app_old.py

import os
import re
import json
import math
import hashlib
from dataclasses import dataclass
from datetime import datetime, date
from typing import Dict, List, Optional, Tuple, Any

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

CACHE_DIR = os.path.join(os.getcwd(), ".onlyrouters_cache")
os.makedirs(CACHE_DIR, exist_ok=True)


# ============================
# 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


# ============================
# Utilities
# ============================
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 = 700) -> 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 files (must exist in repo)
# ============================
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()

# Region filter: keep USA / North America / blank / not specified
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)

# Optional "Device Type"
device_type_col = None
for c in df_eos.columns:
    if norm_text(c) == "device type":
        device_type_col = c
        break

# Maker mapping (expanded — adds 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"},
}
DISPLAY_MAKER = {
    "CRADLEPOINT": "Cradlepoint",
    "SIERRA": "Sierra Wireless",
    "FEENEY": "Feeney Wireless",
    "DIGI": "Digi",
    "CISCO_MERAKI": "Cisco Meraki",
    "CISCO": "Cisco",
    "TELTONIKA": "Teltonika",
    "UNKNOWN": "Unknown",
}

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)

    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))

    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)

    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(parsed: ParsedDate) -> str:
    if parsed.kind == "missing":
        return "Not listed"
    if parsed.kind == "bad":
        return parsed.raw or "Not listed"
    return parsed.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(life_row: pd.Series) -> Tuple[str, str, str]:
    eos = parse_date_field(life_row.get("end_of_sale"))
    eol = parse_date_field(life_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 (exact SKU -> GPT A/B)
# ============================
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 candidate 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=300)

def resolve_device(user_text: str) -> Dict[str, Any]:
    q = norm_text(user_text)
    exact_idxs = df_eos.index[df_eos["_norm_sku"] == q].tolist()
    if len(exact_idxs) == 1:
        return {"mode":"ok","row_idx": int(exact_idxs[0])}
    if len(exact_idxs) > 1:
        opts = [{"row_idx": int(i), "label": EOS_LABELS[int(i)]} for i in exact_idxs[: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}

    # fallback
    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 is source of truth
# Fix: always show 4G alternative if lifecycle suggests it (even if Active)
# ============================
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:
        # Teltonika family
        m = re.search(r"\bRUT[A-Z]?\d{2,4}\b", cand.upper())
        if m:
            return m.group(0).upper()
        # Digi IX-series
        m = re.search(r"\bIX\d{2}\b", cand, flags=re.IGNORECASE)
        if m:
            return m.group(0).upper()
        # Cradlepoint R/E/S
        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()
        # Generic model token
        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(life_row: pd.Series) -> bool:
    t = norm_text(life_row.get("description","")) + " " + norm_text(life_row.get("notes",""))
    return (("lte" in t or "4g" in t) and ("5g" not in t and "nr" not in t))

def _candidate_5g_models_from_lifecycle(manufacturer: str) -> List[str]:
    # Pool within same manufacturer text (not just canon) to support Teltonika etc
    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(life_row: pd.Series, status: str) -> Dict[str, Any]:
    canon = str(life_row.get("_canon_make","UNKNOWN"))
    manufacturer = str(life_row.get("manufacturer","") or "")

    is_4g_device = _device_is_4g(life_row)
    needs_4g_repl = is_4g_device and (status in {"End of Sale","End of Life"})
    want_5g = is_4g_device or (status in {"End of Sale","End of Life"})

    # 4G alternative: ALWAYS if suggested_replacement exists for 4G devices
    repl_4g = "Not applicable"
    if is_4g_device:
        repl_4g = _extract_model_token(_safe_str(life_row.get("suggested_replacement","")))
        if not repl_4g:
            cand4 = _candidate_4g_models_from_lifecycle(manufacturer)
            repl_4g = _gpt_pick_from_candidates(life_row, cand4, "4G alternative") or (cand4[0] if cand4 else "")
        if not repl_4g:
            repl_4g = "Not applicable"

    # 5G replacement: ALWAYS when want_5g is true
    repl_5g = "Not applicable"
    if want_5g:
        repl_5g = _extract_model_token(_safe_str(life_row.get("advanced_5g_option","")))
        if not repl_5g:
            cand5 = _candidate_5g_models_from_lifecycle(manufacturer)
            repl_5g = _gpt_pick_from_candidates(life_row, cand5, "5G replacement/upgrade") or (cand5[0] if cand5 else "")
        if not repl_5g:
            # last resort: dec catalog fallback
            repl_5g = _fallback_5g_from_dec(canon)

    if repl_5g.lower() == "nan":
        repl_5g = ""

    return {
        "repl_4g": repl_4g,
        "repl_5g": repl_5g,
        "why": "Lifecycle replacements (GPT fallback when missing).",
        "sources": ["lifecycle_csv"] + (["gpt"] if client else []) + (["dec_fallback"] if (want_5g and not repl_5g) else []),
    }


# ============================
# Antennas (Parsec-only; family name extraction)
# ============================
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_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

    # fallback near SKU line
    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 = 10) -> 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 = []
    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),
            })
    return out

def antenna_options_for(router_model: str, tech: str, mimo: str) -> Dict[str, Any]:
    q_stationary = f"{router_model} {tech} {mimo} omni stationary outdoor Parsec"
    q_vehicle = f"{router_model} {tech} {mimo} omni vehicle mobile Parsec"
    cand_stationary = parsec_retrieve(q_stationary, top_k=10)
    cand_vehicle = parsec_retrieve(q_vehicle, top_k=10)

    # deterministic fallback if no GPT
    s = cand_stationary[0] if cand_stationary else {"name":"Parsec antenna","part_number":"","description":""}
    v = cand_vehicle[0] if cand_vehicle else {"name":"Parsec antenna","part_number":"","description":""}
    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"]}


# ============================
# Feature table + GPT fill for missing fields
# ============================
FEATURE_COLS = ["Name","Modem technology","WiFi","Ports","Antennas","Ruggedness","Use case"]

def dec_features_by_model(model: str, canon_make: str) -> Dict[str, str]:
    if not model or model in {"Not applicable","Not listed"}:
        return {k:"Not listed" for k in FEATURE_COLS}
    pool = df_dec[df_dec["_canon_make"] == canon_make].copy()
    if pool.empty:
        return {k:"Not listed" for k in FEATURE_COLS}
    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}
    r = pool.iloc[int(hit[2])]
    ports = f"WAN: {r.get('WAN ports and speed','')} | LAN: {r.get('LAN ports and speed','')}"
    return {
        "Name": str(r.get("Model","")),
        "Modem technology": str(r.get("Modem Type","")),
        "WiFi": str(r.get("WiFi type","")),
        "Ports": ports,
        "Antennas": str(r.get("Antennas (internal/external/both)","")),
        "Ruggedness": str(r.get("Ruggedization","")),
        "Use case": str(r.get("Primary use case","")),
    }

def gpt_fill_features(device_label: str, feats: Dict[str,str], context: str) -> Dict[str,str]:
    missing = [k for k,v in feats.items() if (not v) or v.strip().lower() in {"not listed","nan"}]
    if client is None or not missing:
        return feats
    sys = "Fill missing router feature fields. Return strict JSON only."
    payload = {
        "device": device_label,
        "known": feats,
        "context": context[:2000],
        "fill_only": missing,
        "rules": ["Fill only requested fields. Best guess if needed. Return JSON only."],
        "output_schema": {k:"string" for k in missing}
    }
    out = gpt_json(sys, payload, max_tokens=350) or {}
    for k in missing:
        v = str(out.get(k,"") or "").strip()
        if v:
            feats[k] = v
    return feats

def current_features_guess(life_row: pd.Series) -> Dict[str,str]:
    sku = str(life_row.get("sku","") or "").strip()
    desc = str(life_row.get("description","") or "").strip()
    notes = str(life_row.get("notes","") or "").strip()
    base = {
        "Name": sku,
        "Modem technology": "4G" if _device_is_4g(life_row) else ("5G" if ("5g" in (desc+notes).lower() or "nr" in (desc+notes).lower()) else "Not listed"),
        "WiFi": "Not listed",
        "Ports": "Not listed",
        "Antennas": "Not listed",
        "Ruggedness": "Not listed",
        "Use case": "Not listed",
    }
    return gpt_fill_features("Current device", base, f"{desc}\n{notes}")

def build_features_table(cur: Dict[str,str], r4: Dict[str,str], r5: Dict[str,str]) -> str:
    cols = ["Device", "Modem technology", "WiFi", "Ports", "Antennas", "Ruggedness", "Use case"]
    header = "| " + " | ".join(cols) + " |"
    sep = "| " + " | ".join(["---"]*len(cols)) + " |"
    def row(name: str, feats: Dict[str,str]) -> str:
        return "| " + " | ".join([
            name,
            feats.get("Modem technology","Not listed"),
            feats.get("WiFi","Not listed"),
            feats.get("Ports","Not listed"),
            feats.get("Antennas","Not listed"),
            feats.get("Ruggedness","Not listed"),
            feats.get("Use case","Not listed"),
        ]) + " |"
    return "\n".join([header, sep, row("Current", cur), row("4G alternative", r4), row("5G replacement", r5)])


# ============================
# Output + Gradio
# ============================
def assemble_output(life_row: pd.Series, status: str, eos: str, eol: str, repl: Dict[str,Any], ant: Dict[str,Any]) -> str:
    canon_make = str(life_row.get("_canon_make","UNKNOWN"))
    current_name = f"{life_row.get('sku','')} — {life_row.get('description','')}".strip(" —")

    st = ant.get("stationary_omni", {})
    vh = ant.get("vehicle_omni", {})

    cur_feats = current_features_guess(life_row)
    r4_feats = dec_features_by_model(repl.get("repl_4g",""), canon_make)
    r5_feats = dec_features_by_model(repl.get("repl_5g",""), canon_make)

    # If dec doesn't know the model, ask GPT to fill missing cells (best guess)
    if client is not None:
        r4_feats = gpt_fill_features("4G alternative", r4_feats, f"Model: {repl.get('repl_4g','')}\nMake: {canon_make}")
        r5_feats = gpt_fill_features("5G replacement", r5_feats, f"Model: {repl.get('repl_5g','')}\nMake: {canon_make}")

    table_md = build_features_table(cur_feats, r4_feats, r5_feats)

    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):")
    lines.append(f"   - Stationary (Omni): **{st.get('name','')}** (Part #: {st.get('part_number','')}) — {st.get('description','')} — MIMO: {st.get('mimo','')} — {st.get('why','')}")
    lines.append(f"   - Vehicle (Omni): **{vh.get('name','')}** (Part #: {vh.get('part_number','')}) — {vh.get('description','')} — MIMO: {vh.get('mimo','')} — {vh.get('why','')}")
    lines.append("8. Recommended features table:")
    lines.append(table_md)
    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)")
    lines.append("- dec2025routers.csv (features)")
    return "\n".join(lines)

def run_lookup(user_text: str, st: Dict[str,Any]):
    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}
        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), 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)

    tech = "5G" if repl.get("repl_5g") and repl.get("repl_5g") not in {"Not applicable","Not listed"} else ("4G" if _device_is_4g(life_row) else "Unknown")
    mimo_guess = "4x4" if tech == "5G" else "2x2"
    ant = antenna_options_for(router_model=repl.get("repl_5g") or str(life_row.get("sku","")), tech=tech, mimo=mimo_guess)

    return assemble_output(life_row, status, eos, eol, repl, ant), gr.update(visible=False), gr.update(visible=False), {}

def use_selection(selected_label: str, st: Dict[str,Any]):
    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

    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

    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)
    tech = "5G" if repl.get("repl_5g") and repl.get("repl_5g") not in {"Not applicable","Not listed"} else ("4G" if _device_is_4g(life_row) else "Unknown")
    mimo_guess = "4x4" if tech == "5G" else "2x2"
    ant = antenna_options_for(router_model=repl.get("repl_5g") or str(life_row.get("sku","")), tech=tech, mimo=mimo_guess)

    return assemble_output(life_row, status, eos, eol, repl, ant), gr.update(visible=False), gr.update(visible=False), {}

with gr.Blocks(title="Only-Routers") as demo:
    gr.Markdown("## Only-Routers\nEnter a router SKU/model. If ambiguous, you’ll get A/B choices.")
    user_text = gr.Textbox(label="Router SKU or model", placeholder="Examples: IBR650B, AER1600, ES450, WR21, RUT240", lines=1)
    st = gr.State({})

    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()

    check_btn.click(fn=run_lookup, inputs=[user_text, st], outputs=[output_md, pick_dd, use_btn, st])
    use_btn.click(fn=use_selection, inputs=[pick_dd, st], outputs=[output_md, pick_dd, use_btn, st])

demo.launch()