+    "    \"CISCO_MERAKI\": [\"meraki.cisco.com\", \"cisco.com\"],\n",
+    "    \"CISCO\": [\"cisco.com\"],\n",
+    "    \"TELTONIKA\": [\"teltonika-networks.com\"],\n",
+    "    \"UNKNOWN\": [],\n",
+    "}\n",
+    "\n",
+    "HTTP_HEADERS = {\n",
+    "    \"User-Agent\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 \"\n",
+    "                  \"(KHTML, like Gecko) Chrome/120.0 Safari/537.36\"\n",
+    "}\n",
+    "HTTP_TIMEOUT = 12\n",
+    "\n",
+    "def _best_effort_manufacturer_url(model: str, canon_make: str) -> str:\n",
+    "    \\\"\\\"\\\"Try to find a manufacturer page or datasheet link using simple on-domain searches.\n",
+    "    If we can't confirm a page, return the manufacturer homepage for the maker family.\n",
+    "    \\\"\\\"\\\"\n",
+    "    model = str(model or \"\").strip()\n",
+    "    if not model or model in {\"Not listed\", \"Not applicable\"}:\n",
+    "        return \"\"\n",
+    "\n",
+    "    domains = MAKER_DOMAINS.get(canon_make, []) or []\n",
+    "    if not domains:\n",
+    "        return \"\"\n",
+    "\n",
+    "    # Candidate on-domain search URLs (common patterns across sites).\n",
+    "    # We keep these on the manufacturer domain (no Google/Bing).\n",
+    "    q = re.sub(r\"\\s+\", \"+\", model)\n",
+    "    url_candidates = []\n",
+    "    for d in domains:\n",
+    "        url_candidates += [\n",
+    "            f\"https://{d}/search?q={q}\",\n",
+    "            f\"https://{d}/search?query={q}\",\n",
+    "            f\"https://{d}/?s={q}\",\n",
+    "            f\"https://www.{d}/search?q={q}\",\n",
+    "            f\"https://www.{d}/search?query={q}\",\n",
+    "            f\"https://www.{d}/?s={q}\",\n",
+    "        ]\n",
+    "\n",
+    "    # Also try a few direct product patterns for known makers (best effort).\n",
+    "    if canon_make == \"TELTONIKA\":\n",
+    "        slug = model.lower()\n",
+    "        url_candidates += [\n",
+    "            f\"https://teltonika-networks.com/products/routers/{slug}\",\n",
+    "            f\"https://teltonika-networks.com/product/{slug}\",\n",
+    "            \"https://teltonika-networks.com/products/routers/\",\n",
+    "        ]\n",
+    "    if canon_make == \"DIGI\":\n",
+    "        url_candidates += [\n",
+    "            \"https://www.digi.com/products/networking/cellular-routers\",\n",
+    "            f\"https://www.digi.com/search?q={q}\",\n",
+    "        ]\n",
+    "    if canon_make == \"CRADLEPOINT\":\n",
+    "        url_candidates += [\n",
+    "            \"https://cradlepoint.com/products/\",\n",
+    "            f\"https://cradlepoint.com/?s={q}\",\n",
+    "        ]\n",
+    "    if canon_make in {\"CISCO\", \"CISCO_MERAKI\"}:\n",
+    "        url_candidates += [\n",
+    "            f\"https://www.cisco.com/c/en/us/search.html?q={q}\",\n",
+    "        ]\n",
+    "\n",
+    "    # Try to confirm a working page (HTTP 200 and model string somewhere in HTML).\n",
+    "    for u in url_candidates[:18]:\n",
+    "        try:\n",
+    "            import requests\n",
+    "            r = requests.get(u, headers=HTTP_HEADERS, timeout=HTTP_TIMEOUT, allow_redirects=True)\n",
+    "            if r.status_code != 200:\n",
+    "                continue\n",
+    "            html = (r.text or \"\").lower()\n",
+    "            if model.lower() in html or \"datasheet\" in html or \"data sheet\" in html:\n",
+    "                return r.url\n",
+    "        except Exception:\n",
+    "            continue\n",
+    "\n",
+    "    # Fallback: maker homepage\n",
+    "    d0 = domains[0]\n",
+    "    return f\"https://{d0}\"\n",
+    "\n",
+    "def _features_from_dec(model: str, canon_make: str) -> Dict[str, str]:\n",
+    "    \\\"\\\"\\\"Lookup a router model in dec2025routers.csv and return the key feature fields.\\\"\\\"\\\"\n",
+    "    if not model or model in {\"Not listed\", \"Not applicable\"}:\n",
+    "        return {k: \"Not listed\" for k in FEATURE_COLS[1:]}\n",
+    "\n",
+    "    pool = df_dec[df_dec[\"_canon_make\"] == canon_make].copy()\n",
+    "    if pool.empty:\n",
+    "        return {k: \"Not listed\" for k in FEATURE_COLS[1:]}\n",
+    "\n",
+    "    hit = process.extractOne(norm_text(model), pool[\"_norm_model\"].tolist(), scorer=fuzz.WRatio)\n",
+    "    if not hit or hit[1] < MATCH_OK:\n",
+    "        return {k: \"Not listed\" for k in FEATURE_COLS[1:]}\n",
+    "\n",
+    "    r = pool.iloc[int(hit[2])]\n",
+    "    ports = f\"WAN: {r.get('WAN ports and speed','')} | LAN: {r.get('LAN ports and speed','')}\"\n",
+    "    return {\n",
+    "        \"Modem technology\": str(r.get(\"Modem Type\",\"\")) or \"Not listed\",\n",
+    "        \"WiFi\": str(r.get(\"WiFi type\",\"\")) or \"Not listed\",\n",
+    "        \"Ports\": ports.strip() if ports.strip() else \"Not listed\",\n",
+    "        \"Antennas\": str(r.get(\"Antennas (internal/external/both)\",\"\")) or \"Not listed\",\n",
+    "        \"Ruggedness\": str(r.get(\"Ruggedization\",\"\")) or \"Not listed\",\n",
+    "        \"Use case\": str(r.get(\"Primary use case\",\"\")) or \"Not listed\",\n",
+    "    }\n",
+    "\n",
+    "def _gpt_fill_feature_row(device_label: str, model: str, canon_make: str, row: Dict[str, str]) -> Dict[str, str]:\n",
+    "    \\\"\\\"\\\"If dec can't supply values, ask GPT to fill missing ones (best guess).\\\"\\\"\\\"\n",
+    "    if client is None:\n",
+    "        return row\n",
+    "\n",
+    "    missing = [k for k,v in row.items() if (not v) or str(v).strip().lower() in {\"not listed\",\"nan\",\"\"}]\n",
+    "    if not missing:\n",
+    "        return row\n",
+    "\n",
+    "    sys = \"Fill missing router feature fields for a Verizon rep. Return strict JSON only.\"\n",
+    "    payload = {\n",
+    "        \"device_label\": device_label,\n",
+    "        \"model\": model,\n",
+    "        \"maker_family\": canon_make,\n",
+    "        \"known\": row,\n",
+    "        \"fill_only\": missing,\n",
+    "        \"rules\": [\n",
+    "            \"Fill only the requested fields.\",\n",
+    "            \"Best guess if needed. Short phrases only.\",\n",
+    "            \"Return JSON only.\"\n",
+    "        ],\n",
+    "        \"output_schema\": {k: \"string\" for k in missing}\n",
+    "    }\n",
+    "    out = gpt_json(sys, payload, max_tokens=260) or {}\n",
+    "    for k in missing:\n",
+    "        val = str(out.get(k, \"\") or \"\").strip()\n",
+    "        if val:\n",
+    "            row[k] = val\n",
+    "    return row\n",
+    "\n",
+    "def build_replacement_features_table(repl_4g: str, repl_5g: str, canon_make: str) -> pd.DataFrame:\n",
+    "    rows = []\n",
+    "\n",
+    "    # 4G\n",
+    "    row4 = _features_from_dec(repl_4g, canon_make)\n",
+    "    row4 = _gpt_fill_feature_row(\"4G alternative\", repl_4g, canon_make, row4)\n",
+    "    rows.append({\"Device\": \"4G alternative\", **row4})\n",
+    "\n",
+    "    # 5G\n",
+    "    row5 = _features_from_dec(repl_5g, canon_make)\n",
+    "    row5 = _gpt_fill_feature_row(\"5G replacement\", repl_5g, canon_make, row5)\n",
+    "    rows.append({\"Device\": \"5G replacement\", **row5})\n",
+    "\n",
+    "    df = pd.DataFrame(rows, columns=FEATURE_COLS)\n",
+    "    return df\n",
+    "\n",
+    "# ============================\n",
+    "# Output\n",
+    "# ============================\n",
+    "def assemble_output(life_row: pd.Series, status: str, eos: str, eol: str, repl: Dict[str,Any], ant: Dict[str,Any]) -> str:\n",
+    "    current_name = f\"{life_row.get('sku','')} — {life_row.get('description','')}\".strip(\" —\")\n",
+    "    st = ant.get(\"stationary_omni\", {})\n",
+    "    vh = ant.get(\"vehicle_omni\", {})\n",
+    "\n",
+    "    lines = []\n",
+    "    lines.append(f\"1. Current device: **{current_name}**\")\n",
+    "    lines.append(f\"2. Status: **{status}**\")\n",
+    "    lines.append(f\"3. End of Sale date: **{eos}**\")\n",
+    "    lines.append(f\"4. End of Life date: **{eol}**\")\n",
+    "    lines.append(f\"5. 4G alternative (lifecycle): **{repl.get('repl_4g','Not applicable')}**\")\n",
+    "    lines.append(f\"6. 5G replacement (lifecycle): **{repl.get('repl_5g','Not listed')}**\")\n",
+    "    lines.append(\"7. Antenna options (Parsec-only):\")\n",
+    "    conn_s = f\" | Conn: {st.get('connectors','')}\" if st.get(\"connectors\") else \"\"\n",
+    "    conn_v = f\" | Conn: {vh.get('connectors','')}\" if vh.get(\"connectors\") else \"\"\n",
+    "    lines.append(f\"   - Stationary (Omni): **{st.get('name','')}** (Part #: {st.get('part_number','')}) — {st.get('description','')} — MIMO: {st.get('mimo','')}{conn_s}\")\n",
+    "    lines.append(f\"   - Vehicle (Omni): **{vh.get('name','')}** (Part #: {vh.get('part_number','')}) — {vh.get('description','')} — MIMO: {vh.get('mimo','')}{conn_v}\")\n",
+    "\n",
+    "    lines.append(\"\\nSources (debug):\")\n",
+    "    for s in repl.get(\"sources\", []) if isinstance(repl.get(\"sources\"), list) else []:\n",
+    "        lines.append(f\"- {s}\")\n",
+    "    lines.append(\"- ParsecCatalog.pdf (local RAG)\")\n",
+    "    lines.append(\"- routers_eos_eol_by_sku.csv (replacements)\")\n",
+    "    return \"\\n\".join(lines)\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Gradio callbacks\n",
+    "# IMPORTANT: no dict state and ALL events have api_name=False (prevents api_info schema generation)\n",
+    "# ============================\n",
+    "def run_lookup(user_text: str, st_json: str):\n",
+    "    user_text = str(user_text or \"\").strip()\n",
+    "    if not user_text:\n",
+    "        return \"Enter a router SKU/model.\", \"\", None, gr.update(visible=False), gr.update(visible=False), \"{}\", \"\"\n",
+    "\n",
+    "    res = resolve_device(user_text)\n",
+    "\n",
+    "    if res.get(\"mode\") == \"pick\":\n",
+    "        opts = res.get(\"options\", [])\n",
+    "        choices = [o[\"label\"] for o in opts]\n",
+    "        st2 = {\"mode\":\"pick\",\"options\": opts, \"raw\": user_text}\n",
+    "        return \"Did you mean A or B? Pick one, then click Use selection.\", \"\", None, gr.update(choices=choices, value=None, visible=True), gr.update(visible=True), state_dump(st2), \"\"\n",
+    "\n",
+    "    if res.get(\"mode\") != \"ok\":\n",
+    "        return \"Not found.\", \"\", None, gr.update(visible=False), gr.update(visible=False), \"{}\", \"\"\n",
+    "\n",
+    "    life_row = df_eos.iloc[int(res[\"row_idx\"])]\n",
+    "    eos, eol, status = row_to_dates_and_status(life_row)\n",
+    "\n",
+    "    repl = pick_replacements_lifecycle(life_row, status, use_gpt=True)\n",
+    "    canon_make = str(life_row.get(\"_canon_make\",\"UNKNOWN\"))\n",
+    "    mimo = infer_mimo_for_5g(repl.get(\"repl_5g\",\"\"))\n",
+    "    tech = \"5G\" if repl.get(\"repl_5g\") and repl.get(\"repl_5g\") != \"Not listed\" else (\"4G\" if device_is_4g(life_row) else \"Unknown\")\n",
+    "    ant = antenna_options_for(repl.get(\"repl_5g\") or str(life_row.get(\"sku\",\"\")), tech, mimo)\n",
+    "\n",
+    "    output = assemble_output(life_row, status, eos, eol, repl, ant)\n",
+    "    st_out = {\"row_idx\": int(res[\"row_idx\"]), \"repl\": repl, \"ant\": ant, \"raw\": user_text}\n",
+    "    url5 = _best_effort_manufacturer_url(repl.get('repl_5g',''), canon_make)\n",
+    "    link = f\"**5G manufacturer page (best effort):** {url5}\" if url5 else \"\"\n",
+    "    feat_df = build_replacement_features_table(repl.get('repl_4g',''), repl.get('repl_5g',''), canon_make)\n",
+    "    return output, link, feat_df, gr.update(visible=False), gr.update(visible=False), state_dump(st_out), \"\"\n",
+    "\n",
+    "def use_selection(selected_label: str, st_json: str):\n",
+    "    st = state_load(st_json)\n",
+    "    if not st or st.get(\"mode\") != \"pick\":\n",
+    "        return \"Run a search first.\", \"\", None, gr.update(visible=False), gr.update(visible=False), \"{}\", \"\"\n",
+    "\n",
+    "    if not selected_label:\n",
+    "        return \"Pick A or B first.\", \"\", None, gr.update(visible=True), gr.update(visible=True), st_json, \"\"\n",
+    "\n",
+    "    chosen_row = None\n",
+    "    for o in st.get(\"options\", []):\n",
+    "        if o.get(\"label\") == selected_label:\n",
+    "            chosen_row = int(o[\"row_idx\"])\n",
+    "            break\n",
+    "    if chosen_row is None:\n",
+    "        return \"Pick a valid option.\", \"\", None, gr.update(visible=True), gr.update(visible=True), st_json, \"\"\n",
+    "\n",
+    "    life_row = df_eos.iloc[int(chosen_row)]\n",
+    "    eos, eol, status = row_to_dates_and_status(life_row)\n",
+    "\n",
+    "    repl = pick_replacements_lifecycle(life_row, status, use_gpt=True)\n",
+    "    canon_make = str(life_row.get(\"_canon_make\",\"UNKNOWN\"))\n",
+    "    mimo = infer_mimo_for_5g(repl.get(\"repl_5g\",\"\"))\n",
+    "    tech = \"5G\" if repl.get(\"repl_5g\") and repl.get(\"repl_5g\") != \"Not listed\" else (\"4G\" if device_is_4g(life_row) else \"Unknown\")\n",
+    "    ant = antenna_options_for(repl.get(\"repl_5g\") or str(life_row.get(\"sku\",\"\")), tech, mimo)\n",
+    "\n",
+    "    output = assemble_output(life_row, status, eos, eol, repl, ant)\n",
+    "    st_out = {\"row_idx\": int(chosen_row), \"repl\": repl, \"ant\": ant, \"raw\": st.get(\"raw\",\"\")}\n",
+    "    url5 = _best_effort_manufacturer_url(repl.get('repl_5g',''), canon_make)\n",
+    "    link = f\"**5G manufacturer page (best effort):** {url5}\" if url5 else \"\"\n",
+    "    feat_df = build_replacement_features_table(repl.get('repl_4g',''), repl.get('repl_5g',''), canon_make)\n",
+    "    return output, link, feat_df, gr.update(visible=False), gr.update(visible=False), state_dump(st_out), \"\"\n",
+    "\n",
+    "def make_install_ready(st_json: str):\n",
+    "    st = state_load(st_json)\n",
+    "    if not st or \"row_idx\" not in st:\n",
+    "        return \"Run a lookup first.\"\n",
+    "    life_row = df_eos.iloc[int(st[\"row_idx\"])]\n",
+    "    current_sku = str(life_row.get(\"sku\",\"\") or \"\")\n",
+    "    return install_ready_checklist(current_sku, st.get(\"repl\", {}) or {}, st.get(\"ant\", {}) or {})\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# UI\n",
+    "# ============================\n",
+    "with gr.Blocks(title=\"Only-Routers\") as demo:\n",
+    "    gr.Markdown(\"## Only-Routers\\nSingle lookup + Batch upload for Verizon reps.\")\n",
+    "\n",
+    "    with gr.Tabs():\n",
+    "        with gr.Tab(\"Single\"):\n",
+    "            user_text = gr.Textbox(label=\"Router SKU or model\", placeholder=\"Examples: IBR650B, AER1600, ES450, WR21, RUT240\", lines=1)\n",
+    "            st = gr.State(\"{}\")  # JSON string\n",
+    "\n",
+    "            check_btn = gr.Button(\"Check\", variant=\"primary\")\n",
+    "            pick_dd = gr.Dropdown(label=\"Pick A or B\", choices=[], visible=False)\n",
+    "            use_btn = gr.Button(\"Use selection\", visible=False)\n",
+    "\n",
+    "            output_md = gr.Markdown()\n",
+    "\n",
+    "            link_md = gr.Markdown()\n",
+    "            features_df = gr.Dataframe(headers=FEATURE_COLS, interactive=False, wrap=True)\n",
+    "\n",
+    "\n",
+    "            install_btn = gr.Button(\"Make install-ready checklist\")\n",
+    "            install_md = gr.Markdown()\n",
+    "\n",
+    "            check_btn.click(fn=run_lookup, inputs=[user_text, st], outputs=[output_md, link_md, features_df, pick_dd, use_btn, st, install_md], api_name=False)\n",
+    "            use_btn.click(fn=use_selection, inputs=[pick_dd, st], outputs=[output_md, link_md, features_df, pick_dd, use_btn, st, install_md], api_name=False)\n",
+    "            install_btn.click(fn=make_install_ready, inputs=[st], outputs=[install_md], api_name=False)\n",
+    "\n",
+    "        with gr.Tab(\"Batch\"):\n",
+    "            gr.Markdown(\"Paste one per line or upload a CSV (first column). Batch runs fast (no GPT).\")\n",
+    "            batch_text = gr.Textbox(label=\"Paste devices (one per line)\", lines=8, placeholder=\"WR21\\nRUT240\\nIBR650B\")\n",
+    "            batch_file = gr.File(label=\"Upload CSV\", file_types=[\".csv\"])\n",
+    "            include_ant = gr.Checkbox(label=\"Include antenna picks (slower)\", value=False)\n",
+    "            run_btn = gr.Button(\"Run batch\", variant=\"primary\")\n",
+    "\n",
+    "            summary_md = gr.Markdown()\n",
+    "            rollup_md = gr.Markdown()\n",
+    "            table = gr.Dataframe(interactive=False, wrap=True)\n",
+    "            dl = gr.File(label=\"Download results CSV\")\n",
+    "\n",
+    "            run_btn.click(fn=run_batch, inputs=[batch_text, batch_file, include_ant], outputs=[summary_md, table, dl, rollup_md], api_name=False)\n",
+    "\n",
+    "# IMPORTANT: On Spaces, demo.launch() is correct; do NOT use share=True.\n",
+    "demo.launch(show_api=False)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Updates/only-routers_ai_poc_hf_fixed_v8.ipynb

@@ -0,0 +1,1288 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d439c9b3",
+   "metadata": {},
+   "source": [
+    "# Only-Routers (HF fixed v8)\n",
+    "\n",
+    "Adds post-recommendation Q&A box powered by GPT.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c68bc169",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import re\n",
+    "import json\n",
+    "import math\n",
+    "import hashlib\n",
+    "import tempfile\n",
+    "from dataclasses import dataclass\n",
+    "from datetime import datetime, date\n",
+    "from typing import Any, Dict, List, Optional, Tuple\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import fitz  # PyMuPDF\n",
+    "import faiss\n",
+    "from sentence_transformers import SentenceTransformer\n",
+    "from rapidfuzz import fuzz, process\n",
+    "\n",
+    "import gradio as gr\n",
+    "from openai import OpenAI\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Settings\n",
+    "# ============================\n",
+    "TODAY = date(2026, 1, 18)\n",
+    "OPENAI_MODEL = \"gpt-5.2\"\n",
+    "OPENAI_REASONING = {\"effort\": \"high\"}\n",
+    "MATCH_OK = 80\n",
+    "\n",
+    "EMBED_MODEL_NAME = \"sentence-transformers/all-MiniLM-L6-v2\"\n",
+    "PARSEC_CONTEXT_BEFORE = 900\n",
+    "PARSEC_CONTEXT_AFTER = 1600\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# OpenAI client (HF Space secret: OPENAI_API_KEY)\n",
+    "# ============================\n",
+    "API_KEY = os.getenv(\"OPENAI_API_KEY\", \"\").strip()\n",
+    "client = OpenAI(api_key=API_KEY) if API_KEY else None\n",
+    "\n",
+    "# ----------------------------\n",
+    "# Gradio state helpers\n",
+    "# Keep state as a JSON STRING to avoid schema issues on Hugging Face.\n",
+    "# ----------------------------\n",
+    "def state_load(st_json: str) -> Dict[str, Any]:\n",
+    "    try:\n",
+    "        if not st_json:\n",
+    "            return {}\n",
+    "        return json.loads(st_json) if isinstance(st_json, str) else {}\n",
+    "    except Exception:\n",
+    "        return {}\n",
+    "\n",
+    "def state_dump(st: Dict[str, Any]) -> str:\n",
+    "    try:\n",
+    "        return json.dumps(st or {}, ensure_ascii=False)\n",
+    "    except Exception:\n",
+    "        return \"{}\"\n",
+    "\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Helpers\n",
+    "# ============================\n",
+    "def norm_text(s: Any) -> str:\n",
+    "    try:\n",
+    "        if s is None or (isinstance(s, float) and math.isnan(s)) or pd.isna(s):\n",
+    "            return \"\"\n",
+    "    except Exception:\n",
+    "        pass\n",
+    "    s = str(s).strip().lower()\n",
+    "    s = re.sub(r\"[^a-z0-9\\s\\-\\/]\", \" \", s)\n",
+    "    s = re.sub(r\"\\s+\", \" \", s).strip()\n",
+    "    return s\n",
+    "\n",
+    "def safe_str(v: Any) -> str:\n",
+    "    if v is None or (isinstance(v, float) and pd.isna(v)) or pd.isna(v):\n",
+    "        return \"\"\n",
+    "    return str(v).strip()\n",
+    "\n",
+    "def is_5g(modem_type: Any) -> bool:\n",
+    "    s = norm_text(modem_type)\n",
+    "    return (\"5g\" in s) or (\"nr\" in s)\n",
+    "\n",
+    "def json_load_safe(s: str) -> Dict[str, Any]:\n",
+    "    try:\n",
+    "        return json.loads(s)\n",
+    "    except Exception:\n",
+    "        return {}\n",
+    "\n",
+    "def gpt_json(system: str, payload: Dict[str, Any], max_tokens: int = 600) -> Dict[str, Any]:\n",
+    "    if client is None:\n",
+    "        return {}\n",
+    "    resp = client.responses.create(\n",
+    "        model=OPENAI_MODEL,\n",
+    "        reasoning=OPENAI_REASONING,\n",
+    "        input=[{\"role\":\"system\",\"content\":system},{\"role\":\"user\",\"content\":json.dumps(payload)}],\n",
+    "        max_output_tokens=max_tokens,\n",
+    "    )\n",
+    "    return json_load_safe(getattr(resp, \"output_text\", \"\") or \"\")\n",
+    "\n",
+    "\n",
+    "def gpt_answer_md(system: str, user: str, max_tokens: int = 650) -> str:\n",
+    "    \"\"\"Return a rep-friendly markdown answer.\"\"\"\n",
+    "    if client is None:\n",
+    "        return \"No API key is configured, so I can't answer detailed questions right now.\"\n",
+    "    resp = client.responses.create(\n",
+    "        model=OPENAI_MODEL,\n",
+    "        reasoning=OPENAI_REASONING,\n",
+    "        input=[\n",
+    "            {\"role\": \"system\", \"content\": system},\n",
+    "            {\"role\": \"user\", \"content\": user},\n",
+    "        ],\n",
+    "        max_output_tokens=max_tokens,\n",
+    "    )\n",
+    "    return (getattr(resp, \"output_text\", \"\") or \"\").strip()\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Load data\n",
+    "# ============================\n",
+    "EOS_PATH = \"routers_eos_eol_by_sku.csv\"\n",
+    "DEC_PATH = \"dec2025routers.csv\"\n",
+    "PARSEC_PDF = \"ParsecCatalog.pdf\"\n",
+    "\n",
+    "if not os.path.exists(EOS_PATH):\n",
+    "    raise FileNotFoundError(f\"Missing {EOS_PATH} in repo.\")\n",
+    "if not os.path.exists(DEC_PATH):\n",
+    "    raise FileNotFoundError(f\"Missing {DEC_PATH} in repo.\")\n",
+    "if not os.path.exists(PARSEC_PDF):\n",
+    "    raise FileNotFoundError(f\"Missing {PARSEC_PDF} in repo.\")\n",
+    "\n",
+    "df_eos = pd.read_csv(EOS_PATH).copy()\n",
+    "df_dec = pd.read_csv(DEC_PATH).copy()\n",
+    "\n",
+    "\n",
+    "def _canonize_eos_columns(df: pd.DataFrame) -> pd.DataFrame:\n",
+    "    \"\"\"Normalize lifecycle CSV column names (case-insensitive) and create expected columns.\"\"\"\n",
+    "    # Map various header spellings to canonical names used by the app\n",
+    "    mapping = {}\n",
+    "    for c in df.columns:\n",
+    "        k = str(c).strip().lower().replace(\" \", \"_\")\n",
+    "        if k in {\"sku\", \"model\", \"device\", \"device_sku\"}:\n",
+    "            mapping[c] = \"sku\"\n",
+    "        elif k in {\"manufacturer\", \"make\", \"vendor\"}:\n",
+    "            mapping[c] = \"manufacturer\"\n",
+    "        elif k in {\"device_type\", \"type\"}:\n",
+    "            mapping[c] = \"device_type\"\n",
+    "        elif k in {\"end_of_sale\", \"eos\", \"end_sale\", \"end_of_sales\"}:\n",
+    "            mapping[c] = \"end_of_sale\"\n",
+    "        elif k in {\"end_of_life\", \"eol\", \"end_life\"}:\n",
+    "            mapping[c] = \"end_of_life\"\n",
+    "        elif k in {\"suggested_replacement\", \"replacement_4g\", \"lte_replacement\", \"replacement_lte\", \"replacement\"}:\n",
+    "            mapping[c] = \"suggested_replacement\"\n",
+    "        elif k in {\"advanced_5g_option\", \"replacement_5g\", \"fiveg_replacement\", \"5g_replacement\", \"upgrade_5g\"}:\n",
+    "            mapping[c] = \"advanced_5g_option\"\n",
+    "        elif k in {\"region\", \"market\"}:\n",
+    "            mapping[c] = \"region\"\n",
+    "        elif k in {\"notes\", \"note\"}:\n",
+    "            mapping[c] = \"notes\"\n",
+    "        elif k in {\"description\", \"device_description\", \"name\"}:\n",
+    "            mapping[c] = \"description\"\n",
+    "\n",
+    "    df = df.rename(columns=mapping).copy()\n",
+    "\n",
+    "    # Create expected columns if missing\n",
+    "    if \"sku\" not in df.columns:\n",
+    "        # Try the common capitalized header as a fallback\n",
+    "        if \"SKU\" in df.columns:\n",
+    "            df[\"sku\"] = df[\"SKU\"].astype(str)\n",
+    "        else:\n",
+    "            df[\"sku\"] = \"\"\n",
+    "\n",
+    "    if \"manufacturer\" not in df.columns:\n",
+    "        df[\"manufacturer\"] = \"\"\n",
+    "\n",
+    "    if \"device_type\" not in df.columns:\n",
+    "        df[\"device_type\"] = \"\"\n",
+    "\n",
+    "    if \"description\" not in df.columns:\n",
+    "        # If the simplified file removed description, use SKU as description (still searchable)\n",
+    "        df[\"description\"] = df[\"sku\"].astype(str)\n",
+    "\n",
+    "    if \"notes\" not in df.columns:\n",
+    "        df[\"notes\"] = \"\"\n",
+    "\n",
+    "    if \"region\" not in df.columns:\n",
+    "        df[\"region\"] = \"\"\n",
+    "\n",
+    "    if \"suggested_replacement\" not in df.columns:\n",
+    "        df[\"suggested_replacement\"] = \"\"\n",
+    "\n",
+    "    if \"advanced_5g_option\" not in df.columns:\n",
+    "        df[\"advanced_5g_option\"] = \"\"\n",
+    "\n",
+    "    if \"end_of_sale\" not in df.columns:\n",
+    "        df[\"end_of_sale\"] = \"\"\n",
+    "\n",
+    "    if \"end_of_life\" not in df.columns:\n",
+    "        df[\"end_of_life\"] = \"\"\n",
+    "\n",
+    "    return df\n",
+    "\n",
+    "df_eos = _canonize_eos_columns(df_eos)\n",
+    "\n",
+    "\n",
+    "def region_ok(x: Any) -> bool:\n",
+    "    s = str(x or \"\").strip().lower()\n",
+    "    if not s:\n",
+    "        return True\n",
+    "    if \"not specified\" in s:\n",
+    "        return True\n",
+    "    if \"north america\" in s:\n",
+    "        return True\n",
+    "    if re.search(r\"\\busa\\b\", s):\n",
+    "        return True\n",
+    "    if re.search(r\"\\bunited\\s+states\\b\", s):\n",
+    "        return True\n",
+    "    if re.search(r\"\\bu\\.?s\\.?\\b\", s):\n",
+    "        return True\n",
+    "    return False\n",
+    "\n",
+    "if \"region\" in df_eos.columns:\n",
+    "    df_eos = df_eos[df_eos[\"region\"].apply(region_ok)].reset_index(drop=True)\n",
+    "\n",
+    "# Maker mapping (includes Teltonika)\n",
+    "CANON_MAKER = {\n",
+    "    \"CRADLEPOINT\": {\"cradlepoint\", \"ericsson\", \"ericsson enterprise wireless\"},\n",
+    "    \"SIERRA\": {\"sierra\", \"sierra wireless\", \"semtech\", \"airlink\"},\n",
+    "    \"FEENEY\": {\"feeney\", \"feeney wireless\", \"inseego\"},\n",
+    "    \"DIGI\": {\"digi\", \"accelerated\", \"accelerated concepts\"},\n",
+    "    \"CISCO_MERAKI\": {\"meraki\", \"cisco meraki\"},\n",
+    "    \"CISCO\": {\"cisco\"},\n",
+    "    \"TELTONIKA\": {\"teltonika\"},\n",
+    "}\n",
+    "\n",
+    "def canon_maker_from_text(s: Any) -> str:\n",
+    "    t = norm_text(s)\n",
+    "    for canon, terms in CANON_MAKER.items():\n",
+    "        for term in terms:\n",
+    "            if term in t:\n",
+    "                return canon\n",
+    "    return \"UNKNOWN\"\n",
+    "\n",
+    "df_eos[\"_canon_make\"] = df_eos[\"manufacturer\"].apply(canon_maker_from_text) if \"manufacturer\" in df_eos.columns else \"UNKNOWN\"\n",
+    "df_eos[\"_norm_sku\"] = df_eos[\"sku\"].apply(norm_text) if \"sku\" in df_eos.columns else \"\"\n",
+    "df_eos[\"_norm_desc\"] = df_eos[\"description\"].apply(norm_text) if \"description\" in df_eos.columns else \"\"\n",
+    "df_eos[\"_norm_notes\"] = df_eos[\"notes\"].apply(norm_text) if \"notes\" in df_eos.columns else \"\"\n",
+    "\n",
+    "df_dec[\"_canon_make\"] = df_dec[\"Make\"].apply(canon_maker_from_text) if \"Make\" in df_dec.columns else \"UNKNOWN\"\n",
+    "df_dec[\"_norm_model\"] = df_dec[\"Model\"].apply(norm_text) if \"Model\" in df_dec.columns else \"\"\n",
+    "df_dec[\"_is5g\"] = df_dec[\"Modem Type\"].apply(is_5g) if \"Modem Type\" in df_dec.columns else False\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Date helpers\n",
+    "# ============================\n",
+    "@dataclass\n",
+    "class ParsedDate:\n",
+    "    raw: str\n",
+    "    kind: str\n",
+    "    value: Optional[date]\n",
+    "\n",
+    "def parse_date_field(x: Any) -> ParsedDate:\n",
+    "    raw = str(x or \"\").strip()\n",
+    "    if not raw:\n",
+    "        return ParsedDate(raw=\"\", kind=\"missing\", value=None)\n",
+    "\n",
+    "    # Common US formats: M/D/YY or M/D/YYYY (e.g., 6/24/24, 9/30/21)\n",
+    "    for fmt in (\"%m/%d/%y\", \"%m/%d/%Y\", \"%-m/%-d/%y\", \"%-m/%-d/%Y\"):\n",
+    "        try:\n",
+    "            dt = datetime.strptime(raw, fmt).date()\n",
+    "            return ParsedDate(raw=raw, kind=\"full\", value=dt)\n",
+    "        except Exception:\n",
+    "            pass\n",
+    "\n",
+    "    # ISO-ish: YYYY\n",
+    "    if re.fullmatch(r\"\\d{4}\", raw):\n",
+    "        y = int(raw)\n",
+    "        if y == TODAY.year:\n",
+    "            return ParsedDate(raw=raw, kind=\"year\", value=date(y, 1, 1))\n",
+    "        if y < TODAY.year:\n",
+    "            return ParsedDate(raw=raw, kind=\"year\", value=date(y, 1, 1))\n",
+    "        return ParsedDate(raw=raw, kind=\"year\", value=date(y, 12, 31))\n",
+    "\n",
+    "    # YYYY-MM\n",
+    "    if re.fullmatch(r\"\\d{4}-\\d{2}\", raw):\n",
+    "        try:\n",
+    "            y, m = raw.split(\"-\")\n",
+    "            return ParsedDate(raw=raw, kind=\"year_month\", value=date(int(y), int(m), 1))\n",
+    "        except Exception:\n",
+    "            return ParsedDate(raw=raw, kind=\"bad\", value=None)\n",
+    "\n",
+    "    # YYYY-MM-DD\n",
+    "    if re.fullmatch(r\"\\d{4}-\\d{2}-\\d{2}\", raw):\n",
+    "        try:\n",
+    "            dt = datetime.strptime(raw, \"%Y-%m-%d\").date()\n",
+    "            return ParsedDate(raw=raw, kind=\"full\", value=dt)\n",
+    "        except Exception:\n",
+    "            return ParsedDate(raw=raw, kind=\"bad\", value=None)\n",
+    "\n",
+    "    # Last resort: leave as raw (unparsed)\n",
+    "    return ParsedDate(raw=raw, kind=\"bad\", value=None)\n",
+    "\n",
+    "    if re.fullmatch(r\"\\d{4}-\\d{2}-\\d{2}\", raw):\n",
+    "        try:\n",
+    "            dt = datetime.strptime(raw, \"%Y-%m-%d\").date()\n",
+    "            return ParsedDate(raw=raw, kind=\"full\", value=dt)\n",
+    "        except Exception:\n",
+    "            return ParsedDate(raw=raw, kind=\"bad\", value=None)\n",
+    "\n",
+    "    return ParsedDate(raw=raw, kind=\"bad\", value=None)\n",
+    "\n",
+    "def display_date(pd_: ParsedDate) -> str:\n",
+    "    if pd_.kind == \"missing\":\n",
+    "        return \"Not listed\"\n",
+    "    if pd_.kind == \"bad\":\n",
+    "        return pd_.raw or \"Not listed\"\n",
+    "    return pd_.raw\n",
+    "\n",
+    "def status_from_eos_eol(eos: ParsedDate, eol: ParsedDate) -> str:\n",
+    "    if eos.value is None and eol.value is None:\n",
+    "        return \"Unknown\"\n",
+    "    if eol.value is not None and eol.value <= TODAY:\n",
+    "        return \"End of Life\"\n",
+    "    if eos.value is not None and eos.value <= TODAY:\n",
+    "        return \"End of Sale\"\n",
+    "    return \"Active\"\n",
+    "\n",
+    "def row_to_dates_and_status(row: pd.Series) -> Tuple[str, str, str]:\n",
+    "    eos = parse_date_field(row.get(\"end_of_sale\"))\n",
+    "    eol = parse_date_field(row.get(\"end_of_life\"))\n",
+    "    return display_date(eos), display_date(eol), status_from_eos_eol(eos, eol)\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Embeddings + Parsec index\n",
+    "# ============================\n",
+    "embedder = SentenceTransformer(EMBED_MODEL_NAME)\n",
+    "\n",
+    "def extract_pdf_text_pages(path: str) -> List[str]:\n",
+    "    doc = fitz.open(path)\n",
+    "    return [doc[i].get_text(\"text\") for i in range(len(doc))]\n",
+    "\n",
+    "def build_parsec_cards(pages: List[str]) -> List[str]:\n",
+    "    cards = []\n",
+    "    for p in pages:\n",
+    "        for m in re.finditer(r\"Standard\\s+SKU:\", p):\n",
+    "            start = max(0, m.start() - PARSEC_CONTEXT_BEFORE)\n",
+    "            end = min(len(p), m.start() + PARSEC_CONTEXT_AFTER)\n",
+    "            c = p[start:end].strip()\n",
+    "            if len(c) >= 200:\n",
+    "                cards.append(c)\n",
+    "    out, seen = [], set()\n",
+    "    for c in cards:\n",
+    "        h = hashlib.sha1(c.encode(\"utf-8\")).hexdigest()\n",
+    "        if h not in seen:\n",
+    "            seen.add(h); out.append(c)\n",
+    "    return out\n",
+    "\n",
+    "parsec_cards = build_parsec_cards(extract_pdf_text_pages(PARSEC_PDF))\n",
+    "parsec_emb = embedder.encode(parsec_cards, batch_size=64, show_progress_bar=False, normalize_embeddings=True)\n",
+    "parsec_emb = np.asarray(parsec_emb, dtype=np.float32)\n",
+    "parsec_index = faiss.IndexFlatIP(parsec_emb.shape[1])\n",
+    "parsec_index.add(parsec_emb)\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Device resolution\n",
+    "# ============================\n",
+    "def label_for_row(i: int) -> str:\n",
+    "    r = df_eos.iloc[i]\n",
+    "    return f\"{r.get('sku','')} — {r.get('manufacturer','')} — {r.get('description','')}\"[:220]\n",
+    "\n",
+    "EOS_LABELS = [label_for_row(i) for i in range(len(df_eos))]\n",
+    "EOS_CORPUS = []\n",
+    "for _, r in df_eos.iterrows():\n",
+    "    EOS_CORPUS.append(\" \".join([r.get(\"_norm_sku\",\"\"), r.get(\"_canon_make\",\"\"), r.get(\"_norm_desc\",\"\"), r.get(\"_norm_notes\",\"\")]))\n",
+    "\n",
+    "def local_candidates(query: str, top_k: int = 6) -> List[Tuple[int, int, str]]:\n",
+    "    q = norm_text(query)\n",
+    "    hits = process.extract(q, EOS_CORPUS, scorer=fuzz.WRatio, limit=top_k)\n",
+    "    return [(int(idx), int(score), EOS_LABELS[int(idx)]) for _, score, idx in hits]\n",
+    "\n",
+    "def gpt_choose_device(user_text: str, candidates: List[Tuple[int,int,str]]) -> Dict[str, Any]:\n",
+    "    if client is None:\n",
+    "        return {}\n",
+    "    sys = \"Pick which router the user meant. Never invent. Return strict JSON only.\"\n",
+    "    payload = {\n",
+    "        \"user_input\": user_text,\n",
+    "        \"candidates\": [{\"row_idx\": i, \"score\": s, \"label\": lbl} for (i,s,lbl) in candidates],\n",
+    "        \"rules\": [\n",
+    "            \"If one is clearly correct, return mode='ok' with row_idx.\",\n",
+    "            \"If two are plausible, return mode='pick' with top 2 options.\"\n",
+    "        ],\n",
+    "        \"output_schema\": {\"mode\":\"ok|pick\",\"row_idx\":\"int\",\"options\":[{\"row_idx\":\"int\",\"label\":\"string\"}]}\n",
+    "    }\n",
+    "    return gpt_json(sys, payload, max_tokens=280)\n",
+    "\n",
+    "def resolve_device(user_text: str) -> Dict[str, Any]:\n",
+    "    q = norm_text(user_text)\n",
+    "    exact = df_eos.index[df_eos[\"_norm_sku\"] == q].tolist()\n",
+    "    if len(exact) == 1:\n",
+    "        return {\"mode\":\"ok\",\"row_idx\": int(exact[0])}\n",
+    "    if len(exact) > 1:\n",
+    "        opts = [{\"row_idx\": int(i), \"label\": EOS_LABELS[int(i)]} for i in exact[:2]]\n",
+    "        return {\"mode\":\"pick\",\"options\": opts}\n",
+    "\n",
+    "    cands = local_candidates(user_text, top_k=6)\n",
+    "    if not cands:\n",
+    "        return {\"mode\":\"not_found\"}\n",
+    "\n",
+    "    if cands[0][1] >= 95 and (len(cands) == 1 or (cands[0][1] - cands[1][1]) >= 8):\n",
+    "        return {\"mode\":\"ok\",\"row_idx\": cands[0][0]}\n",
+    "\n",
+    "    g = gpt_choose_device(user_text, cands)\n",
+    "    if g.get(\"mode\") == \"ok\" and isinstance(g.get(\"row_idx\"), int):\n",
+    "        return {\"mode\":\"ok\",\"row_idx\": int(g[\"row_idx\"])}\n",
+    "\n",
+    "    if g.get(\"mode\") == \"pick\":\n",
+    "        opts = g.get(\"options\", []) or []\n",
+    "        opts2 = [{\"row_idx\": int(o[\"row_idx\"]), \"label\": str(o[\"label\"])} for o in opts[:2] if \"row_idx\" in o]\n",
+    "        if opts2:\n",
+    "            return {\"mode\":\"pick\",\"options\": opts2}\n",
+    "\n",
+    "    if len(cands) > 1:\n",
+    "        return {\"mode\":\"pick\",\"options\":[{\"row_idx\":cands[0][0],\"label\":cands[0][2]},{\"row_idx\":cands[1][0],\"label\":cands[1][2]}]}\n",
+    "    return {\"mode\":\"pick\",\"options\":[{\"row_idx\":cands[0][0],\"label\":cands[0][2]}]}\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Replacements — lifecycle CSV source of truth\n",
+    "# ============================\n",
+    "def extract_model_token(text: str) -> str:\n",
+    "    s = safe_str(text)\n",
+    "    if not s:\n",
+    "        return \"\"\n",
+    "    parts = [p.strip() for p in s.split(\"|\") if p.strip()]\n",
+    "    candidates = parts[::-1] if parts else [s]\n",
+    "    for cand in candidates:\n",
+    "        m = re.search(r\"\\bRUT[A-Z]?\\d{2,4}\\b\", cand.upper())\n",
+    "        if m:\n",
+    "            return m.group(0).upper()\n",
+    "        m = re.search(r\"\\bIX\\d{2}\\b\", cand, flags=re.IGNORECASE)\n",
+    "        if m:\n",
+    "            return m.group(0).upper()\n",
+    "        m = re.search(r\"\\b(R\\d{3,4}|E\\d{3,4}|S\\d{3,4})\\b\", cand, flags=re.IGNORECASE)\n",
+    "        if m:\n",
+    "            return m.group(0).upper()\n",
+    "        m = re.search(r\"\\b[A-Z]{1,6}\\d{2,4}[A-Z]?\\b\", cand.upper())\n",
+    "        if m:\n",
+    "            return m.group(0).upper()\n",
+    "    return candidates[0][:60]\n",
+    "\n",
+    "def device_is_4g(row: pd.Series) -> bool:\n",
+    "    # Detect LTE/4G even when the description uses \"Cat 4 / Cat6 / Cat 12\" without saying \"LTE\"\n",
+    "    t = norm_text(row.get(\"description\",\"\")) + \" \" + norm_text(row.get(\"notes\",\"\")) + \" \" + norm_text(row.get(\"sku\",\"\"))\n",
+    "\n",
+    "    # If it explicitly says 5G/NR, treat as not 4G-only\n",
+    "    if (\"5g\" in t) or (\"nr\" in t):\n",
+    "        return False\n",
+    "\n",
+    "    # Classic signals\n",
+    "    if (\"lte\" in t) or (\"4g\" in t):\n",
+    "        return True\n",
+    "\n",
+    "    # LTE category signals (Cat 1..20 are LTE categories; Cat M1/M2 are LTE-M)\n",
+    "    if re.search(r\"\\bcat\\s*[-]?\\s*(m1|m2)\\b\", t):\n",
+    "        return True\n",
+    "\n",
+    "    m = re.search(r\"\\bcat\\s*[-]?\\s*(\\d{1,2})\\b\", t)\n",
+    "    if m:\n",
+    "        try:\n",
+    "            cat = int(m.group(1))\n",
+    "            if 0 < cat <= 20:\n",
+    "                return True\n",
+    "        except Exception:\n",
+    "            pass\n",
+    "\n",
+    "    # If \"cat\" appears at all, it's almost always LTE-family\n",
+    "    if \"cat\" in t:\n",
+    "        return True\n",
+    "\n",
+    "    return False\n",
+    "\n",
+    "    # If it explicitly says 5G/NR, treat as not 4G-only\n",
+    "    if (\"5g\" in t) or (\"nr\" in t):\n",
+    "        return False\n",
+    "\n",
+    "    # Classic signals\n",
+    "    if (\"lte\" in t) or (\"4g\" in t):\n",
+    "        return True\n",
+    "\n",
+    "    # LTE category signals (Cat 1..20 are LTE categories; Cat M1/M2 are LTE-M)\n",
+    "    if re.search(r\"\\bcat\\s*[-]?\\s*(m1|m2)\\b\", t):\n",
+    "        return True\n",
+    "\n",
+    "    m = re.search(r\"\\bcat\\s*[-]?\\s*(\\d{1,2})\\b\", t)\n",
+    "    if m:\n",
+    "        try:\n",
+    "            cat = int(m.group(1))\n",
+    "            if 0 < cat <= 20:\n",
+    "                return True\n",
+    "        except Exception:\n",
+    "            pass\n",
+    "\n",
+    "    # If \"cat\" appears at all, it's almost always LTE-family\n",
+    "    if \"cat\" in t:\n",
+    "        return True\n",
+    "\n",
+    "    return False\n",
+    "\n",
+    "\n",
+    "def candidate_5g_models_from_lifecycle(manufacturer: str) -> List[str]:\n",
+    "    mfr = norm_text(manufacturer)\n",
+    "    pool = df_eos[df_eos[\"manufacturer\"].astype(str).str.lower().eq(mfr)].copy() if \"manufacturer\" in df_eos.columns else df_eos.copy()\n",
+    "    vals = pool[\"advanced_5g_option\"].tolist() if \"advanced_5g_option\" in pool.columns else []\n",
+    "    out, seen = [], set()\n",
+    "    for v in vals:\n",
+    "        tok = extract_model_token(v)\n",
+    "        if tok and tok.lower() != \"nan\" and tok not in seen:\n",
+    "            seen.add(tok); out.append(tok)\n",
+    "    return out\n",
+    "\n",
+    "def candidate_4g_models_from_lifecycle(manufacturer: str) -> List[str]:\n",
+    "    mfr = norm_text(manufacturer)\n",
+    "    pool = df_eos[df_eos[\"manufacturer\"].astype(str).str.lower().eq(mfr)].copy() if \"manufacturer\" in df_eos.columns else df_eos.copy()\n",
+    "    vals = pool[\"suggested_replacement\"].tolist() if \"suggested_replacement\" in pool.columns else []\n",
+    "    out, seen = [], set()\n",
+    "    for v in vals:\n",
+    "        tok = extract_model_token(v)\n",
+    "        if tok and tok.lower() != \"nan\" and tok not in seen:\n",
+    "            seen.add(tok); out.append(tok)\n",
+    "    return out\n",
+    "\n",
+    "def gpt_pick_from_candidates(old_row: pd.Series, candidates: List[str], need: str) -> str:\n",
+    "    if client is None or not candidates:\n",
+    "        return \"\"\n",
+    "    sys = \"Pick the best replacement model. Choose only from candidates. Return strict JSON only.\"\n",
+    "    payload = {\n",
+    "        \"old_device\": {\n",
+    "            \"sku\": str(old_row.get(\"sku\",\"\")),\n",
+    "            \"manufacturer\": str(old_row.get(\"manufacturer\",\"\")),\n",
+    "            \"description\": str(old_row.get(\"description\",\"\")),\n",
+    "            \"need\": need,\n",
+    "        },\n",
+    "        \"candidates\": candidates[:40],\n",
+    "        \"output_schema\": {\"choice\":\"string\"}\n",
+    "    }\n",
+    "    out = gpt_json(sys, payload, max_tokens=240) or {}\n",
+    "    choice = str(out.get(\"choice\",\"\") or \"\").strip()\n",
+    "    return choice if choice in candidates else \"\"\n",
+    "\n",
+    "def fallback_5g_from_dec(canon_make: str) -> str:\n",
+    "    pool5 = df_dec[(df_dec[\"_canon_make\"] == canon_make) & (df_dec[\"_is5g\"] == True)]\n",
+    "    return str(pool5.iloc[0][\"Model\"]).strip() if not pool5.empty else \"\"\n",
+    "\n",
+    "def pick_replacements_lifecycle(row: pd.Series, status: str, use_gpt: bool = True) -> Dict[str, Any]:\n",
+    "    canon = str(row.get(\"_canon_make\",\"UNKNOWN\"))\n",
+    "    manufacturer = str(row.get(\"manufacturer\",\"\") or \"\")\n",
+    "\n",
+    "    sug_raw = safe_str(row.get(\"suggested_replacement\",\"\"))\n",
+    "    adv_raw = safe_str(row.get(\"advanced_5g_option\",\"\"))\n",
+    "\n",
+    "    has_4g_alt = bool(sug_raw.strip())\n",
+    "    has_5g_alt = bool(adv_raw.strip())\n",
+    "\n",
+    "    # Treat as 4G if the description indicates LTE OR lifecycle provides a 4G suggested replacement\n",
+    "    is_4g = device_is_4g(row) or has_4g_alt\n",
+    "\n",
+    "    # Provide 5G option if the unit is 4G, EOS/EOL, or lifecycle explicitly provides advanced_5g_option\n",
+    "    want_5g = is_4g or (status in {\"End of Sale\",\"End of Life\"}) or has_5g_alt\n",
+    "\n",
+    "    # 4G alternative: show whenever lifecycle provides it (or device appears 4G)\n",
+    "    repl_4g = \"Not applicable\"\n",
+    "    if is_4g or has_4g_alt:\n",
+    "        repl_4g = extract_model_token(sug_raw)\n",
+    "        if not repl_4g:\n",
+    "            cand4 = candidate_4g_models_from_lifecycle(manufacturer)\n",
+    "            repl_4g = (gpt_pick_from_candidates(row, cand4, \"4G alternative\") if (use_gpt and client) else \"\") or (cand4[0] if cand4 else \"\")\n",
+    "        if not repl_4g:\n",
+    "            repl_4g = \"Not applicable\"\n",
+    "\n",
+    "    # 5G replacement: prefer lifecycle advanced_5g_option whenever present\n",
+    "    repl_5g = \"Not listed\"\n",
+    "    if want_5g:\n",
+    "        repl_5g = extract_model_token(adv_raw)\n",
+    "        if not repl_5g:\n",
+    "            cand5 = candidate_5g_models_from_lifecycle(manufacturer)\n",
+    "            repl_5g = (gpt_pick_from_candidates(row, cand5, \"5G replacement/upgrade\") if (use_gpt and client) else \"\") or (cand5[0] if cand5 else \"\")\n",
+    "        if not repl_5g:\n",
+    "            repl_5g = fallback_5g_from_dec(canon) or \"Not listed\"\n",
+    "\n",
+    "    if repl_5g.lower() == \"nan\":\n",
+    "        repl_5g = \"Not listed\"\n",
+    "\n",
+    "    return {\"repl_4g\": repl_4g, \"repl_5g\": repl_5g, \"sources\": [\"lifecycle_csv\"] + ([\"gpt\"] if (use_gpt and client) else [])}\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Antennas (Parsec-only)\n",
+    "# ============================\n",
+    "PARSEC_FAMILY_WORDS = {\"chinook\",\"labrador\",\"boxer\",\"bloodhound\",\"husky\",\"beagle\",\"mastiff\",\"collie\",\"shepherd\",\"belgian\",\"australian\",\"terrier\",\"pyrenees\"}\n",
+    "BAD_NAME_MARKERS = {\"customization\",\"standard connectors\",\"connectors\",\"features\",\"benefits\",\"specifications\",\"mechanical\",\"electrical\",\"mounting\",\"accessories\",\"description:\",\"standard sku\"}\n",
+    "\n",
+    "def clean_line(s: str) -> str:\n",
+    "    s = re.sub(r\"\\s+\", \" \", str(s or \"\").strip())\n",
+    "    if re.fullmatch(r\"-[a-z0-9]+\", s.lower()):\n",
+    "        return \"\"\n",
+    "    return s\n",
+    "\n",
+    "def is_bad_name_line(line: str) -> bool:\n",
+    "    low = line.lower()\n",
+    "    if any(m in low for m in BAD_NAME_MARKERS):\n",
+    "        return True\n",
+    "    if re.search(r\"\\b-[a-z0-9]{1,4}\\b\", low) and len(low) <= 25:\n",
+    "        return True\n",
+    "    return False\n",
+    "\n",
+    "def family_from_line(line: str) -> str:\n",
+    "    low = line.lower()\n",
+    "    for fam in PARSEC_FAMILY_WORDS:\n",
+    "        if fam in low:\n",
+    "            return fam.capitalize()\n",
+    "    return \"\"\n",
+    "\n",
+    "def parsec_connectors_from_card(t: str) -> str:\n",
+    "    m = re.search(r\"Standard\\s+Connectors:\\s*(.+)\", t, flags=re.IGNORECASE)\n",
+    "    if m:\n",
+    "        return re.sub(r\"\\s+\", \" \", m.group(1).strip())[:80]\n",
+    "    return \"\"\n",
+    "\n",
+    "def parsec_mounts_from_card(t: str) -> List[str]:\n",
+    "    mounts = []\n",
+    "    for m in re.finditer(r\"Mount:\\s*(.+)\", t, flags=re.IGNORECASE):\n",
+    "        val = re.sub(r\"\\s+\", \" \", m.group(1).strip())\n",
+    "        parts = [p.strip().lower() for p in val.split(\",\") if p.strip()]\n",
+    "        mounts.extend(parts)\n",
+    "    out = []\n",
+    "    seen = set()\n",
+    "    for x in mounts:\n",
+    "        if x not in seen:\n",
+    "            seen.add(x); out.append(x)\n",
+    "    return out\n",
+    "\n",
+    "def parsec_name_from_card(card_text: str) -> str:\n",
+    "    lines = [clean_line(ln) for ln in str(card_text or \"\").splitlines()]\n",
+    "    lines = [ln for ln in lines if ln]\n",
+    "\n",
+    "    for ln in lines:\n",
+    "        if is_bad_name_line(ln):\n",
+    "            continue\n",
+    "        fam = family_from_line(ln)\n",
+    "        if fam:\n",
+    "            return fam\n",
+    "\n",
+    "    sku_i = None\n",
+    "    for i, ln in enumerate(lines):\n",
+    "        if \"standard sku\" in ln.lower():\n",
+    "            sku_i = i\n",
+    "            break\n",
+    "    if sku_i is not None:\n",
+    "        window = lines[max(0, sku_i - 12):sku_i]\n",
+    "        for ln in reversed(window):\n",
+    "            if is_bad_name_line(ln):\n",
+    "                continue\n",
+    "            if 3 <= len(ln) <= 40 and re.search(r\"[A-Za-z]\", ln):\n",
+    "                return ln.split()[0].capitalize()\n",
+    "\n",
+    "    return \"Parsec antenna\"\n",
+    "\n",
+    "def parsec_part_from_card(t: str) -> str:\n",
+    "    m = re.search(r\"Standard\\s+SKU:\\s*([A-Z0-9]+)\", t)\n",
+    "    return m.group(1).strip() if m else \"\"\n",
+    "\n",
+    "def parsec_desc_from_card(t: str) -> str:\n",
+    "    m = re.search(r\"Description:\\s*(.+?)(?:\\n|$)\", t, flags=re.IGNORECASE)\n",
+    "    return re.sub(r\"\\s+\",\" \",m.group(1).strip())[:220] if m else \"\"\n",
+    "\n",
+    "def parsec_retrieve(query: str, top_k: int = 12) -> List[Dict[str, Any]]:\n",
+    "    qv = embedder.encode([query], normalize_embeddings=True)\n",
+    "    qv = np.asarray(qv, dtype=np.float32)\n",
+    "    scores, ids = parsec_index.search(qv, top_k)\n",
+    "    out: List[Dict[str, Any]] = []\n",
+    "    for sc, i in zip(scores[0].tolist(), ids[0].tolist()):\n",
+    "        if 0 <= int(i) < len(parsec_cards):\n",
+    "            card = parsec_cards[int(i)]\n",
+    "            out.append({\n",
+    "                \"score\": float(sc),\n",
+    "                \"name\": parsec_name_from_card(card),\n",
+    "                \"part_number\": parsec_part_from_card(card),\n",
+    "                \"description\": parsec_desc_from_card(card),\n",
+    "                \"connectors\": parsec_connectors_from_card(card),\n",
+    "                \"mounts\": parsec_mounts_from_card(card),\n",
+    "                \"_card\": card.lower(),\n",
+    "            })\n",
+    "    return out\n",
+    "\n",
+    "def choose_best_parsec(cands: List[Dict[str, Any]], mode: str) -> Dict[str, Any]:\n",
+    "    best = None\n",
+    "    best_score = -1e9\n",
+    "\n",
+    "    for c in cands:\n",
+    "        card = c.get(\"_card\",\"\")\n",
+    "        mounts = c.get(\"mounts\", []) or []\n",
+    "        score = float(c.get(\"score\", 0.0))\n",
+    "\n",
+    "        if \"omni\" in card:\n",
+    "            score += 0.6\n",
+    "        if \"directional\" in card:\n",
+    "            score -= 1.5\n",
+    "\n",
+    "        if mode == \"vehicle\":\n",
+    "            if any(\"magnetic\" in m for m in mounts):\n",
+    "                score += 3.0\n",
+    "            if any(\"through\" in m for m in mounts):\n",
+    "                score += 2.0\n",
+    "            if any(\"wall\" in m for m in mounts) or any(\"pole\" in m for m in mounts):\n",
+    "                score -= 1.2\n",
+    "            if \"app: fixed\" in card and \"mobile\" not in card:\n",
+    "                score -= 2.0\n",
+    "\n",
+    "        if mode == \"stationary\":\n",
+    "            if any(\"wall\" in m for m in mounts):\n",
+    "                score += 2.0\n",
+    "            if any(\"pole\" in m for m in mounts):\n",
+    "                score += 1.8\n",
+    "\n",
+    "        if score > best_score:\n",
+    "            best_score = score\n",
+    "            best = c\n",
+    "\n",
+    "    if not best:\n",
+    "        return {\"name\":\"Parsec antenna\",\"part_number\":\"\",\"description\":\"\",\"connectors\":\"\",\"mounts\":[]}\n",
+    "\n",
+    "    best = dict(best)\n",
+    "    best.pop(\"_card\", None)\n",
+    "    return best\n",
+    "\n",
+    "\n",
+    "def infer_mimo_for_5g(repl_5g_model: str) -> str:\n",
+    "    \"\"\"Rule: every 5G router uses a 4x4 antenna.\"\"\"\n",
+    "    return \"4x4\"\n",
+    "\n",
+    "    # If the model name hints 5G, lean 4x4\n",
+    "    if \"5g\" in model.lower() or model.upper().startswith((\"R\", \"E\", \"S\", \"IX\", \"RUTM\")):\n",
+    "        default = \"4x4\"\n",
+    "    else:\n",
+    "        default = \"2x2\"\n",
+    "\n",
+    "    # Use dec2025routers.csv if we can match the model under the same maker family\n",
+    "    try:\n",
+    "        pool = df_dec[df_dec[\"_canon_make\"] == canon_make].copy()\n",
+    "        if pool.empty:\n",
+    "            return default\n",
+    "        hit = process.extractOne(norm_text(model), pool[\"_norm_model\"].tolist(), scorer=fuzz.WRatio)\n",
+    "        if not hit or hit[1] < MATCH_OK:\n",
+    "            return default\n",
+    "        row = pool.iloc[int(hit[2])]\n",
+    "        txt2 = (str(row.get(\"Antennas (internal/external/both)\", \"\")) + \" \" + str(row.get(\"Modem Type\", \"\")) + \" \" + str(row.get(\"Special notes\",\"\"))).lower()\n",
+    "        if \"4x4\" in txt2 or \"4 x 4\" in txt2 or \"4x 4\" in txt2:\n",
+    "            return \"4x4\"\n",
+    "        if \"2x2\" in txt2 or \"2 x 2\" in txt2:\n",
+    "            return \"2x2\"\n",
+    "        # If modem type includes 5G, lean 4x4\n",
+    "        if \"5g\" in txt2 or \"nr\" in txt2:\n",
+    "            return \"4x4\"\n",
+    "        return default\n",
+    "    except Exception:\n",
+    "        return default\n",
+    "\n",
+    "def antenna_options_for(router_model: str, tech: str, mimo: str) -> Dict[str, Any]:\n",
+    "    q_stationary = f\"{router_model} {tech} {mimo} omni stationary pole wall fixed site Parsec\"\n",
+    "    q_vehicle = f\"{router_model} {tech} {mimo} omni vehicle mobile magnetic through-bolt Parsec\"\n",
+    "\n",
+    "    cand_stationary = parsec_retrieve(q_stationary, top_k=12)\n",
+    "    cand_vehicle = parsec_retrieve(q_vehicle, top_k=12)\n",
+    "\n",
+    "    s = choose_best_parsec(cand_stationary, mode=\"stationary\")\n",
+    "    v = choose_best_parsec(cand_vehicle, mode=\"vehicle\")\n",
+    "\n",
+    "    s.update({\"mimo\": mimo, \"why\": \"Stationary omni best match.\"})\n",
+    "    v.update({\"mimo\": mimo, \"why\": \"Vehicle omni best match.\"})\n",
+    "\n",
+    "    return {\"stationary_omni\": s, \"vehicle_omni\": v, \"sources\":[\"parsec_rag\"]}\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Install-ready checklist\n",
+    "# ============================\n",
+    "def install_ready_checklist(current_sku: str, repl: Dict[str,Any], ant: Dict[str,Any]) -> str:\n",
+    "    st = ant.get(\"stationary_omni\", {})\n",
+    "    vh = ant.get(\"vehicle_omni\", {})\n",
+    "    if client is not None:\n",
+    "        sys = \"Create a short, install-ready checklist for a Verizon rep. Return markdown only.\"\n",
+    "        payload = {\"current_device\": current_sku, \"replacements\": repl, \"antennas\": {\"stationary\": st, \"vehicle\": vh}}\n",
+    "        resp = client.responses.create(\n",
+    "            model=OPENAI_MODEL,\n",
+    "            reasoning=OPENAI_REASONING,\n",
+    "            input=[{\"role\":\"system\",\"content\":sys},{\"role\":\"user\",\"content\":json.dumps(payload)}],\n",
+    "            max_output_tokens=520,\n",
+    "        )\n",
+    "        return (getattr(resp, \"output_text\", \"\") or \"\").strip()\n",
+    "    return \"\\n\".join([\n",
+    "        \"### Install-ready checklist\",\n",
+    "        f\"- Current device: {current_sku}\",\n",
+    "        f\"- 5G replacement: {repl.get('repl_5g','')}\",\n",
+    "        f\"- 4G alternative: {repl.get('repl_4g','Not applicable')}\",\n",
+    "        f\"- Stationary omni antenna: {st.get('name','')} (PN {st.get('part_number','')})\",\n",
+    "        f\"- Vehicle omni antenna: {vh.get('name','')} (PN {vh.get('part_number','')})\",\n",
+    "        \"- Next steps: confirm mounting + cable lengths + power; place order; schedule install.\",\n",
+    "    ])\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Batch mode (NO GPT)\n",
+    "# ============================\n",
+    "def parse_batch_inputs(text_blob: str, file_obj: Any) -> List[str]:\n",
+    "    items: List[str] = []\n",
+    "    if file_obj is not None:\n",
+    "        try:\n",
+    "            path = file_obj.name if hasattr(file_obj, \"name\") else str(file_obj)\n",
+    "            df = pd.read_csv(path)\n",
+    "            col = df.columns[0]\n",
+    "            items.extend([str(x).strip() for x in df[col].tolist() if str(x).strip()])\n",
+    "        except Exception:\n",
+    "            pass\n",
+    "    if text_blob:\n",
+    "        for ln in str(text_blob).splitlines():\n",
+    "            ln = ln.strip()\n",
+    "            if ln:\n",
+    "                items.append(ln)\n",
+    "    seen=set()\n",
+    "    out=[]\n",
+    "    for x in items:\n",
+    "        k=norm_text(x)\n",
+    "        if k and k not in seen:\n",
+    "            seen.add(k); out.append(x)\n",
+    "    return out\n",
+    "\n",
+    "def run_batch(text_blob: str, file_obj: Any, include_antennas: bool):\n",
+    "    inputs = parse_batch_inputs(text_blob, file_obj)\n",
+    "    if not inputs:\n",
+    "        return \"\", None, None, \"\"\n",
+    "\n",
+    "    rows=[]\n",
+    "    for item in inputs:\n",
+    "        res = resolve_device(item)\n",
+    "        if res.get(\"mode\") != \"ok\":\n",
+    "            rows.append({\"Input\": item, \"Matched\":\"\", \"Status\":\"Needs review\", \"EOS\":\"\", \"EOL\":\"\", \"4G alternative\":\"\", \"5G replacement\":\"\", \"Notes\":\"Not found/ambiguous\"})\n",
+    "            continue\n",
+    "\n",
+    "        life_row = df_eos.iloc[int(res[\"row_idx\"])]\n",
+    "        eos, eol, status = row_to_dates_and_status(life_row)\n",
+    "        repl = pick_replacements_lifecycle(life_row, status, use_gpt=False)\n",
+    "\n",
+    "        rows.append({\n",
+    "            \"Input\": item,\n",
+    "            \"Matched\": str(life_row.get(\"sku\",\"\")),\n",
+    "            \"Status\": status,\n",
+    "            \"EOS\": eos,\n",
+    "            \"EOL\": eol,\n",
+    "            \"4G alternative\": repl.get(\"repl_4g\",\"\"),\n",
+    "            \"5G replacement\": repl.get(\"repl_5g\",\"\"),\n",
+    "            \"Notes\": \"\",\n",
+    "        })\n",
+    "\n",
+    "    out_df = pd.DataFrame(rows)\n",
+    "    counts = out_df[\"Status\"].value_counts(dropna=False).to_dict()\n",
+    "    top_5g = out_df[\"5G replacement\"].value_counts(dropna=False).head(5).to_dict()\n",
+    "    summary = f\"Rows: {len(out_df)} | \" + \" | \".join([f\"{k}: {v}\" for k,v in counts.items()])\n",
+    "    rollup = \"Top 5G recommendations:\\n\" + \"\\n\".join([f\"- {k}: {v}\" for k,v in top_5g.items() if str(k).strip()])\n",
+    "\n",
+    "    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=\".csv\")\n",
+    "    out_df.to_csv(tmp.name, index=False)\n",
+    "\n",
+    "    return summary, out_df, tmp.name, rollup\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Replacement feature table + manufacturer link (5G device)\n",
+    "# ============================\n",
+    "\n",
+    "FEATURE_COLS = [\"Device\", \"Modem technology\", \"WiFi\", \"Ports\", \"Antennas\", \"Ruggedness\", \"Use case\"]\n",
+    "\n",
+    "# Manufacturer domains used for best-effort link resolution (no non-maker domains).\n",
+    "MAKER_DOMAINS = {\n",
+    "    \"CRADLEPOINT\": [\"cradlepoint.com\", \"ericsson.com\"],\n",
+    "    \"SIERRA\": [\"semtech.com\", \"airlink.com\"],\n",
+    "    \"FEENEY\": [\"inseego.com\"],\n",
+    "    \"DIGI\": [\"digi.com\"],\n",
+    "    \"CISCO_MERAKI\": [\"meraki.cisco.com\", \"cisco.com\"],\n",
+    "    \"CISCO\": [\"cisco.com\"],\n",
+    "    \"TELTONIKA\": [\"teltonika-networks.com\"],\n",
+    "    \"UNKNOWN\": [],\n",
+    "}\n",
+    "\n",
+    "HTTP_HEADERS = {\n",
+    "    \"User-Agent\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 \"\n",
+    "                  \"(KHTML, like Gecko) Chrome/120.0 Safari/537.36\"\n",
+    "}\n",
+    "HTTP_TIMEOUT = 12\n",
+    "\n",
+    "def _best_effort_manufacturer_url(model: str, canon_make: str) -> str:\n",
+    "    \\\"\\\"\\\"Try to find a manufacturer page or datasheet link using simple on-domain searches.\n",
+    "    If we can't confirm a page, return the manufacturer homepage for the maker family.\n",
+    "    \\\"\\\"\\\"\n",
+    "    model = str(model or \"\").strip()\n",
+    "    if not model or model in {\"Not listed\", \"Not applicable\"}:\n",
+    "        return \"\"\n",
+    "\n",
+    "    domains = MAKER_DOMAINS.get(canon_make, []) or []\n",
+    "    if not domains:\n",
+    "        return \"\"\n",
+    "\n",
+    "    # Candidate on-domain search URLs (common patterns across sites).\n",
+    "    # We keep these on the manufacturer domain (no Google/Bing).\n",
+    "    q = re.sub(r\"\\s+\", \"+\", model)\n",
+    "    url_candidates = []\n",
+    "    for d in domains:\n",
+    "        url_candidates += [\n",
+    "            f\"https://{d}/search?q={q}\",\n",
+    "            f\"https://{d}/search?query={q}\",\n",
+    "            f\"https://{d}/?s={q}\",\n",
+    "            f\"https://www.{d}/search?q={q}\",\n",
+    "            f\"https://www.{d}/search?query={q}\",\n",
+    "            f\"https://www.{d}/?s={q}\",\n",
+    "        ]\n",
+    "\n",
+    "    # Also try a few direct product patterns for known makers (best effort).\n",
+    "    if canon_make == \"TELTONIKA\":\n",
+    "        slug = model.lower()\n",
+    "        url_candidates += [\n",
+    "            f\"https://teltonika-networks.com/products/routers/{slug}\",\n",
+    "            f\"https://teltonika-networks.com/product/{slug}\",\n",
+    "            \"https://teltonika-networks.com/products/routers/\",\n",
+    "        ]\n",
+    "    if canon_make == \"DIGI\":\n",
+    "        url_candidates += [\n",
+    "            \"https://www.digi.com/products/networking/cellular-routers\",\n",
+    "            f\"https://www.digi.com/search?q={q}\",\n",
+    "        ]\n",
+    "    if canon_make == \"CRADLEPOINT\":\n",
+    "        url_candidates += [\n",
+    "            \"https://cradlepoint.com/products/\",\n",
+    "            f\"https://cradlepoint.com/?s={q}\",\n",
+    "        ]\n",
+    "    if canon_make in {\"CISCO\", \"CISCO_MERAKI\"}:\n",
+    "        url_candidates += [\n",
+    "            f\"https://www.cisco.com/c/en/us/search.html?q={q}\",\n",
+    "        ]\n",
+    "\n",
+    "    # Try to confirm a working page (HTTP 200 and model string somewhere in HTML).\n",
+    "    for u in url_candidates[:18]:\n",
+    "        try:\n",
+    "            import requests\n",
+    "            r = requests.get(u, headers=HTTP_HEADERS, timeout=HTTP_TIMEOUT, allow_redirects=True)\n",
+    "            if r.status_code != 200:\n",
+    "                continue\n",
+    "            html = (r.text or \"\").lower()\n",
+    "            if model.lower() in html or \"datasheet\" in html or \"data sheet\" in html:\n",
+    "                return r.url\n",
+    "        except Exception:\n",
+    "            continue\n",
+    "\n",
+    "    # Fallback: maker homepage\n",
+    "    d0 = domains[0]\n",
+    "    return f\"https://{d0}\"\n",
+    "\n",
+    "def _features_from_dec(model: str, canon_make: str) -> Dict[str, str]:\n",
+    "    \\\"\\\"\\\"Lookup a router model in dec2025routers.csv and return the key feature fields.\\\"\\\"\\\"\n",
+    "    if not model or model in {\"Not listed\", \"Not applicable\"}:\n",
+    "        return {k: \"Not listed\" for k in FEATURE_COLS[1:]}\n",
+    "\n",
+    "    pool = df_dec[df_dec[\"_canon_make\"] == canon_make].copy()\n",
+    "    if pool.empty:\n",
+    "        return {k: \"Not listed\" for k in FEATURE_COLS[1:]}\n",
+    "\n",
+    "    hit = process.extractOne(norm_text(model), pool[\"_norm_model\"].tolist(), scorer=fuzz.WRatio)\n",
+    "    if not hit or hit[1] < MATCH_OK:\n",
+    "        return {k: \"Not listed\" for k in FEATURE_COLS[1:]}\n",
+    "\n",
+    "    r = pool.iloc[int(hit[2])]\n",
+    "    ports = f\"WAN: {r.get('WAN ports and speed','')} | LAN: {r.get('LAN ports and speed','')}\"\n",
+    "    return {\n",
+    "        \"Modem technology\": str(r.get(\"Modem Type\",\"\")) or \"Not listed\",\n",
+    "        \"WiFi\": str(r.get(\"WiFi type\",\"\")) or \"Not listed\",\n",
+    "        \"Ports\": ports.strip() if ports.strip() else \"Not listed\",\n",
+    "        \"Antennas\": str(r.get(\"Antennas (internal/external/both)\",\"\")) or \"Not listed\",\n",
+    "        \"Ruggedness\": str(r.get(\"Ruggedization\",\"\")) or \"Not listed\",\n",
+    "        \"Use case\": str(r.get(\"Primary use case\",\"\")) or \"Not listed\",\n",
+    "    }\n",
+    "\n",
+    "def _gpt_fill_feature_row(device_label: str, model: str, canon_make: str, row: Dict[str, str]) -> Dict[str, str]:\n",
+    "    \\\"\\\"\\\"If dec can't supply values, ask GPT to fill missing ones (best guess).\\\"\\\"\\\"\n",
+    "    if client is None:\n",
+    "        return row\n",
+    "\n",
+    "    missing = [k for k,v in row.items() if (not v) or str(v).strip().lower() in {\"not listed\",\"nan\",\"\"}]\n",
+    "    if not missing:\n",
+    "        return row\n",
+    "\n",
+    "    sys = \"Fill missing router feature fields for a Verizon rep. Return strict JSON only.\"\n",
+    "    payload = {\n",
+    "        \"device_label\": device_label,\n",
+    "        \"model\": model,\n",
+    "        \"maker_family\": canon_make,\n",
+    "        \"known\": row,\n",
+    "        \"fill_only\": missing,\n",
+    "        \"rules\": [\n",
+    "            \"Fill only the requested fields.\",\n",
+    "            \"Best guess if needed. Short phrases only.\",\n",
+    "            \"Return JSON only.\"\n",
+    "        ],\n",
+    "        \"output_schema\": {k: \"string\" for k in missing}\n",
+    "    }\n",
+    "    out = gpt_json(sys, payload, max_tokens=260) or {}\n",
+    "    for k in missing:\n",
+    "        val = str(out.get(k, \"\") or \"\").strip()\n",
+    "        if val:\n",
+    "            row[k] = val\n",
+    "    return row\n",
+    "\n",
+    "def build_replacement_features_table(repl_4g: str, repl_5g: str, canon_make: str) -> pd.DataFrame:\n",
+    "    rows = []\n",
+    "\n",
+    "    # 4G\n",
+    "    row4 = _features_from_dec(repl_4g, canon_make)\n",
+    "    row4 = _gpt_fill_feature_row(\"4G alternative\", repl_4g, canon_make, row4)\n",
+    "    rows.append({\"Device\": \"4G alternative\", **row4})\n",
+    "\n",
+    "    # 5G\n",
+    "    row5 = _features_from_dec(repl_5g, canon_make)\n",
+    "    row5 = _gpt_fill_feature_row(\"5G replacement\", repl_5g, canon_make, row5)\n",
+    "    rows.append({\"Device\": \"5G replacement\", **row5})\n",
+    "\n",
+    "    df = pd.DataFrame(rows, columns=FEATURE_COLS)\n",
+    "    return df\n",
+    "\n",
+    "# ============================\n",
+    "# Output\n",
+    "# ============================\n",
+    "def assemble_output(life_row: pd.Series, status: str, eos: str, eol: str, repl: Dict[str,Any], ant: Dict[str,Any]) -> str:\n",
+    "    current_name = f\"{life_row.get('sku','')} — {life_row.get('description','')}\".strip(\" —\")\n",
+    "    st = ant.get(\"stationary_omni\", {})\n",
+    "    vh = ant.get(\"vehicle_omni\", {})\n",
+    "\n",
+    "    lines = []\n",
+    "    lines.append(f\"1. Current device: **{current_name}**\")\n",
+    "    lines.append(f\"2. Status: **{status}**\")\n",
+    "    lines.append(f\"3. End of Sale date: **{eos}**\")\n",
+    "    lines.append(f\"4. End of Life date: **{eol}**\")\n",
+    "    lines.append(f\"5. 4G alternative (lifecycle): **{repl.get('repl_4g','Not applicable')}**\")\n",
+    "    lines.append(f\"6. 5G replacement (lifecycle): **{repl.get('repl_5g','Not listed')}**\")\n",
+    "    lines.append(\"7. Antenna options (Parsec-only):\")\n",
+    "    conn_s = f\" | Conn: {st.get('connectors','')}\" if st.get(\"connectors\") else \"\"\n",
+    "    conn_v = f\" | Conn: {vh.get('connectors','')}\" if vh.get(\"connectors\") else \"\"\n",
+    "    lines.append(f\"   - Stationary (Omni): **{st.get('name','')}** (Part #: {st.get('part_number','')}) — {st.get('description','')} — MIMO: {st.get('mimo','')}{conn_s}\")\n",
+    "    lines.append(f\"   - Vehicle (Omni): **{vh.get('name','')}** (Part #: {vh.get('part_number','')}) — {vh.get('description','')} — MIMO: {vh.get('mimo','')}{conn_v}\")\n",
+    "\n",
+    "    lines.append(\"\\nSources (debug):\")\n",
+    "    for s in repl.get(\"sources\", []) if isinstance(repl.get(\"sources\"), list) else []:\n",
+    "        lines.append(f\"- {s}\")\n",
+    "    lines.append(\"- ParsecCatalog.pdf (local RAG)\")\n",
+    "    lines.append(\"- routers_eos_eol_by_sku.csv (replacements)\")\n",
+    "    return \"\\n\".join(lines)\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Gradio callbacks\n",
+    "# IMPORTANT: no dict state and ALL events have api_name=False (prevents api_info schema generation)\n",
+    "# ============================\n",
+    "def run_lookup(user_text: str, st_json: str):\n",
+    "    user_text = str(user_text or \"\").strip()\n",
+    "    if not user_text:\n",
+    "        return \"Enter a router SKU/model.\", \"\", None, \"\", gr.update(visible=False), gr.update(visible=False), \"{}\", \"\"\n",
+    "\n",
+    "    res = resolve_device(user_text)\n",
+    "\n",
+    "    if res.get(\"mode\") == \"pick\":\n",
+    "        opts = res.get(\"options\", [])\n",
+    "        choices = [o[\"label\"] for o in opts]\n",
+    "        st2 = {\"mode\":\"pick\",\"options\": opts, \"raw\": user_text}\n",
+    "        return \"Did you mean A or B? Pick one, then click Use selection.\", \"\", None, \"\", gr.update(choices=choices, value=None, visible=True), gr.update(visible=True), state_dump(st2), \"\"\n",
+    "\n",
+    "    if res.get(\"mode\") != \"ok\":\n",
+    "        return \"Not found.\", \"\", None, \"\", gr.update(visible=False), gr.update(visible=False), \"{}\", \"\"\n",
+    "\n",
+    "    life_row = df_eos.iloc[int(res[\"row_idx\"])]\n",
+    "    eos, eol, status = row_to_dates_and_status(life_row)\n",
+    "\n",
+    "    repl = pick_replacements_lifecycle(life_row, status, use_gpt=True)\n",
+    "    canon_make = str(life_row.get(\"_canon_make\",\"UNKNOWN\"))\n",
+    "    mimo = infer_mimo_for_5g(repl.get(\"repl_5g\",\"\"))\n",
+    "    tech = \"5G\" if repl.get(\"repl_5g\") and repl.get(\"repl_5g\") != \"Not listed\" else (\"4G\" if device_is_4g(life_row) else \"Unknown\")\n",
+    "    ant = antenna_options_for(repl.get(\"repl_5g\") or str(life_row.get(\"sku\",\"\")), tech, mimo)\n",
+    "\n",
+    "    output = assemble_output(life_row, status, eos, eol, repl, ant)\n",
+    "    st_out = {\"row_idx\": int(res[\"row_idx\"]), \"repl\": repl, \"ant\": ant, \"raw\": user_text}\n",
+    "    url5 = _best_effort_manufacturer_url(repl.get('repl_5g',''), canon_make)\n",
+    "    link = f\"**5G manufacturer page (best effort):** {url5}\" if url5 else \"\"\n",
+    "    feat_df = build_replacement_features_table(repl.get('repl_4g',''), repl.get('repl_5g',''), canon_make)\n",
+    "    return output, link, feat_df, \"\", gr.update(visible=False), gr.update(visible=False), state_dump(st_out), \"\"\n",
+    "\n",
+    "def use_selection(selected_label: str, st_json: str):\n",
+    "    st = state_load(st_json)\n",
+    "    if not st or st.get(\"mode\") != \"pick\":\n",
+    "        return \"Run a search first.\", \"\", None, \"\", gr.update(visible=False), gr.update(visible=False), \"{}\", \"\"\n",
+    "\n",
+    "    if not selected_label:\n",
+    "        return \"Pick A or B first.\", \"\", None, \"\", gr.update(visible=True), gr.update(visible=True), st_json, \"\"\n",
+    "\n",
+    "    chosen_row = None\n",
+    "    for o in st.get(\"options\", []):\n",
+    "        if o.get(\"label\") == selected_label:\n",
+    "            chosen_row = int(o[\"row_idx\"])\n",
+    "            break\n",
+    "    if chosen_row is None:\n",
+    "        return \"Pick a valid option.\", \"\", None, \"\", gr.update(visible=True), gr.update(visible=True), st_json, \"\"\n",
+    "\n",
+    "    life_row = df_eos.iloc[int(chosen_row)]\n",
+    "    eos, eol, status = row_to_dates_and_status(life_row)\n",
+    "\n",
+    "    repl = pick_replacements_lifecycle(life_row, status, use_gpt=True)\n",
+    "    canon_make = str(life_row.get(\"_canon_make\",\"UNKNOWN\"))\n",
+    "    mimo = infer_mimo_for_5g(repl.get(\"repl_5g\",\"\"))\n",
+    "    tech = \"5G\" if repl.get(\"repl_5g\") and repl.get(\"repl_5g\") != \"Not listed\" else (\"4G\" if device_is_4g(life_row) else \"Unknown\")\n",
+    "    ant = antenna_options_for(repl.get(\"repl_5g\") or str(life_row.get(\"sku\",\"\")), tech, mimo)\n",
+    "\n",
+    "    output = assemble_output(life_row, status, eos, eol, repl, ant)\n",
+    "    st_out = {\"row_idx\": int(chosen_row), \"repl\": repl, \"ant\": ant, \"raw\": st.get(\"raw\",\"\")}\n",
+    "    url5 = _best_effort_manufacturer_url(repl.get('repl_5g',''), canon_make)\n",
+    "    link = f\"**5G manufacturer page (best effort):** {url5}\" if url5 else \"\"\n",
+    "    feat_df = build_replacement_features_table(repl.get('repl_4g',''), repl.get('repl_5g',''), canon_make)\n",
+    "    return output, link, feat_df, \"\", gr.update(visible=False), gr.update(visible=False), state_dump(st_out), \"\"\n",
+    "\n",
+    "def make_install_ready(st_json: str):\n",
+    "    st = state_load(st_json)\n",
+    "    if not st or \"row_idx\" not in st:\n",
+    "        return \"Run a lookup first.\"\n",
+    "    life_row = df_eos.iloc[int(st[\"row_idx\"])]\n",
+    "    current_sku = str(life_row.get(\"sku\",\"\") or \"\")\n",
+    "    return install_ready_checklist(current_sku, st.get(\"repl\", {}) or {}, st.get(\"ant\", {}) or {})\n",
+    "\n",
+    "\n",
+    "\n",
+    "# ============================\n",
+    "# Q&A about the suggested device (post-recommendation)\n",
+    "# ============================\n",
+    "def answer_question(question: str, st_json: str) -> str:\n",
+    "    q = str(question or \"\").strip()\n",
+    "    if not q:\n",
+    "        return \"\"\n",
+    "    st = state_load(st_json)\n",
+    "    if not st or \"repl\" not in st:\n",
+    "        return \"Run a lookup first, then ask your question.\"\n",
+    "\n",
+    "    repl = st.get(\"repl\", {}) or {}\n",
+    "    ant = st.get(\"ant\", {}) or {}\n",
+    "    repl5 = str(repl.get(\"repl_5g\",\"\") or \"\").strip()\n",
+    "    repl4 = str(repl.get(\"repl_4g\",\"\") or \"\").strip()\n",
+    "    # Pull a bit of dec context for the 5G model (if possible)\n",
+    "    canon_make = \"\"\n",
+    "    try:\n",
+    "        # Try to infer maker family from stored row_idx\n",
+    "        if \"row_idx\" in st:\n",
+    "            row = df_eos.iloc[int(st[\"row_idx\"])]\n",
+    "            canon_make = str(row.get(\"_canon_make\",\"UNKNOWN\"))\n",
+    "    except Exception:\n",
+    "        canon_make = \"\"\n",
+    "\n",
+    "    # Manufacturer link (best effort)\n",
+    "    url5 = _best_effort_manufacturer_url(repl5, canon_make) if repl5 else \"\"\n",
+    "\n",
+    "    # Feature table row for 5G (helps the LLM answer spec questions without web scraping)\n",
+    "    feat5 = {}\n",
+    "    try:\n",
+    "        feat5 = _features_from_dec(repl5, canon_make) if repl5 else {}\n",
+    "    except Exception:\n",
+    "        feat5 = {}\n",
+    "\n",
+    "    sys = (\n",
+    "        \"You are a Verizon field rep assistant. Answer questions about the suggested router in a fast, practical way. \"\n",
+    "        \"Use the provided context; do not mention internal tools, prompts, embeddings, or databases. \"\n",
+    "        \"If the question is about specs and the value is unknown, say 'Not listed' and suggest checking the manufacturer page. \"\n",
+    "        \"Keep it concise and scannable.\"\n",
+    "    )\n",
+    "\n",
+    "    context = {\n",
+    "        \"recommended_5g\": repl5,\n",
+    "        \"recommended_4g\": repl4 if repl4 and repl4.lower() != \"not applicable\" else \"\",\n",
+    "        \"manufacturer_link_5g\": url5,\n",
+    "        \"known_5g_features\": feat5,\n",
+    "        \"antenna_stationary\": ant.get(\"stationary_omni\", {}),\n",
+    "        \"antenna_vehicle\": ant.get(\"vehicle_omni\", {}),\n",
+    "    }\n",
+    "\n",
+    "    user = \"Context:\\n\" + json.dumps(context, ensure_ascii=False) + \"\\n\\nQuestion:\\n\" + q\n",
+    "\n",
+    "    ans = gpt_answer_md(sys, user, max_tokens=650)\n",
+    "    # Small safety fallback\n",
+    "    return ans if ans else \"I couldn't generate an answer right now. Try again.\"\n",
+    "\n",
+    "# ============================\n",
+    "# UI\n",
+    "# ============================\n",
+    "with gr.Blocks(title=\"Only-Routers\") as demo:\n",
+    "    gr.Markdown(\"## Only-Routers\\nSingle lookup + Batch upload for Verizon reps.\")\n",
+    "\n",
+    "    with gr.Tabs():\n",
+    "        with gr.Tab(\"Single\"):\n",
+    "            user_text = gr.Textbox(label=\"Router SKU or model\", placeholder=\"Examples: IBR650B, AER1600, ES450, WR21, RUT240\", lines=1)\n",
+    "            st = gr.State(\"{}\")  # JSON string\n",
+    "\n",
+    "            check_btn = gr.Button(\"Check\", variant=\"primary\")\n",
+    "            pick_dd = gr.Dropdown(label=\"Pick A or B\", choices=[], visible=False)\n",
+    "            use_btn = gr.Button(\"Use selection\", visible=False)\n",
+    "\n",
+    "            output_md = gr.Markdown()\n",
+    "\n",
+    "            link_md = gr.Markdown()\n",
+    "            features_df = gr.Dataframe(headers=FEATURE_COLS, interactive=False, wrap=True)\n",
+    "\n",
+    "\n",
+    "gr.Markdown(\"### Questions about the suggested device?\")\n",
+    "question_box = gr.Textbox(label=\"Ask a question (optional)\", placeholder=\"Example: Does the 5G device support dual-SIM? What antenna ports does it have?\", lines=2)\n",
+    "ask_btn = gr.Button(\"Ask\", variant=\"secondary\")\n",
+    "qa_md = gr.Markdown()\n",
+    "\n",
+    "\n",
+    "            install_btn = gr.Button(\"Make install-ready checklist\")\n",
+    "            install_md = gr.Markdown()\n",
+    "\n",
+    "            check_btn.click(fn=run_lookup, inputs=[user_text, st], outputs=[output_md, link_md, features_df, qa_md, pick_dd, use_btn, st, install_md], api_name=False)\n",
+    "            use_btn.click(fn=use_selection, inputs=[pick_dd, st], outputs=[output_md, link_md, features_df, qa_md, pick_dd, use_btn, st, install_md], api_name=False)\n",
+    "            install_btn.click(fn=make_install_ready, inputs=[st], outputs=[install_md], api_name=False)\n",
+    "            ask_btn.click(fn=answer_question, inputs=[question_box, st], outputs=[qa_md], api_name=False)\n",
+    "\n",
+    "        with gr.Tab(\"Batch\"):\n",
+    "            gr.Markdown(\"Paste one per line or upload a CSV (first column). Batch runs fast (no GPT).\")\n",
+    "            batch_text = gr.Textbox(label=\"Paste devices (one per line)\", lines=8, placeholder=\"WR21\\nRUT240\\nIBR650B\")\n",
+    "            batch_file = gr.File(label=\"Upload CSV\", file_types=[\".csv\"])\n",
+    "            include_ant = gr.Checkbox(label=\"Include antenna picks (slower)\", value=False)\n",
+    "            run_btn = gr.Button(\"Run batch\", variant=\"primary\")\n",
+    "\n",
+    "            summary_md = gr.Markdown()\n",
+    "            rollup_md = gr.Markdown()\n",
+    "            table = gr.Dataframe(interactive=False, wrap=True)\n",
+    "            dl = gr.File(label=\"Download results CSV\")\n",
+    "\n",
+    "            run_btn.click(fn=run_batch, inputs=[batch_text, batch_file, include_ant], outputs=[summary_md, table, dl, rollup_md], api_name=False)\n",
+    "\n",
+    "# IMPORTANT: On Spaces, demo.launch() is correct; do NOT use share=True.\n",
+    "demo.launch(show_api=False)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

app.py

@@ -100,6 +100,22 @@ def gpt_json(system: str, payload: Dict[str, Any], max_tokens: int = 600) -> Dic
     return json_load_safe(getattr(resp, "output_text", "") or "")
 
 
+def gpt_answer_md(system: str, user: str, max_tokens: int = 650) -> str:
+    """Return a rep-friendly markdown answer."""
+    if client is None:
+        return "No API key is configured, so I can't answer detailed questions right now."
+    resp = client.responses.create(
+        model=OPENAI_MODEL,
+        reasoning=OPENAI_REASONING,
+        input=[
+            {"role": "system", "content": system},
+            {"role": "user", "content": user},
+        ],
+        max_output_tokens=max_tokens,
+    )
+    return (getattr(resp, "output_text", "") or "").strip()
+
+
 # ============================
 # Load data
 # ============================
@@ -722,11 +738,9 @@ def choose_best_parsec(cands: List[Dict[str, Any]], mode: str) -> Dict[str, Any]
     return best
 
 
-def infer_mimo_for_5g(model: str, canon_make: str) -> str:
-    """Best-effort MIMO guess for antenna selection (2x2 vs 4x4)."""
-    # If model is unknown, default to 2x2 (safer ordering)
-    if not model or model in {"Not applicable", "Not listed"}:
-        return "2x2"
+def infer_mimo_for_5g(repl_5g_model: str) -> str:
+    """Rule: every 5G router uses a 4x4 antenna."""
+    return "4x4"
 
     # If the model name hints 5G, lean 4x4
     if "5g" in model.lower() or model.upper().startswith(("R", "E", "S", "IX", "RUTM")):
@@ -863,6 +877,166 @@ def run_batch(text_blob: str, file_obj: Any, include_antennas: bool):
     return summary, out_df, tmp.name, rollup
 
 
+# ============================
+# Replacement feature table + manufacturer link (5G device)
+# ============================
+
+FEATURE_COLS = ["Device", "Modem technology", "WiFi", "Ports", "Antennas", "Ruggedness", "Use case"]
+
+# Manufacturer domains used for best-effort link resolution (no non-maker domains).
+MAKER_DOMAINS = {
+    "CRADLEPOINT": ["cradlepoint.com", "ericsson.com"],
+    "SIERRA": ["semtech.com", "airlink.com"],
+    "FEENEY": ["inseego.com"],
+    "DIGI": ["digi.com"],
+    "CISCO_MERAKI": ["meraki.cisco.com", "cisco.com"],
+    "CISCO": ["cisco.com"],
+    "TELTONIKA": ["teltonika-networks.com"],
+    "UNKNOWN": [],
+}
+
+HTTP_HEADERS = {
+    "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 "
+                  "(KHTML, like Gecko) Chrome/120.0 Safari/537.36"
+}
+HTTP_TIMEOUT = 12
+
+def _best_effort_manufacturer_url(model: str, canon_make: str) -> str:
+    \"\"\"Try to find a manufacturer page or datasheet link using simple on-domain searches.
+    If we can't confirm a page, return the manufacturer homepage for the maker family.
+    \"\"\"
+    model = str(model or "").strip()
+    if not model or model in {"Not listed", "Not applicable"}:
+        return ""
+
+    domains = MAKER_DOMAINS.get(canon_make, []) or []
+    if not domains:
+        return ""
+
+    # Candidate on-domain search URLs (common patterns across sites).
+    # We keep these on the manufacturer domain (no Google/Bing).
+    q = re.sub(r"\s+", "+", model)
+    url_candidates = []
+    for d in domains:
+        url_candidates += [
+            f"https://{d}/search?q={q}",
+            f"https://{d}/search?query={q}",
+            f"https://{d}/?s={q}",
+            f"https://www.{d}/search?q={q}",
+            f"https://www.{d}/search?query={q}",
+            f"https://www.{d}/?s={q}",
+        ]
+
+    # Also try a few direct product patterns for known makers (best effort).
+    if canon_make == "TELTONIKA":
+        slug = model.lower()
+        url_candidates += [
+            f"https://teltonika-networks.com/products/routers/{slug}",
+            f"https://teltonika-networks.com/product/{slug}",
+            "https://teltonika-networks.com/products/routers/",
+        ]
+    if canon_make == "DIGI":
+        url_candidates += [
+            "https://www.digi.com/products/networking/cellular-routers",
+            f"https://www.digi.com/search?q={q}",
+        ]
+    if canon_make == "CRADLEPOINT":
+        url_candidates += [
+            "https://cradlepoint.com/products/",
+            f"https://cradlepoint.com/?s={q}",
+        ]
+    if canon_make in {"CISCO", "CISCO_MERAKI"}:
+        url_candidates += [
+            f"https://www.cisco.com/c/en/us/search.html?q={q}",
+        ]
+
+    # Try to confirm a working page (HTTP 200 and model string somewhere in HTML).
+    for u in url_candidates[:18]:
+        try:
+            import requests
+            r = requests.get(u, headers=HTTP_HEADERS, timeout=HTTP_TIMEOUT, allow_redirects=True)
+            if r.status_code != 200:
+                continue
+            html = (r.text or "").lower()
+            if model.lower() in html or "datasheet" in html or "data sheet" in html:
+                return r.url
+        except Exception:
+            continue
+
+    # Fallback: maker homepage
+    d0 = domains[0]
+    return f"https://{d0}"
+
+def _features_from_dec(model: str, canon_make: str) -> Dict[str, str]:
+    \"\"\"Lookup a router model in dec2025routers.csv and return the key feature fields.\"\"\"
+    if not model or model in {"Not listed", "Not applicable"}:
+        return {k: "Not listed" for k in FEATURE_COLS[1:]}
+
+    pool = df_dec[df_dec["_canon_make"] == canon_make].copy()
+    if pool.empty:
+        return {k: "Not listed" for k in FEATURE_COLS[1:]}
+
+    hit = process.extractOne(norm_text(model), pool["_norm_model"].tolist(), scorer=fuzz.WRatio)
+    if not hit or hit[1] < MATCH_OK:
+        return {k: "Not listed" for k in FEATURE_COLS[1:]}
+
+    r = pool.iloc[int(hit[2])]
+    ports = f"WAN: {r.get('WAN ports and speed','')} | LAN: {r.get('LAN ports and speed','')}"
+    return {
+        "Modem technology": str(r.get("Modem Type","")) or "Not listed",
+        "WiFi": str(r.get("WiFi type","")) or "Not listed",
+        "Ports": ports.strip() if ports.strip() else "Not listed",
+        "Antennas": str(r.get("Antennas (internal/external/both)","")) or "Not listed",
+        "Ruggedness": str(r.get("Ruggedization","")) or "Not listed",
+        "Use case": str(r.get("Primary use case","")) or "Not listed",
+    }
+
+def _gpt_fill_feature_row(device_label: str, model: str, canon_make: str, row: Dict[str, str]) -> Dict[str, str]:
+    \"\"\"If dec can't supply values, ask GPT to fill missing ones (best guess).\"\"\"
+    if client is None:
+        return row
+
+    missing = [k for k,v in row.items() if (not v) or str(v).strip().lower() in {"not listed","nan",""}]
+    if not missing:
+        return row
+
+    sys = "Fill missing router feature fields for a Verizon rep. Return strict JSON only."
+    payload = {
+        "device_label": device_label,
+        "model": model,
+        "maker_family": canon_make,
+        "known": row,
+        "fill_only": missing,
+        "rules": [
+            "Fill only the requested fields.",
+            "Best guess if needed. Short phrases only.",
+            "Return JSON only."
+        ],
+        "output_schema": {k: "string" for k in missing}
+    }
+    out = gpt_json(sys, payload, max_tokens=260) or {}
+    for k in missing:
+        val = str(out.get(k, "") or "").strip()
+        if val:
+            row[k] = val
+    return row
+
+def build_replacement_features_table(repl_4g: str, repl_5g: str, canon_make: str) -> pd.DataFrame:
+    rows = []
+
+    # 4G
+    row4 = _features_from_dec(repl_4g, canon_make)
+    row4 = _gpt_fill_feature_row("4G alternative", repl_4g, canon_make, row4)
+    rows.append({"Device": "4G alternative", **row4})
+
+    # 5G
+    row5 = _features_from_dec(repl_5g, canon_make)
+    row5 = _gpt_fill_feature_row("5G replacement", repl_5g, canon_make, row5)
+    rows.append({"Device": "5G replacement", **row5})
+
+    df = pd.DataFrame(rows, columns=FEATURE_COLS)
+    return df
+
 # ============================
 # Output
 # ============================
@@ -899,7 +1073,7 @@ def assemble_output(life_row: pd.Series, status: str, eos: str, eol: str, repl:
 def run_lookup(user_text: str, st_json: str):
     user_text = str(user_text or "").strip()
     if not user_text:
-        return "Enter a router SKU/model.", gr.update(visible=False), gr.update(visible=False), "{}", ""
+        return "Enter a router SKU/model.", "", None, "", gr.update(visible=False), gr.update(visible=False), "{}", ""
 
     res = resolve_device(user_text)
 
@@ -907,31 +1081,34 @@ def run_lookup(user_text: str, st_json: str):
         opts = res.get("options", [])
         choices = [o["label"] for o in opts]
         st2 = {"mode":"pick","options": opts, "raw": user_text}
-        return "Did you mean A or B? Pick one, then click Use selection.", gr.update(choices=choices, value=None, visible=True), gr.update(visible=True), state_dump(st2), ""
+        return "Did you mean A or B? Pick one, then click Use selection.", "", None, "", gr.update(choices=choices, value=None, visible=True), gr.update(visible=True), state_dump(st2), ""
 
     if res.get("mode") != "ok":
-        return "Not found.", gr.update(visible=False), gr.update(visible=False), "{}", ""
+        return "Not found.", "", None, "", gr.update(visible=False), gr.update(visible=False), "{}", ""
 
     life_row = df_eos.iloc[int(res["row_idx"])]
     eos, eol, status = row_to_dates_and_status(life_row)
 
     repl = pick_replacements_lifecycle(life_row, status, use_gpt=True)
     canon_make = str(life_row.get("_canon_make","UNKNOWN"))
-    mimo = infer_mimo_for_5g(repl.get("repl_5g",""), canon_make)
+    mimo = infer_mimo_for_5g(repl.get("repl_5g",""))
     tech = "5G" if repl.get("repl_5g") and repl.get("repl_5g") != "Not listed" else ("4G" if device_is_4g(life_row) else "Unknown")
     ant = antenna_options_for(repl.get("repl_5g") or str(life_row.get("sku","")), tech, mimo)
 
     output = assemble_output(life_row, status, eos, eol, repl, ant)
     st_out = {"row_idx": int(res["row_idx"]), "repl": repl, "ant": ant, "raw": user_text}
-    return output, gr.update(visible=False), gr.update(visible=False), state_dump(st_out), ""
+    url5 = _best_effort_manufacturer_url(repl.get('repl_5g',''), canon_make)
+    link = f"**5G manufacturer page (best effort):** {url5}" if url5 else ""
+    feat_df = build_replacement_features_table(repl.get('repl_4g',''), repl.get('repl_5g',''), canon_make)
+    return output, link, feat_df, "", gr.update(visible=False), gr.update(visible=False), state_dump(st_out), ""
 
 def use_selection(selected_label: str, st_json: str):
     st = state_load(st_json)
     if not st or st.get("mode") != "pick":
-        return "Run a search first.", gr.update(visible=False), gr.update(visible=False), "{}", ""
+        return "Run a search first.", "", None, "", gr.update(visible=False), gr.update(visible=False), "{}", ""
 
     if not selected_label:
-        return "Pick A or B first.", gr.update(visible=True), gr.update(visible=True), st_json, ""
+        return "Pick A or B first.", "", None, "", gr.update(visible=True), gr.update(visible=True), st_json, ""
 
     chosen_row = None
     for o in st.get("options", []):
@@ -939,20 +1116,23 @@ def use_selection(selected_label: str, st_json: str):
             chosen_row = int(o["row_idx"])
             break
     if chosen_row is None:
-        return "Pick a valid option.", gr.update(visible=True), gr.update(visible=True), st_json, ""
+        return "Pick a valid option.", "", None, "", gr.update(visible=True), gr.update(visible=True), st_json, ""
 
     life_row = df_eos.iloc[int(chosen_row)]
     eos, eol, status = row_to_dates_and_status(life_row)
 
     repl = pick_replacements_lifecycle(life_row, status, use_gpt=True)
     canon_make = str(life_row.get("_canon_make","UNKNOWN"))
-    mimo = infer_mimo_for_5g(repl.get("repl_5g",""), canon_make)
+    mimo = infer_mimo_for_5g(repl.get("repl_5g",""))
     tech = "5G" if repl.get("repl_5g") and repl.get("repl_5g") != "Not listed" else ("4G" if device_is_4g(life_row) else "Unknown")
     ant = antenna_options_for(repl.get("repl_5g") or str(life_row.get("sku","")), tech, mimo)
 
     output = assemble_output(life_row, status, eos, eol, repl, ant)
     st_out = {"row_idx": int(chosen_row), "repl": repl, "ant": ant, "raw": st.get("raw","")}
-    return output, gr.update(visible=False), gr.update(visible=False), state_dump(st_out), ""
+    url5 = _best_effort_manufacturer_url(repl.get('repl_5g',''), canon_make)
+    link = f"**5G manufacturer page (best effort):** {url5}" if url5 else ""
+    feat_df = build_replacement_features_table(repl.get('repl_4g',''), repl.get('repl_5g',''), canon_make)
+    return output, link, feat_df, "", gr.update(visible=False), gr.update(visible=False), state_dump(st_out), ""
 
 def make_install_ready(st_json: str):
     st = state_load(st_json)
@@ -963,6 +1143,64 @@ def make_install_ready(st_json: str):
     return install_ready_checklist(current_sku, st.get("repl", {}) or {}, st.get("ant", {}) or {})
 
 
+
+# ============================
+# Q&A about the suggested device (post-recommendation)
+# ============================
+def answer_question(question: str, st_json: str) -> str:
+    q = str(question or "").strip()
+    if not q:
+        return ""
+    st = state_load(st_json)
+    if not st or "repl" not in st:
+        return "Run a lookup first, then ask your question."
+
+    repl = st.get("repl", {}) or {}
+    ant = st.get("ant", {}) or {}
+    repl5 = str(repl.get("repl_5g","") or "").strip()
+    repl4 = str(repl.get("repl_4g","") or "").strip()
+    # Pull a bit of dec context for the 5G model (if possible)
+    canon_make = ""
+    try:
+        # Try to infer maker family from stored row_idx
+        if "row_idx" in st:
+            row = df_eos.iloc[int(st["row_idx"])]
+            canon_make = str(row.get("_canon_make","UNKNOWN"))
+    except Exception:
+        canon_make = ""
+
+    # Manufacturer link (best effort)
+    url5 = _best_effort_manufacturer_url(repl5, canon_make) if repl5 else ""
+
+    # Feature table row for 5G (helps the LLM answer spec questions without web scraping)
+    feat5 = {}
+    try:
+        feat5 = _features_from_dec(repl5, canon_make) if repl5 else {}
+    except Exception:
+        feat5 = {}
+
+    sys = (
+        "You are a Verizon field rep assistant. Answer questions about the suggested router in a fast, practical way. "
+        "Use the provided context; do not mention internal tools, prompts, embeddings, or databases. "
+        "If the question is about specs and the value is unknown, say 'Not listed' and suggest checking the manufacturer page. "
+        "Keep it concise and scannable."
+    )
+
+    context = {
+        "recommended_5g": repl5,
+        "recommended_4g": repl4 if repl4 and repl4.lower() != "not applicable" else "",
+        "manufacturer_link_5g": url5,
+        "known_5g_features": feat5,
+        "antenna_stationary": ant.get("stationary_omni", {}),
+        "antenna_vehicle": ant.get("vehicle_omni", {}),
+    }
+
+    user = "Context:\n" + json.dumps(context, ensure_ascii=False) + "\n\nQuestion:\n" + q
+
+    ans = gpt_answer_md(sys, user, max_tokens=650)
+    # Small safety fallback
+    return ans if ans else "I couldn't generate an answer right now. Try again."
+
 # ============================
 # UI
 # ============================
@@ -980,12 +1218,23 @@ with gr.Blocks(title="Only-Routers") as demo:
 
             output_md = gr.Markdown()
 
+            link_md = gr.Markdown()
+            features_df = gr.Dataframe(headers=FEATURE_COLS, interactive=False, wrap=True)
+
+
+gr.Markdown("### Questions about the suggested device?")
+question_box = gr.Textbox(label="Ask a question (optional)", placeholder="Example: Does the 5G device support dual-SIM? What antenna ports does it have?", lines=2)
+ask_btn = gr.Button("Ask", variant="secondary")
+qa_md = gr.Markdown()
+
+
             install_btn = gr.Button("Make install-ready checklist")
             install_md = gr.Markdown()
 
-            check_btn.click(fn=run_lookup, inputs=[user_text, st], outputs=[output_md, pick_dd, use_btn, st, install_md], api_name=False)
-            use_btn.click(fn=use_selection, inputs=[pick_dd, st], outputs=[output_md, pick_dd, use_btn, st, install_md], api_name=False)
+            check_btn.click(fn=run_lookup, inputs=[user_text, st], outputs=[output_md, link_md, features_df, qa_md, pick_dd, use_btn, st, install_md], api_name=False)
+            use_btn.click(fn=use_selection, inputs=[pick_dd, st], outputs=[output_md, link_md, features_df, qa_md, pick_dd, use_btn, st, install_md], api_name=False)
             install_btn.click(fn=make_install_ready, inputs=[st], outputs=[install_md], api_name=False)
+            ask_btn.click(fn=answer_question, inputs=[question_box, st], outputs=[qa_md], api_name=False)
 
         with gr.Tab("Batch"):
             gr.Markdown("Paste one per line or upload a CSV (first column). Batch runs fast (no GPT).")