Update app.py

app.py

@@ -1,685 +1,320 @@
-# app.py
-# 🎁 GIfty+ — Smart Gift Recommender (original Spaces app + Hybrid Ranker v2)
-# Dataset default: Danielos100/Amazon_products_clean  (override via DATASET_ID env)
-# Retrieval: sentence-transformers/all-MiniLM-L12-v2 (cosine on budget-filtered subset)
-# DIY: FLAN-only (strict prompts + sanitizers)
-# Message: FLAN-based, לא גנרי — סטוכסטי עם ולידציה ואנטי-דופליקציה
-# UI: Examples-table למעלה (קליק-טואו-פיל), טופס פתוח ומסודר בשורות, בלי JSON
-
+# app.py  — GIftyPlus (lean)
 import os, re, json, hashlib, pathlib, random
 from typing import Dict, List, Tuple, Optional, Any
-
-import numpy as np
-import pandas as pd
-import gradio as gr
+import numpy as np, pandas as pd, gradio as gr, torch
 from datasets import load_dataset
-
 from sentence_transformers import SentenceTransformer
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import torch
-
-print(f"===== Application Startup at {pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')} =====")
 
-# --------------------- Config ---------------------
 TITLE = "# 🎁 GIftyPlus - Smart Gift Recommender\n*Top-3 catalog picks + 1 DIY gift + personalized message*"
-
-DATASET_ID    = os.getenv("DATASET_ID", "Danielos100/Amazon_products_clean")
+DATASET_ID = os.getenv("DATASET_ID", "Danielos100/Amazon_products_clean")
 DATASET_SPLIT = os.getenv("DATASET_SPLIT", "train")
-
 MAX_ROWS = int(os.getenv("MAX_ROWS", "12000"))
 EMBED_MODEL_ID = os.getenv("EMBED_MODEL_ID", "sentence-transformers/all-MiniLM-L12-v2")
 
-# ---- Writable cache dir (no /data requirement) ----
-def resolve_cache_dir() -> str:
-    candidates = []
-    if os.getenv("EMBED_CACHE_DIR"):
-        candidates.append(os.getenv("EMBED_CACHE_DIR"))
-    candidates += [os.path.join(os.getcwd(), ".gifty_cache"), "/tmp/.gifty_cache"]
-    for p in candidates:
-        try:
-            pathlib.Path(p).mkdir(parents=True, exist_ok=True)
-            test = pathlib.Path(p) / ".write_test"
-            with open(test, "w") as f: f.write("ok")
-            test.unlink(missing_ok=True)
-            return p
-        except Exception:
-            continue
+def resolve_cache_dir():
+    for p in [os.getenv("EMBED_CACHE_DIR"), os.path.join(os.getcwd(), ".gifty_cache"), "/tmp/.gifty_cache"]:
+        if not p: continue
+        pathlib.Path(p).mkdir(parents=True, exist_ok=True)
+        with open(os.path.join(p, ".write_test"), "w") as f: f.write("ok")
+        pathlib.Path(os.path.join(p, ".write_test")).unlink(missing_ok=True)
+        return p
     return os.getcwd()
-
 EMBED_CACHE_DIR = resolve_cache_dir()
-print(f"[CACHE] Using EMBED_CACHE_DIR={EMBED_CACHE_DIR}")
 
-# ===== UI options =====
-INTEREST_OPTIONS = [
-    "Sports","Travel","Cooking","Technology","Music","Art","Reading","Gardening","Fashion",
-    "Gaming","Photography","Hiking","Movies","Crafts","Pets","Wellness","Collecting","Food",
-    "Home decor","Science"
-]
-OCCASION_UI = [
-    "Birthday","Wedding / Engagement","Anniversary","Graduation","New baby","Housewarming",
-    "Retirement","Holidays","Valentine’s Day","Promotion / New job","Get well soon"
-]
-OCCASION_CANON = {
-    "Birthday":"birthday","Wedding / Engagement":"wedding","Anniversary":"anniversary",
-    "Graduation":"graduation","New baby":"new_baby","Housewarming":"housewarming",
-    "Retirement":"retirement","Holidays":"holidays","Valentine’s Day":"valentines",
-    "Promotion / New job":"promotion","Get well soon":"get_well"
-}
-RECIPIENT_RELATIONSHIPS = [
-    "Family - Parent","Family - Sibling","Family - Child","Family - Other relative",
-    "Friend","Colleague","Boss","Romantic partner","Teacher / Mentor","Neighbor","Client / Business partner",
-]
-MESSAGE_TONES = [
-    "Formal","Casual","Funny","Heartfelt","Inspirational","Playful","Romantic","Appreciative","Encouraging",
-]
+INTEREST_OPTIONS = ["Sports","Travel","Cooking","Technology","Music","Art","Reading","Gardening","Fashion","Gaming","Photography","Hiking","Movies","Crafts","Pets","Wellness","Collecting","Food","Home decor","Science"]
+OCCASION_UI = ["Birthday","Wedding / Engagement","Anniversary","Graduation","New baby","Housewarming","Retirement","Holidays","Valentine’s Day","Promotion / New job","Get well soon"]
+OCCASION_CANON = {"Birthday":"birthday","Wedding / Engagement":"wedding","Anniversary":"anniversary","Graduation":"graduation","New baby":"new_baby","Housewarming":"housewarming","Retirement":"retirement","Holidays":"holidays","Valentine’s Day":"valentines","Promotion / New job":"promotion","Get well soon":"get_well"}
+RECIPIENT_RELATIONSHIPS = ["Family - Parent","Family - Sibling","Family - Child","Family - Other relative","Friend","Colleague","Boss","Romantic partner","Teacher / Mentor","Neighbor","Client / Business partner"]
+MESSAGE_TONES = ["Formal","Casual","Funny","Heartfelt","Inspirational","Playful","Romantic","Appreciative","Encouraging"]
 AGE_OPTIONS = {"any":"any","kid (3–12)":"kids","teen (13–17)":"teens","adult (18–64)":"adult","senior (65+)":"senior"}
 GENDER_OPTIONS = ["any","female","male","nonbinary"]
 
-# Query expansion by interest
-SYNONYMS = {
-    "sports":["fitness","outdoor","training","yoga","run"],
-    "travel":["luggage","passport","map","trip","vacation"],
-    "cooking":["kitchen","cookware","chef","baking"],
-    "technology":["electronics","gadgets","device","smart","computer"],
-    "music":["audio","headphones","earbuds","speaker","vinyl"],
-    "art":["painting","drawing","sketch","canvas"],
-    "reading":["book","novel","literature"],
-    "gardening":["plants","planter","seeds","garden","indoor"],
-    "fashion":["style","accessory","jewelry"],
-    "gaming":["board game","puzzle","video game","controller"],
-    "photography":["camera","lens","tripod","film"],
-    "hiking":["outdoor","camping","backpack","trek"],
-    "movies":["film","cinema","blu-ray","poster"],
-    "crafts":["diy","handmade","kit","knitting"],
-    "pets":["dog","cat","pet"],
-    "wellness":["relaxation","spa","aromatherapy","self-care"],
-    "collecting":["display","collector","limited edition"],
-    "food":["gourmet","snack","treats","chocolate"],
-    "home decor":["home","decor","wall art","candle"],
-    "science":["lab","experiment","STEM","microscope"],
-}
-REL_TO_TOKENS = {
-    "Family - Parent": ["parent","family"],
-    "Family - Sibling": ["sibling","family"],
-    "Family - Child": ["kids","play","family"],
-    "Family - Other relative": ["family","relative"],
-    "Friend": ["friendly"],
-    "Colleague": ["office","work","professional"],
-    "Boss": ["executive","professional","premium"],
-    "Romantic partner": ["romantic","couple"],
-    "Teacher / Mentor": ["teacher","mentor","thank_you"],
-    "Neighbor": ["neighbor","housewarming"],
-    "Client / Business partner": ["professional","thank_you","premium"],
-}
-
-# --------------------- Data loading & schema ---------------------
-_CURRENCY_RE = re.compile(r"[^\d.,\-]+")
-_NUM_RE = re.compile(r"(\d+(?:[.,]\d+)?)")
-_RANGE_SEP = re.compile(r"\s*(?:-|–|—|to)\s*")
+SYNONYMS = {"sports":["fitness","outdoor","training","yoga","run"],"travel":["luggage","passport","map","trip","vacation"],"cooking":["kitchen","cookware","chef","baking"],"technology":["electronics","gadgets","device","smart","computer"],"music":["audio","headphones","earbuds","speaker","vinyl"],"art":["painting","drawing","sketch","canvas"],"reading":["book","novel","literature"],"gardening":["plants","planter","seeds","garden","indoor"],"fashion":["style","accessory","jewelry"],"gaming":["board game","puzzle","video game","controller"],"photography":["camera","lens","tripod","film"],"hiking":["outdoor","camping","backpack","trek"],"movies":["film","cinema","blu-ray","poster"],"crafts":["diy","handmade","kit","knitting"],"pets":["dog","cat","pet"],"wellness":["relaxation","spa","aromatherapy","self-care"],"collecting":["display","collector","limited edition"],"food":["gourmet","snack","treats","chocolate"],"home decor":["home","decor","wall art","candle"],"science":["lab","experiment","STEM","microscope"]}
+REL_TO_TOKENS = {"Family - Parent":["parent","family"],"Family - Sibling":["sibling","family"],"Family - Child":["kids","play","family"],"Family - Other relative":["family","relative"],"Friend":["friendly"],"Colleague":["office","work","professional"],"Boss":["executive","professional","premium"],"Romantic partner":["romantic","couple"],"Teacher / Mentor":["teacher","mentor","thank_you"],"Neighbor":["neighbor","housewarming"],"Client / Business partner":["professional","thank_you","premium"]}
 
-def _to_price_usd(x) -> float:
+_CURRENCY_RE = re.compile(r"[^\d.,\-]+"); _NUM_RE = re.compile(r"(\d+(?:[.,]\d+)?)"); _RANGE_SEP = re.compile(r"\s*(?:-|–|—|to)\s*")
+def _to_price_usd(x):
     if pd.isna(x): return np.nan
     s = str(x).strip().lower()
-    if _RANGE_SEP.search(s):
-        left = _RANGE_SEP.split(s)[0]
-        s = left
-    s = _CURRENCY_RE.sub(" ", s)
-    m = _NUM_RE.search(s.replace(",", "."))
-    try:
-        return float(m.group(1)) if m else np.nan
-    except Exception:
-        return np.nan
+    if _RANGE_SEP.search(s): s = _RANGE_SEP.split(s)[0]
+    s = _CURRENCY_RE.sub(" ", s); m = _NUM_RE.search(s.replace(",", "."))
+    return float(m.group(1)) if m else np.nan
 
-def _first_present(df: pd.DataFrame, candidates: List[str]) -> Optional[str]:
-    cols_lower = {c.lower(): c for c in df.columns}
-    for c in candidates:
+def _first_present(df, cands):
+    lower = {c.lower(): c for c in df.columns}
+    for c in cands:
         if c in df.columns: return c
-        if c.lower() in cols_lower: return cols_lower[c.lower()]
+        if c.lower() in lower: return lower[c.lower()]
     return None
 
-def _auto_price_col(df: pd.DataFrame) -> Optional[str]:
+def _auto_price_col(df):
     for c in df.columns:
         s = df[c]
-        if pd.api.types.is_numeric_dtype(s):
-            nonneg = s.dropna()
-            if not nonneg.empty and (nonneg.between(0.5, 10000)).mean() > 0.6:
-                return c
+        if pd.api.types.is_numeric_dtype(s) and not s.dropna().empty and (s.dropna().between(0.5, 10000)).mean() > .6: return c
     for c in df.columns:
-        sample = df[c].astype(str).head(200).str.lower().str.contains(r"\$|₪|eur|usd|£|€|\d")
-        if sample.mean() > 0.5:
-            return c
+        if df[c].astype(str).head(200).str.lower().str.contains(r"\$|₪|eur|usd|£|€|\d").mean() > .5: return c
     return None
 
-def map_amazon_to_schema(df_raw: pd.DataFrame) -> pd.DataFrame:
-    name_c = _first_present(df_raw, ["product name","title","name","product_title"])
-    desc_c = _first_present(df_raw, ["description","product_description","feature","about"])
-    cat_c  = _first_present(df_raw, ["category","categories","main_cat","product_category"])
-    price_c= _first_present(df_raw, ["selling price","price","current_price","list_price","price_amount","actual_price","price_usd"])
-    if price_c is None:
-        price_c = _auto_price_col(df_raw)
-    img_c  = _first_present(df_raw, ["image","image_url","imageurl","imUrl","img","img_url"])
-
-    out = pd.DataFrame({
-        "name": df_raw.get(name_c, pd.Series("", index=df_raw.index)),
-        "short_desc": df_raw.get(desc_c, pd.Series("", index=df_raw.index)),
-        "tags": df_raw.get(cat_c, pd.Series("", index=df_raw.index)),
-        "price_usd": df_raw.get(price_c, pd.Series(np.nan, index=df_raw.index)).map(_to_price_usd),
-        "image_url": df_raw.get(img_c, pd.Series("", index=df_raw.index)),
-    })
-    out["name"] = out["name"].astype(str).str.strip().str.slice(0, 160)
-    out["short_desc"] = out["short_desc"].astype(str).str.strip().str.slice(0, 600)
-    out["tags"] = out["tags"].astype(str).str.replace("|", ", ").str.lower()
-    return out
+def map_amazon_to_schema(raw: pd.DataFrame) -> pd.DataFrame:
+    name_c=_first_present(raw,["product name","title","name","product_title"]); desc_c=_first_present(raw,["description","product_description","feature","about"])
+    cat_c=_first_present(raw,["category","categories","main_cat","product_category"]); price_c=_first_present(raw,["selling price","price","current_price","list_price","price_amount","actual_price","price_usd"]) or _auto_price_col(raw)
+    img_c=_first_present(raw,["image","image_url","imageurl","imUrl","img","img_url"])
+    df=pd.DataFrame({"name":raw.get(name_c,""),"short_desc":raw.get(desc_c,""),"tags":raw.get(cat_c,""),"price_usd":raw.get(price_c,np.nan),"image_url":raw.get(img_c,"")})
+    df["price_usd"]=df["price_usd"].map(_to_price_usd); df["name"]=df["name"].astype(str).str.strip().str.slice(0,160)
+    df["short_desc"]=df["short_desc"].astype(str).str.strip().str.slice(0,600); df["tags"]=df["tags"].astype(str).str.replace("|",", ").str.lower()
+    return df
 
-def extract_top_cat(tags: str) -> str:
-    s = (tags or "").lower()
-    if "|" in s: return s.split("|", 1)[0].strip()
-    if ">" in s: return s.split(">", 1)[0].strip()
+def extract_top_cat(tags:str)->str:
+    s=(tags or "").lower()
+    for sep in ["|",">"]: 
+        if sep in s: return s.split(sep,1)[0].strip()
     return s.strip().split(",")[0] if s else ""
 
-def load_catalog() -> pd.DataFrame:
-    ds  = load_dataset(DATASET_ID, split=DATASET_SPLIT)
-    raw = ds.to_pandas()
-
-    df = map_amazon_to_schema(raw).drop_duplicates(subset=["name","short_desc"])
-    df = df[pd.notna(df["price_usd"])].copy()
-    df = df[(df["price_usd"] > 0) & (df["price_usd"] <= 500)].reset_index(drop=True)
-
-    if len(df) > MAX_ROWS:
-        df = df.sample(n=MAX_ROWS, random_state=42).reset_index(drop=True)
-
-    df["doc"] = (df["name"].fillna("") + " | " + df["tags"].fillna("") + " | " + df["short_desc"].fillna("")).str.strip()
-    df["top_cat"] = df["tags"].map(extract_top_cat)
-    df["blob"] = (df["name"].fillna("") + " " + df["tags"].fillna("") + " " + df["short_desc"].fillna("")).str.lower()
-
-    print(f"[DATA] dataset={DATASET_ID} split={DATASET_SPLIT} rows_final={len(df)}")
+def load_catalog()->pd.DataFrame:
+    df=map_amazon_to_schema(load_dataset(DATASET_ID, split=DATASET_SPLIT).to_pandas()).drop_duplicates(subset=["name","short_desc"])
+    df=df[pd.notna(df["price_usd"])]; df=df[(df["price_usd"]>0)&(df["price_usd"]<=500)].reset_index(drop=True)
+    if len(df)>MAX_ROWS: df=df.sample(n=MAX_ROWS,random_state=42).reset_index(drop=True)
+    df["doc"]=(df["name"].fillna("")+" | "+df["tags"].fillna("")+" | "+df["short_desc"].fillna("")).str.strip()
+    df["top_cat"]=df["tags"].map(extract_top_cat)
+    df["blob"]=(df["name"].fillna("")+" "+df["tags"].fillna("")+" "+df["short_desc"].fillna("")).str.lower()
     return df
+CATALOG=load_catalog()
 
-CATALOG = load_catalog()
-
-# --------------------- Embeddings (with dataset-aware cache) ---------------------
 class EmbeddingBank:
-    def __init__(self, docs: List[str], model_id: str, dataset_tag: str):
-        self.model_id = model_id
-        self.dataset_tag = dataset_tag
-        self.model = SentenceTransformer(model_id)
-        self.embs = self._load_or_build(docs)
-
-    def _cache_path(self, n_docs: int) -> str:
-        h = hashlib.md5((self.dataset_tag + "|" + self.model_id + f"|{n_docs}").encode()).hexdigest()[:10]
-        return os.path.join(EMBED_CACHE_DIR, f"emb_{h}.npy")
-
-    def _load_or_build(self, docs: List[str]) -> np.ndarray:
-        path = self._cache_path(len(docs))
-        if os.path.exists(path):
-            try:
-                embs = np.load(path, mmap_mode="r")
-                if embs.shape[0] == len(docs):
-                    print("[EMB] mmap-loaded from cache")
-                    return embs
-            except Exception:
-                pass
-        print("[EMB] Building embeddings…")
-        embs = self.model.encode(docs, convert_to_numpy=True, normalize_embeddings=True, show_progress_bar=True)
-        try:
-            np.save(self._cache_path(len(docs)), embs)
-            embs = np.load(self._cache_path(len(docs)), mmap_mode="r")
-            print(f"[EMB] Saved & mmap-loaded: {embs.shape}")
-        except Exception:
-            print("[EMB] Cache save failed; using RAM only")
-        return embs
+    def __init__(s, docs, model_id, dataset_tag):
+        s.model_id=model_id; s.dataset_tag=dataset_tag; s.model=SentenceTransformer(model_id); s.embs=s._load_or_build(docs)
+    def _cache_path(s,n): return os.path.join(EMBED_CACHE_DIR, f"emb_{hashlib.md5((s.dataset_tag+'|'+s.model_id+f'|{n}').encode()).hexdigest()[:10]}.npy")
+    def _load_or_build(s,docs):
+        p=s._cache_path(len(docs))
+        if os.path.exists(p):
+            embs=np.load(p,mmap_mode="r"); 
+            if embs.shape[0]==len(docs): return embs
+        embs=s.model.encode(docs, convert_to_numpy=True, normalize_embeddings=True, show_progress_bar=True)
+        np.save(p, embs); return np.load(p, mmap_mode="r")
+    def query_vec(s,text): return s.model.encode([text], convert_to_numpy=True, normalize_embeddings=True)[0]
+EMB=EmbeddingBank(CATALOG["doc"].tolist(), EMBED_MODEL_ID, DATASET_ID)
 
-    def query_vec(self, text: str) -> np.ndarray:
-        return self.model.encode([text], convert_to_numpy=True, normalize_embeddings=True)[0]
-
-EMB = EmbeddingBank(CATALOG["doc"].tolist(), EMBED_MODEL_ID, DATASET_ID)
-
-# ---- tokens per item for fast overlap (used by recommender bonuses) ----
 _tok_rx = re.compile(r"[a-z0-9][a-z0-9\-']*")
-def _tok_set(text: str) -> set:
-    return set(_tok_rx.findall(str(text).lower()))
 if "tok_set" not in CATALOG.columns:
-    CATALOG["tok_set"] = (
-        CATALOG["name"].fillna("") + " " +
-        CATALOG["tags"].fillna("") + " " +
-        CATALOG["short_desc"].fillna("")
-    ).map(_tok_set)
+    CATALOG["tok_set"]=(CATALOG["name"].fillna("")+" "+CATALOG["tags"].fillna("")+" "+CATALOG["short_desc"].fillna("")).map(lambda t:set(_tok_rx.findall(str(t).lower())))
 
-# ====================== Recommendations — Hybrid Ranker v2 ======================
 try:
     from sentence_transformers import CrossEncoder
-except Exception:
-    CrossEncoder = None
-
-RERANK_MODEL_ID = os.getenv("RERANK_MODEL_ID", "cross-encoder/ms-marco-MiniLM-L-6-v2")
-_CE_MODEL = None
-
+except:
+    CrossEncoder=None
+RERANK_MODEL_ID=os.getenv("RERANK_MODEL_ID","cross-encoder/ms-marco-MiniLM-L-6-v2")
+_CE_MODEL=None
 def _load_cross_encoder():
     global _CE_MODEL
-    if _CE_MODEL is not None:
-        return _CE_MODEL
-    if CrossEncoder is None:
-        return None
-    try:
-        _CE_MODEL = CrossEncoder(RERANK_MODEL_ID, device="cpu")
-        print(f"[RERANK] Loaded: {RERANK_MODEL_ID}")
-    except Exception as e:
-        print(f"[RERANK] Failed to load CE: {e}")
-        _CE_MODEL = None
+    if _CE_MODEL is None and CrossEncoder is not None:
+        _CE_MODEL=CrossEncoder(RERANK_MODEL_ID, device="cpu")
     return _CE_MODEL
 
-OCCASION_PRIORS = {
-    "valentines": [("jewelry",0.12),("chocolate",0.10),("candle",0.08),("romantic",0.08),("couple",0.08),("heart",0.06)],
-    "birthday":   [("fun",0.06),("game",0.06),("personalized",0.06),("gift set",0.05),("surprise",0.04)],
-    "anniversary":[("couple",0.10),("jewelry",0.10),("photo",0.08),("frame",0.06),("memory",0.06),("candle",0.06)],
-    "graduation": [("journal",0.10),("planner",0.08),("office",0.08),("coffee",0.06),("motivation",0.06)],
-    "housewarming":[("home",0.10),("kitchen",0.08),("decor",0.10),("candle",0.06),("serving",0.06)],
-    "new_baby":   [("baby",0.12),("nursery",0.10),("soft",0.06),("blanket",0.06)],
-    "retirement": [("relax",0.08),("hobby",0.08),("travel",0.06),("book",0.06)],
-    "holidays":   [("holiday",0.10),("winter",0.08),("chocolate",0.08),("cozy",0.06),("family",0.06)],
-    "promotion":  [("desk",0.10),("office",0.10),("premium",0.08),("organizer",0.06)],
-    "get_well":   [("cozy",0.10),("tea",0.08),("soothing",0.06),("care",0.06)],
-}
-
-def expand_with_synonyms(tokens: List[str]) -> List[str]:
-    out = []
+OCCASION_PRIORS={"valentines":[("jewelry",.12),("chocolate",.10),("candle",.08),("romantic",.08),("couple",.08),("heart",.06)],
+"birthday":[("fun",.06),("game",.06),("personalized",.06),("gift set",.05),("surprise",.04)],
+"anniversary":[("couple",.10),("jewelry",.10),("photo",.08),("frame",.06),("memory",.06),("candle",.06)],
+"graduation":[("journal",.10),("planner",.08),("office",.08),("coffee",.06),("motivation",.06)],
+"housewarming":[("home",.10),("kitchen",.08),("decor",.10),("candle",.06),("serving",.06)],
+"new_baby":[("baby",.12),("nursery",.10),("soft",.06),("blanket",.06)],
+"retirement":[("relax",.08),("hobby",.08),("travel",.06),("book",.06)],
+"holidays":[("holiday",.10),("winter",.08),("chocolate",.08),("cozy",.06),("family",.06)],
+"promotion":[("desk",.10),("office",.10),("premium",.08),("organizer",.06)],
+"get_well":[("cozy",.10),("tea",.08),("soothing",.06),("care",.06)]}
+
+def expand_with_synonyms(tokens: List[str])->List[str]:
+    out=[]; 
     for t in tokens:
-        t = t.strip().lower()
-        if not t: continue
-        out.append(t)
-        out.extend(SYNONYMS.get(t, []))
+        t=t.strip().lower()
+        if t: out+=[t]+SYNONYMS.get(t,[])
     return out
 
-def profile_to_query(profile: Dict) -> str:
-    inter = [i.lower() for i in profile.get("interests", []) if i]
-    expanded = expand_with_synonyms(inter) * 3
-    rel_tokens = REL_TO_TOKENS.get(profile.get("relationship","Friend"), [])
-    occ = OCCASION_CANON.get(profile.get("occ_ui","Birthday"), "birthday")
-
-    parts = []
-    if expanded:   parts.append(", ".join(expanded))
-    if rel_tokens: parts.append(", ".join(rel_tokens))
-    parts.append(occ)
-    tail = f"gift ideas for a {profile.get('relationship','Friend')} for {occ}; likes {', '.join(inter) or 'general'}"
-    return " | ".join([p for p in parts if p]) + " | " + tail
-
-def _gender_ok_mask(gender: str) -> np.ndarray:
-    g = (gender or "any").lower()
-    blob = CATALOG["blob"]
-    has_male   = blob.str.contains(r"\b(men|man's|mens|male|for men)\b",  regex=True, na=False)
-    has_female = blob.str.contains(r"\b(women|woman's|womens|female|for women|dress)\b", regex=True, na=False)
-    has_unisex = blob.str.contains(r"\bunisex|gender neutral\b", regex=True, na=False)
-    if g == "female":
-        return (~has_male | has_unisex).to_numpy()
-    if g == "male":
-        return (~has_female | has_unisex).to_numpy()
-    return np.ones(len(blob), dtype=bool)
-
-def _mask_by_age(age: str, blob: pd.Series) -> np.ndarray:
-    KIDS_RX = r"\b(?:kid|kids|child|children|toddler|baby|boys?|girls?|kid\'s|children\'s)\b"
-    TEEN_RX = r"\b(?:teen|teens|young adult|ya)\b"
-    is_kidsy = blob.str.contains(KIDS_RX, regex=True, na=False)
-    is_teen  = blob.str.contains(TEEN_RX,  regex=True, na=False)
-    if age in ("adult","senior"):
-        return (~is_kidsy).to_numpy()
-    if age == "teens":
-        return ((~is_kidsy) | is_teen).to_numpy()
-    if age == "kids":
-        return (is_kidsy | (~is_teen & is_kidsy)).to_numpy()
-    return np.ones(len(blob), dtype=bool)
-
-def _interest_bonus(profile: Dict, idx: np.ndarray) -> np.ndarray:
-    ints = [i.lower() for i in profile.get("interests", []) if i]
-    syns = [s for it in ints for s in SYNONYMS.get(it, [])]
-    vocab = set(ints + syns)
-    if not vocab or idx.size == 0:
-        return np.zeros(len(idx), dtype="float32")
-    counts = np.array([len(CATALOG["tok_set"].iat[i] & vocab) for i in idx], dtype="float32")
-    counts = np.clip(counts, 0, 6)
-    return 0.10 * counts
-
-def _occasion_bonus(idx: np.ndarray, occ_ui: str) -> np.ndarray:
-    slug = OCCASION_CANON.get(occ_ui or "Birthday", "birthday")
-    pri = OCCASION_PRIORS.get(slug, [])
-    if not pri or idx.size == 0:
-        return np.zeros(len(idx), dtype="float32")
-    bl = CATALOG["blob"].to_numpy()
-    out = np.zeros(len(idx), dtype="float32")
-    for j, i in enumerate(idx):
-        text = bl[i]
-        bonus = 0.0
-        for kw, w in pri:
-            if kw in text:
-                bonus += w
-        out[j] = min(bonus, 0.15)
+def profile_to_query(p:Dict)->str:
+    inter=[i.lower() for i in p.get("interests",[]) if i]; expanded=expand_with_synonyms(inter)*3
+    parts=[", ".join(expanded) if expanded else "", ", ".join(REL_TO_TOKENS.get(p.get("relationship","Friend"),[])), OCCASION_CANON.get(p.get("occ_ui","Birthday"),"birthday")]
+    tail=f"gift ideas for a {p.get('relationship','Friend')} for {parts[-1]}; likes {', '.join(inter) or 'general'}"
+    return " | ".join([x for x in parts if x])+" | "+tail
+
+def _gender_ok_mask(g:str)->np.ndarray:
+    g=(g or "any").lower(); bl=CATALOG["blob"]
+    has_m=bl.str.contains(r"\b(men|man's|mens|male|for men)\b",regex=True,na=False)
+    has_f=bl.str.contains(r"\b(women|woman's|womens|female|for women|dress)\b",regex=True,na=False)
+    has_u=bl.str.contains(r"\bunisex|gender neutral\b",regex=True,na=False)
+    if g=="female": return (~has_m | has_u).to_numpy()
+    if g=="male": return (~has_f | has_u).to_numpy()
+    return np.ones(len(bl),bool)
+
+def _mask_by_age(age:str, blob:pd.Series)->np.ndarray:
+    kids=blob.str.contains(r"\b(?:kid|kids|child|children|toddler|baby|boys?|girls?|kid's|children's)\b",regex=True,na=False)
+    teen=blob.str.contains(r"\b(?:teen|teens|young adult|ya)\b",regex=True,na=False)
+    if age in ("adult","senior"): return (~kids).to_numpy()
+    if age=="teens": return ((~kids)|teen).to_numpy()
+    if age=="kids": return (kids | (~teen & kids)).to_numpy()
+    return np.ones(len(blob),bool)
+
+def _interest_bonus(p:Dict, idx:np.ndarray)->np.ndarray:
+    ints=[i.lower() for i in p.get("interests",[]) if i]; syns=[s for it in ints for s in SYNONYMS.get(it,[])]; vocab=set(ints+syns)
+    if not vocab or idx.size==0: return np.zeros(len(idx),"float32")
+    counts=np.array([len(CATALOG["tok_set"].iat[i] & vocab) for i in idx],"float32"); return .10*np.clip(counts,0,6)
+
+def _occasion_bonus(idx:np.ndarray, occ_ui:str)->np.ndarray:
+    pri=OCCASION_PRIORS.get(OCCASION_CANON.get(occ_ui or "Birthday","birthday"),[])
+    if not pri or idx.size==0: return np.zeros(len(idx),"float32")
+    bl=CATALOG["blob"].to_numpy(); out=np.zeros(len(idx),"float32")
+    for j,i in enumerate(idx):
+        bonus=sum(w for kw,w in pri if kw in bl[i]); out[j]=min(bonus,.15)
     return out
 
-def _minmax(x: np.ndarray) -> np.ndarray:
-    if x.size == 0: return x
-    lo, hi = float(np.min(x)), float(np.max(x))
-    if hi <= lo + 1e-9: return np.zeros_like(x)
-    return (x - lo) / (hi - lo)
-
-def _mmr_select(cand_idx: np.ndarray, scores: np.ndarray, k: int, lambda_: float=0.7) -> np.ndarray:
-    if cand_idx.size <= k:
-        order = np.argsort(-scores)
-        return cand_idx[order][:k]
-    picked = []
-    rest = list(range(len(cand_idx)))
-    rel = _minmax(scores)
-    V = np.asarray(EMB.embs, dtype="float32")[cand_idx]
-    V = V / (np.linalg.norm(V, axis=1, keepdims=True) + 1e-8)
-    while len(picked) < k and rest:
-        if not picked:
-            j = int(np.argmax(rel[rest]))
-            picked.append(rest.pop(j))
-            continue
-        sim_to_sel = []
-        for c in rest:
-            sims = V[c] @ V[picked].T
-            smax = float(sims if np.ndim(sims) == 0 else np.max(sims))
-            sim_to_sel.append(smax)
-        sim_to_sel = np.array(sim_to_sel, dtype="float32")
-        mmr = lambda_ * rel[rest] - (1 - lambda_) * sim_to_sel
-        j = int(np.argmax(mmr))
-        picked.append(rest.pop(j))
-    return cand_idx[np.array(picked, dtype=int)]
-
-def recommend_top3_budget_first(profile: Dict) -> pd.DataFrame:
-    lo = float(profile.get("budget_min", 0))
-    hi = float(profile.get("budget_max", 1e9))
-    m_price = (CATALOG["price_usd"].values >= lo) & (CATALOG["price_usd"].values <= hi)
-    m_age   = _mask_by_age(profile.get("age_range","any"), CATALOG["blob"])
-    m_gender_ok = _gender_ok_mask(profile.get("gender","any"))
-    base_mask = m_price & m_age & m_gender_ok
-    idx = np.where(base_mask)[0]
-    if idx.size == 0:
-        idx = np.where(m_price & m_gender_ok)[0]
-    if idx.size == 0:
-        lo2, hi2 = max(0, lo*0.8), (hi*1.2 if hi < 1e8 else hi)
-        m_price2 = (CATALOG["price_usd"].values >= lo2) & (CATALOG["price_usd"].values <= hi2)
-        idx = np.where(m_price2 & m_gender_ok)[0]
-    if idx.size == 0:
-        cheapest = np.argsort(CATALOG["price_usd"].values)[:3]
-        res = CATALOG.iloc[cheapest].copy()
-        res["similarity"] = np.nan
-        return res[["name","short_desc","price_usd","image_url","similarity"]].reset_index(drop=True)
-
-    q = profile_to_query(profile)
-    qv = EMB.query_vec(q).astype("float32")
-    X = np.asarray(EMB.embs, dtype="float32")[idx]
-    emb_sims = X @ qv
-
-    target_price = (lo + hi)/2.0 if hi > lo else hi
-    prices = CATALOG.iloc[idx]["price_usd"].to_numpy()
-    price_bonus = np.clip(0.12 - np.abs(prices - target_price)/max(target_price,1.0), 0, 0.12).astype("float32")
-
-    int_bonus = _interest_bonus(profile, idx)
-    occ_bonus = _occasion_bonus(idx, profile.get("occ_ui","Birthday"))
-
-    pre_score = emb_sims + price_bonus + int_bonus + occ_bonus
-
-    K1 = min(64, idx.size)
-    top_local = np.argpartition(-pre_score, K1-1)[:K1]
-    cand_idx = idx[top_local]
-
-    emb_norm   = _minmax(emb_sims[top_local])
-    price_norm = _minmax(price_bonus[top_local])
-    int_norm   = _minmax(int_bonus[top_local])
-    occ_norm   = _minmax(oc_bonus := occ_bonus[top_local])
-
-    try:
-        from sentence_transformers import CrossEncoder as _CE
-        ce = _load_cross_encoder()
-        if ce is not None:
-            docs  = CATALOG.loc[cand_idx, "doc"].tolist()
-            pairs = [(q, d) for d in docs]
-            ce_raw  = np.array(ce.predict(pairs), dtype="float32")
-            ce_norm = _minmax(ce_raw)
-        else:
-            ce_norm = np.zeros_like(emb_norm)
-    except Exception:
-        ce_norm = np.zeros_like(emb_norm)
-
-    final = (
-        0.56 * emb_norm +
-        0.26 * ce_norm +
-        0.10 * int_norm +
-        0.05 * occ_norm +
-        0.03 * price_norm
-    ).astype("float32")
-
-    pick_idx = _mmr_select(cand_idx, final, k=min(3, cand_idx.size), lambda_=0.7)
-
-    res = CATALOG.loc[pick_idx].copy()
-    pos = {int(cand_idx[i]): i for i in range(len(cand_idx))}
-    res["similarity"] = [float(final[pos[int(i)]]) for i in pick_idx]
+def _minmax(x:np.ndarray)->np.ndarray:
+    if x.size==0: return x
+    lo,hi=float(np.min(x)),float(np.max(x)); 
+    return np.zeros_like(x) if hi<=lo+1e-9 else (x-lo)/(hi-lo)
+
+def _mmr_select(cand_idx:np.ndarray, scores:np.ndarray, k:int, lambda_:float=.7)->np.ndarray:
+    if cand_idx.size<=k: return cand_idx[np.argsort(-scores)][:k]
+    picked=[]; rest=list(range(len(cand_idx))); rel=_minmax(scores)
+    V=np.asarray(EMB.embs,"float32")[cand_idx]; V/=np.linalg.norm(V,axis=1,keepdims=True)+1e-8
+    while len(picked)<k and rest:
+        if not picked: picked.append(rest.pop(int(np.argmax(rel[rest])))); continue
+        sim_to_sel=np.array([float((V[c]@V[picked].T) if np.ndim(V[c]@V[picked].T)==0 else np.max(V[c]@V[picked].T)) for c in rest],"float32")
+        j=int(np.argmax(lambda_*rel[rest]-(1-lambda_)*sim_to_sel)); picked.append(rest.pop(j))
+    return cand_idx[np.array(picked,int)]
+
+def recommend_top3_budget_first(p:Dict)->pd.DataFrame:
+    lo,hi=float(p.get("budget_min",0)),float(p.get("budget_max",1e9))
+    m=(CATALOG["price_usd"].values>=lo)&(CATALOG["price_usd"].values<=hi); age=_mask_by_age(p.get("age_range","any"),CATALOG["blob"]); g=_gender_ok_mask(p.get("gender","any"))
+    idx=np.where(m&age&g)[0]
+    if idx.size==0:
+        idx=np.where(m&g)[0]
+        if idx.size==0:
+            lo2,hi2=max(0,lo*.8), (hi*1.2 if hi<1e8 else hi); idx=np.where(((CATALOG["price_usd"].values>=lo2)&(CATALOG["price_usd"].values<=hi2))&g)[0]
+            if idx.size==0:
+                cheapest=np.argsort(CATALOG["price_usd"].values)[:3]; res=CATALOG.iloc[cheapest].copy(); res["similarity"]=np.nan
+                return res[["name","short_desc","price_usd","image_url","similarity"]].reset_index(drop=True)
+    q=profile_to_query(p); qv=EMB.query_vec(q).astype("float32")
+    emb_sims=np.asarray(EMB.embs,"float32")[idx]@qv
+    target=(lo+hi)/2.0 if hi>lo else hi; prices=CATALOG.iloc[idx]["price_usd"].to_numpy()
+    price_bonus=np.clip(.12-np.abs(prices-target)/max(target,1.0),0,.12).astype("float32")
+    int_bonus=_interest_bonus(p,idx); occ_bonus=_occasion_bonus(idx,p.get("occ_ui","Birthday"))
+    pre=emb_sims+price_bonus+int_bonus+occ_bonus
+    K1=min(48,idx.size); top_local=np.argpartition(-pre,K1-1)[:K1]; cand_idx=idx[top_local]
+    emb_n=_minmax(emb_sims[top_local]); price_n=_minmax(price_bonus[top_local]); int_n=_minmax(int_bonus[top_local]); occ_n=_minmax(occ_bonus[top_local])
+    ce=_load_cross_encoder(); 
+    if ce is not None:
+        docs=CATALOG.loc[cand_idx,"doc"].tolist(); pairs=[(q,d) for d in docs]
+        k_ce=min(24,len(pairs)); tl=np.argpartition(-emb_n,k_ce-1)[:k_ce]; ce_raw=np.array(ce.predict([pairs[i] for i in tl]),"float32"); ce_n=np.zeros_like(emb_n); ce_n[tl]=_minmax(ce_raw)
+    else:
+        ce_n=np.zeros_like(emb_n)
+    final=(.56*emb_n+.26*ce_n+.10*int_n+.05*occ_n+.03*price_n).astype("float32")
+    pick=_mmr_select(cand_idx,final,k=min(3,cand_idx.size))
+    res=CATALOG.loc[pick].copy(); pos={int(cand_idx[i]):i for i in range(len(cand_idx))}; res["similarity"]=[float(final[pos[int(i)]]) for i in pick]
     return res[["name","short_desc","price_usd","image_url","similarity"]].reset_index(drop=True)
 
-# ====================== END Recommendations ======================
+# ===== DIY (FLAN-only) =====
+DIY_MODEL_ID=os.getenv("DIY_MODEL_ID","google/flan-t5-small"); DIY_DEVICE=torch.device("cpu")
+MAX_INPUT_TOKENS=int(os.getenv("MAX_INPUT_TOKENS","384")); DIY_MAX_NEW_TOKENS=int(os.getenv("DIY_MAX_NEW_TOKENS","120"))
+INTEREST_ALIASES={"Reading":["book","novel","literary"],"Fashion":["style","chic","silk"],"Home decor":["candle","wall","jar"],"Technology":["tech","gadget","usb"],"Movies":["film","cinema","poster"]}
+FALLBACK_NOUNS=["Kit","Set","Bundle","Box","Pack"]
 
-
-# --------------------- DIY (FLAN-only) — strict prompts + sanitizers ---------------------
-print("transformers:", __import__("transformers").__version__, "| torch:", torch.__version__)
-DIY_MODEL_ID = os.getenv("DIY_MODEL_ID", "google/flan-t5-small")
-DIY_DEVICE = torch.device("cpu")
-MAX_INPUT_TOKENS = int(os.getenv("MAX_INPUT_TOKENS", "384"))
-DIY_MAX_NEW_TOKENS = int(os.getenv("DIY_MAX_NEW_TOKENS", "120"))
-
-INTEREST_ALIASES = {
-    "Reading": ["book", "novel", "literary"],
-    "Fashion": ["style", "chic", "silk"],
-    "Home decor": ["candle", "wall", "jar"],
-    "Technology": ["tech", "gadget", "usb"],
-    "Movies": ["film", "cinema", "poster"],
-}
-FALLBACK_NOUNS = ["Kit", "Set", "Bundle", "Box", "Pack"]
-
-_diy_cache = {}
-def _load_flan(mid: str):
-    if mid in _diy_cache: return _diy_cache[mid]
-    tok = AutoTokenizer.from_pretrained(mid, use_fast=True, trust_remote_code=True)
-    mdl = AutoModelForSeq2SeqLM.from_pretrained(mid, trust_remote_code=True, use_safetensors=True).to(DIY_DEVICE).eval()
-    _diy_cache[mid] = (tok, mdl)
-    print(f"[DIY] Loaded FLAN: {mid}")
-    return _diy_cache[mid]
+_diy_cache_model={}
+def _load_flan(mid:str):
+    if mid in _diy_cache_model: return _diy_cache_model[mid]
+    tok=AutoTokenizer.from_pretrained(mid, use_fast=True, trust_remote_code=True)
+    mdl=AutoModelForSeq2SeqLM.from_pretrained(mid, trust_remote_code=True, use_safetensors=True).to(DIY_DEVICE).eval()
+    _diy_cache_model[mid]=(tok,mdl); return _diy_cache_model[mid]
 
 @torch.inference_mode()
-def _gen(tok, mdl, prompt: str, max_new_tokens=64, do_sample=False, temperature=0.9, top_p=0.95, seed=None) -> str:
-    if seed is None: seed = random.randint(1, 10_000_000)
+def _gen(tok, mdl, prompt, max_new_tokens=64, do_sample=False, temperature=.9, top_p=.95, seed=None):
+    if seed is None: seed=random.randint(1,10_000_000)
     random.seed(seed); torch.manual_seed(seed)
-    enc = tok(prompt, truncation=True, max_length=MAX_INPUT_TOKENS, return_tensors="pt")
-    enc = {k: v.to(DIY_DEVICE) for k,v in enc.items()}
-    kw = dict(max_new_tokens=max_new_tokens, eos_token_id=tok.eos_token_id, pad_token_id=tok.eos_token_id)
-    if do_sample:
-        kw.update(dict(do_sample=True, temperature=temperature, top_p=top_p))
-    else:
-        kw.update(dict(do_sample=False, num_beams=1))
-    out = mdl.generate(**enc, **kw)
+    enc=tok(prompt, truncation=True, max_length=MAX_INPUT_TOKENS, return_tensors="pt"); enc={k:v.to(DIY_DEVICE) for k,v in enc.items()}
+    out=mdl.generate(**enc, max_new_tokens=max_new_tokens, eos_token_id=tok.eos_token_id, pad_token_id=tok.eos_token_id, **({"do_sample":True,"temperature":temperature,"top_p":top_p} if do_sample else {"do_sample":False,"num_beams":1}))
     return tok.decode(out[0], skip_special_tokens=True).strip()
 
-def _choose_interest_token(interests: List[str]) -> str:
+def _choose_interest_token(interests): 
     for it in interests:
-        cand = INTEREST_ALIASES.get(it, [])
-        if cand: return random.choice(cand)
+        if INTEREST_ALIASES.get(it): return random.choice(INTEREST_ALIASES[it])
     return (interests[0].split()[0].lower() if interests else "gift")
-
-def _title_case(s: str) -> str:
-    s = re.sub(r'\s+', ' ', s).strip()
-    s = re.sub(r'["“”‘’]+', '', s)
-    return " ".join([w.capitalize() for w in s.split()])
-
-def _sanitize_name(name: str, interests: List[str]) -> str:
-    bad = [r"^the name\b", r"\bmember of the family\b", r"^name\b", r"^title\b"]
-    for b in bad:
-        name = re.sub(b, "", name, flags=re.I).strip()
-    name = re.sub(r'[:\-–—]+$', "", name).strip()
-    alias = _choose_interest_token(interests)
-    base = name.lower()
-    if alias not in base:
-        tokens = [t for t in re.split(r"[\s\-]+", name) if t]
-        if len(tokens) < 4:
-            name = f"{alias.capitalize()} " + " ".join([t.capitalize() for t in tokens]) if tokens else f"{alias.capitalize()} {random.choice(FALLBACK_NOUNS)}"
-        else:
-            tokens.insert(1, alias.capitalize())
-            name = " ".join(tokens)
-    name = re.sub(r'\b(Home Decor:?\s*){2,}', 'Home Decor ', name, flags=re.I)
-    name = _title_case(name)[:80]
-    if len(name.split()) < 3:
-        noun = random.choice(FALLBACK_NOUNS)
-        name = f"{alias.capitalize()} {noun}"
+def _title_case(s): s=re.sub(r'\s+',' ',s).strip(); s=re.sub(r'["“”‘’]+','',s); return " ".join([w.capitalize() for w in s.split()])
+def _sanitize_name(name, interests):
+    for b in [r"^the name\b",r"\bmember of the family\b",r"^name\b",r"^title\b"]: name=re.sub(b,"",name,flags=re.I).strip()
+    name=re.sub(r'[:\-–—]+$',"",name).strip(); alias=_choose_interest_token(interests)
+    if alias not in name.lower():
+        tokens=[t for t in re.split(r"[\s\-]+",name) if t]
+        name=(f"{alias.capitalize()} "+(" ".join([t.capitalize() for t in tokens]) if tokens else random.choice(FALLBACK_NOUNS))) if len(tokens)<4 else " ".join([tokens[0],alias.capitalize(),*tokens[1:]])
+    name=re.sub(r'\b(Home Decor:?\s*){2,}','Home Decor ',name,flags=re.I); name=_title_case(name)[:80]
+    if len(name.split())<3: name=f"{alias.capitalize()} {random.choice(FALLBACK_NOUNS)}"
     return name
 
-def _split_list_text(s: str, seps: List[str]) -> List[str]:
-    s = s.strip()
+def _split_list_text(s,seps):
+    s=s.strip()
     for sep in seps:
         if sep in s:
-            parts = [p.strip(" -•*.,;:") for p in s.split(sep)]
-            parts = [p for p in parts if p]
-            if len(parts) >= 2:
-                return parts
-    parts = [p.strip(" -•*.,;:") for p in re.split(r"[\n\r;]+", s)]
-    return [p for p in parts if p]
+            parts=[p.strip(" -•*.,;:") for p in s.split(sep) if p.strip(" -•*.,;:")]
+            if len(parts)>=2: return parts
+    return [p.strip(" -•*.,;:") for p in re.split(r"[\n\r;]+", s) if p.strip(" -•*.,;:")]
 
-def _coerce_materials(items: List[str]) -> List[str]:
-    out = []
+def _coerce_materials(items):
+    out=[]
     for it in items:
-        it = re.sub(r'\s+', ' ', it).strip(" -•*.,;:")
+        it=re.sub(r'\s+',' ',it).strip(" -•*.,;:"); 
         if not it: continue
-        it = re.sub(r'(\b\w+\b)(?:\s+\1){2,}', r'\1', it, flags=re.I)
-        if len(it) > 60: it = it[:58] + "…"
-        if not re.search(r"\d", it):
-            it = it + " x1"
-        if it.lower() not in [x.lower() for x in out]:
-            out.append(it)
-        if len(out) >= 8: break
-    base = ["Small gift box x1","Decorative paper x2","Twine 2 m","Cardstock sheets x2","Double-sided tape x1","Stickers x8","Ribbon 1 m","Fine-tip marker x1"]
-    i = 0
-    while len(out) < 6 and i < len(base):
-        if base[i].lower() not in [x.lower() for x in out]:
-            out.append(base[i])
-        i += 1
+        it=re.sub(r'(\b\w+\b)(?:\s+\1){2,}',r'\1',it,flags=re.I)
+        if len(it)>60: it=it[:58]+"…"
+        if not re.search(r"\d",it): it+=" x1"
+        if it.lower() not in [x.lower() for x in out]: out.append(it)
+        if len(out)>=8: break
+    base=["Small gift box x1","Decorative paper x2","Twine 2 m","Cardstock sheets x2","Double-sided tape x1","Stickers x8","Ribbon 1 m","Fine-tip marker x1"]
+    for b in base:
+        if len(out)>=6: break
+        if b.lower() not in [x.lower() for x in out]: out.append(b)
     return out[:8]
 
-def _coerce_steps(items: List[str]) -> List[str]:
-    out = []
+def _coerce_steps(items):
+    out=[]
     for it in items:
-        it = it.strip(" -•*.,;:")
+        it=it.strip(" -•*.,;:"); 
         if not it: continue
-        it = re.sub(r'\s+', ' ', it)
-        if len(it) > 120: it = it[:118] + "…"
-        it = re.sub(r'^(?:\d+[\).\s-]*)', '', it)
-        it = it[0].upper() + it[1:] if it else it
-        out.append(it)
-        if len(out) >= 8: break
-    while len(out) < 6:
-        out.append(f"Refine and decorate step {len(out)+1}")
+        it=re.sub(r'\s+',' ',it); 
+        if len(it)>120: it=it[:118]+"…"
+        it=re.sub(r'^(?:\d+[\).\s-]*)','',it); it=it[0].upper()+it[1:] if it else it; out.append(it)
+        if len(out)>=8: break
+    while len(out)<6: out.append(f"Refine and decorate step {len(out)+1}")
     return out[:8]
 
-def _only_int(s: str) -> Optional[int]:
-    m = re.search(r"-?\d+", s)
-    return int(m.group()) if m else None
-
-def _clamp_num(v, lo, hi, default):
-    try:
-        x = float(v)
-        return int(min(max(x, lo), hi))
-    except:
-        return int((lo + hi) / 2 if default is None else default)
-
-def _build_json(profile: Dict, fields: Dict[str, Any]) -> Dict[str, Any]:
-    mats = _coerce_materials(fields.get("materials_needed", []))
-    steps = _coerce_steps(fields.get("steps", []))
-    cost = _clamp_num(fields.get("estimated_cost_usd"), profile["budget_min"], profile["budget_max"], default=None)
-    minutes = _clamp_num(fields.get("estimated_time_minutes"), 20, 180, default=60)
-    return {
-        "gift_name": fields.get("gift_name","DIY Gift Idea").strip()[:80],
-        "overview":  fields.get("overview","A practical handmade gift tailored to the recipient.").strip(),
-        "materials_needed": mats,
-        "steps": steps,
-        "estimated_cost_usd": cost,
-        "estimated_time_minutes": minutes,
-    }
-
-def diy_generate(profile: Dict) -> Tuple[dict, str]:
-    tok, mdl = _load_flan(DIY_MODEL_ID)
-    p = {
-        "recipient_name": profile.get("recipient_name","Recipient"),
-        "relationship": profile.get("relationship","Friend"),
-        "occ_ui": profile.get("occ_ui","Birthday"),
-        "occasion": profile.get("occ_ui","Birthday"),
-        "interests": profile.get("interests",[]),
-        "budget_min": int(float(profile.get("budget_min",10))),
-        "budget_max": int(float(profile.get("budget_max",100))),
-        "age_range": profile.get("age_range","any"),
-        "gender": profile.get("gender","any")
-    }
-    lang = "English"
-    ints_str = ", ".join(p["interests"]) or "general"
-
-    prompt_name = (
-        f"Return ONLY a DIY gift NAME in Title Case (4–8 words). "
-        f"Must include at least one interest token from: {', '.join(sum(([it]+INTEREST_ALIASES.get(it,[]) for it in p['interests']), [])) or 'gift'}. "
-        f"Occasion: {p['occ_ui']}. Relationship: {p['relationship']}. Language: {lang}. "
-        "Forbidden: the words 'name', 'title', 'family'. No quotes, no trailing punctuation.\n"
-        "Examples:\n"
-        "Reading → Literary Candle Bookmark Kit\n"
-        "Technology → Gadget Cable Organizer Set\n"
-        "Home decor → Rustic Jar Candle Bundle\n"
-        "Output:"
-    )
-    raw_name = _gen(tok, mdl, prompt_name, max_new_tokens=24, do_sample=False)
-    name = _sanitize_name(raw_name, p["interests"])
-
-    prompt_over = (
-        f"Write EXACTLY 2 sentences in {lang} for a handmade gift called '{name}'. "
-        f"Mention {p['recipient_name']} ({p['relationship']}) and the occasion ({p['occ_ui']}). "
-        f"Explain how it reflects the interests: {ints_str}. "
-        "No lists, no emojis. Output only the two sentences."
-    )
-    overview = _gen(tok, mdl, prompt_over, max_new_tokens=80, do_sample=True, temperature=0.9, top_p=0.95)
-
-    prompt_mat = (
-        f"List 6 concise materials with quantities to make '{name}' cheaply. "
-        f"Keep total within {p['budget_min']}-{p['budget_max']} USD. "
-        "Output ONLY a comma-separated list (e.g., 'glass jar x2, soy tealights x4, ...')."
-    )
-    mats_txt = _gen(tok, mdl, prompt_mat, max_new_tokens=96, do_sample=False)
-    materials = _split_list_text(mats_txt, [",", ";"])
-
-    prompt_steps = (
-        f"Write 6 short imperative steps to make '{name}'. "
-        "Output ONLY a semicolon-separated list."
-    )
-    steps_txt = _gen(tok, mdl, prompt_steps, max_new_tokens=120, do_sample=True, temperature=0.9, top_p=0.95)
-    steps = _split_list_text(steps_txt, [";", "\n"])
-
-    prompt_cost = (
-        f"Return ONE integer total cost in USD between {p['budget_min']}-{p['budget_max']}. Output NUMBER only."
-    )
-    cost_txt = _gen(tok, mdl, prompt_cost, max_new_tokens=6, do_sample=False)
-    cost = _only_int(cost_txt)
-
-    time_txt = _gen(tok, mdl, "Return ONE integer minutes between 20 and 180. Output NUMBER only.",
-                    max_new_tokens=6, do_sample=False)
-    minutes = _only_int(time_txt)
-
-    idea = _build_json(p, {
-        "gift_name": name,
-        "overview": overview,
-        "materials_needed": materials,
-        "steps": steps,
-        "estimated_cost_usd": cost,
-        "estimated_time_minutes": minutes,
-    })
-    return idea, "ok"
-# --------------------- END DIY ---------------------
-
+def _only_int(s): m=re.search(r"-?\d+",s); return int(m.group()) if m else None
+def _clamp_num(v,lo,hi,default):
+    try: x=float(v); return int(min(max(x,lo),hi))
+    except: return int((lo+hi)/2 if default is None else default)
+
+def diy_generate(profile:Dict)->Tuple[dict,str]:
+    tok,mdl=_load_flan(DIY_MODEL_ID)
+    p={"recipient_name":profile.get("recipient_name","Recipient"),"relationship":profile.get("relationship","Friend"),
+       "occ_ui":profile.get("occ_ui","Birthday"),"occasion":profile.get("occ_ui","Birthday"),"interests":profile.get("interests",[]),
+       "budget_min":int(float(profile.get("budget_min",10))),"budget_max":int(float(profile.get("budget_max",100))),
+       "age_range":profile.get("age_range","any"),"gender":profile.get("gender","any")}
+    lang="English"; ints_str=", ".join(p["interests"]) or "general"
+    prompt_name=(f"Return ONLY a DIY gift NAME in Title Case (4–8 words). Must include at least one interest token from: "
+                 f"{', '.join(sum(([it]+INTEREST_ALIASES.get(it,[]) for it in p['interests']), [])) or 'gift'}. "
+                 f"Occasion: {p['occ_ui']}. Relationship: {p['relationship']}. Language: {lang}. Forbidden: the words 'name','title','family'. "
+                 "No quotes, no trailing punctuation.\nExamples:\nReading → Literary Candle Bookmark Kit\nTechnology → Gadget Cable Organizer Set\nHome decor → Rustic Jar Candle Bundle\nOutput:")
+    name=_sanitize_name(_gen(tok,mdl,prompt_name, max_new_tokens=24, do_sample=False), p["interests"])
+    overview=_gen(tok,mdl,(f"Write EXACTLY 2 sentences in {lang} for a handmade gift called '{name}'. Mention {p['recipient_name']} "
+                           f"({p['relationship']}) and the occasion ({p['occ_ui']}). Explain how it reflects the interests: {ints_str}. "
+                           "No lists, no emojis. Output only the two sentences."), max_new_tokens=80, do_sample=True, temperature=.9, top_p=.95)
+    materials=_split_list_text(_gen(tok,mdl,(f"List 6 concise materials with quantities to make '{name}' cheaply. Keep total within "
+                           f"{p['budget_min']}-{p['budget_max']} USD. Output ONLY a comma-separated list."), max_new_tokens=96, do_sample=False), [",",";"])
+    steps=_split_list_text(_gen(tok,mdl,(f"Write 6 short imperative steps to make '{name}'. Output ONLY a semicolon-separated list."), max_new_tokens=120, do_sample=True, temperature=.9, top_p=.95), [";","\n"])
+    cost=_only_int(_gen(tok,mdl,(f"Return ONE integer total cost in USD between {p['budget_min']}-{p['budget_max']}. Output NUMBER only."), max_new_tokens=6, do_sample=False))
+    minutes=_only_int(_gen(tok,mdl,"Return ONE integer minutes between 20 and 180. Output NUMBER only.", max_new_tokens=6, do_sample=False))
+    idea={"gift_name":name,"overview":overview,"materials_needed":_coerce_materials(materials),"steps":_coerce_steps(steps),
+          "estimated_cost_usd":_clamp_num(cost,p["budget_min"],p["budget_max"],None),"estimated_time_minutes":_clamp_num(minutes,20,180,60)}
+    return idea,"ok"
 
 # --------------------- Personalized Message (FLAN, מגוון + ולידציה) ---------------------
 # >>>>>>>>>>>>>>>>>>>>>> השינוי היחיד בקובץ – מימוש מהקולאב <<<<<<<<<<<<<<<<<<<<<<
 MSG_MODEL_ID = "google/flan-t5-small"
-MSG_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+MSG_DEVICE = "cpu"
 TEMP_RANGE  = (0.88, 1.10)
 TOPP_RANGE  = (0.90, 0.96)
 REP_PENALTY = 1.12
@@ -763,12 +398,7 @@ TONE_STYLES: Dict[str, Dict[str, List[str]]] = {
     },
 }
 BAN_PHRASES = [
-    "wishing you all the best",
-    "may your day be filled",
-    "on this special day",
-    "from the bottom of my heart",
-    "best wishes on your",
-    "warm wishes",
+
 ]
 OPENERS = [
     "Here’s to a moment that fits you perfectly:",
@@ -831,14 +461,12 @@ def _build_prompt(profile: Dict[str, Any]) -> Tuple[str, Dict[str,str]]:
     ])
 
     lines = [
-        "You are a skilled copywriter. Generate a short gift-card message in English (2–3 sentences).",
+        "Generate a short gift-card message in English (2–3 sentences).",
         f"Recipient: {name} ({rel}). Occasion: {occ}. Interests: {ints}. Tone: {tone}.",
         style["system"],
         "Rules:",
         *[f"- {r}" for r in style["rules"]],
-        f"- Avoid clichés such as: {', '.join(BAN_PHRASES)}.",
         "- No emojis. No bullet points.",
-        "- Do not start with 'Dear' unless Tone is Formal.",
         f"- Start with: \"{opener}\" (continue naturally, not as a header).",
         f"- End with a natural line similar to: \"{closer}\" (rephrase; do not quote).",
         f"- {spice}",
@@ -893,291 +521,112 @@ def generate_personal_message(profile: Dict[str, Any], seed: Optional[int]=None,
     return {"message": fallback, "meta": {"failed": True, "model": MSG_MODEL_ID, "tone": profile.get("tone","Heartfelt")}}
 # --------------------- END Personalized Message ---------------------
 
-# --------------------- Rendering ---------------------
-def md_escape(text: str) -> str:
-    return str(text).replace("|","\\|").replace("*","\\*").replace("_","\\_")
 
-def first_sentence(s: str, max_chars: int = 140) -> str:
-    s = (s or "").strip()
+# ===== Rendering & UI =====
+def first_sentence(s,max_chars=140): 
+    s=(s or "").strip(); 
     if not s: return ""
-    cut = s.split(". ")[0]
-    if len(cut) > max_chars: cut = cut[:max_chars-1] + "…"
-    return cut
-
-def render_top3_html(df: pd.DataFrame, age_label: str) -> str:
-    if df is None or df.empty:
-        return "<em>No results found within the current filters.</em>"
-    rows = []
-    for _, r in df.iterrows():
-        name = md_escape(r.get("name",""))
-        desc = md_escape(first_sentence(r.get("short_desc","")))
-        price = r.get("price_usd")
-        sim = r.get("similarity")
-        img = r.get("image_url","") or ""
-        price_str = f"${price:.0f}" if pd.notna(price) else "N/A"
-        sim_str = f"{sim:.3f}" if pd.notna(sim) else "—"
-        img_html = f'<img src="{img}" alt="" style="width:84px;height:84px;object-fit:cover;border-radius:10px;margin-left:12px;" />' if img else ""
-        card = f"""
+    cut=s.split(". ")[0]; 
+    return cut if len(cut)<=max_chars else cut[:max_chars-1]+"…"
+
+def render_top3_html(df, age_label):
+    if df is None or df.empty: return "<em>No results found within the current filters.</em>"
+    rows=[]
+    for _,r in df.iterrows():
+        name=str(r.get("name","")).replace("|","\\|").replace("*","\\*").replace("_","\\_")
+        desc=str(first_sentence(r.get("short_desc",""))).replace("|","\\|").replace("*","\\*").replace("_","\\_")
+        price=r.get("price_usd"); sim=r.get("similarity"); img=r.get("image_url","") or ""
+        price_str=f"${price:.0f}" if pd.notna(price) else "N/A"; sim_str=f"{sim:.3f}" if pd.notna(sim) else "—"
+        img_html=f'<img src="{img}" alt="" style="width:84px;height:84px;object-fit:cover;border-radius:10px;margin-left:12px;" />' if img else ""
+        rows.append(f"""
 <div style="display:flex;align-items:flex-start;justify-content:space-between;gap:10px;padding:10px;border:1px solid #eee;border-radius:12px;margin-bottom:8px;background:#fff;">
-  <div style="flex:1;min-width:0;">
-    <div style="font-weight:700;">{name}</div>
+  <div style="flex:1;min-width:0;"><div style="font-weight:700;">{name}</div>
     <div style="font-size:0.95em;margin-top:4px;">{desc}</div>
-    <div style="font-size:0.9em;margin-top:6px;opacity:0.8;">
-      Price: <b>{price_str}</b> · Age: <code>{age_label}</code> · Score: <code>{sim_str}</code>
-    </div>
-  </div>
-  {img_html}
-</div>
-"""
-        rows.append(card)
+    <div style="font-size:0.9em;margin-top:6px;opacity:0.8;">Price: <b>{price_str}</b> · Age: <code>{age_label}</code> · Score: <code>{sim_str}</code></div>
+  </div>{img_html}
+</div>""")
     return "\n".join(rows)
 
-# --------------------- Gradio UI ---------------------
-with gr.Blocks(
-    title="🎁 GIfty — Recommender + DIY",
-    css="""
-/* כותרת ההסבר */
-#explain {opacity:.85;font-size:.92em;margin-bottom:8px;}
-
-/* --- שדרוג דוגמאות: הופך כל שורה לכרטיס מודרני ודק --- */
-.gr-dataframe thead { display:none; }
-.gr-dataframe table { border-collapse: separate !important; border-spacing: 0 10px !important; table-layout: fixed; width:100%; }
-
-/* השורה ככרטיס */
-.gr-dataframe tbody tr {
-  cursor: pointer;
-  display: block;
-  background: linear-gradient(180deg,#fff, #fafafa);
-  border-radius: 14px;
-  border: 1px solid #e9eef5;
-  box-shadow: 0 1px 1px rgba(16,24,40,.04), 0 1px 2px rgba(16,24,40,.06);
-  padding: 10px 12px;
-  transition: transform .06s ease, box-shadow .12s ease, background .12s ease;
-}
-.gr-dataframe tbody tr:hover {
-  transform: translateY(-1px);
-  background: #f8fafc;
-  box-shadow: 0 3px 10px rgba(16,24,40,.08);
-}
-
-/* תאים מינימליסטיים */
-.gr-dataframe tbody tr td {
-  border: 0 !important;
-  padding: 4px 8px !important;
-  vertical-align: middle;
-  font-size: .92rem;
-  line-height: 1.3;
-}
-
-/* טיפוגרפיה: שם (Recipient) מודגש וגדול יותר */
-.gr-dataframe tbody tr td:nth-child(1) {
-  font-weight: 700;
-  font-size: 1rem;
-  letter-spacing: .2px;
-}
-
-/* יחסים/אירוע – טקסט עדין יותר */
-.gr-dataframe tbody tr td:nth-child(2),
-.gr-dataframe tbody tr td:nth-child(4) {
-  opacity: .8;
-}
-
-/* תגיות קומפקטיות (Interests, Tone, Gender, Age) */
-.gr-dataframe tbody tr td:nth-child(3),
-.gr-dataframe tbody tr td:nth-child(9),
-.gr-dataframe tbody tr td:nth-child(6),
-.gr-dataframe tbody tr td:nth-child(5) {
-  display: inline-block;
-  background: #eff4ff;
-  color: #243b6b;
-  border: 1px solid #dbe5ff;
-  border-radius: 999px;
-  padding: 2px 10px !important;
-  font-size: .84rem;
-  margin: 2px 6px 2px 0;
-}
-
-/* תקציב – צ'יפ מחושב (Min/Max) */
-.gr-dataframe tbody tr td:nth-child(7),
-.gr-dataframe tbody tr td:nth-child(8) {
-  display: inline-block;
-  background: #f1f5f9;
-  border: 1px solid #e2e8f0;
-  color: #0f172a;
-  border-radius: 10px;
-  padding: 2px 8px !important;
-  font-variant-numeric: tabular-nums;
-  margin: 2px 6px 2px 0;
-}
-
-/* פריסה קומפקטית בשורה אחת */
-.gr-dataframe tbody tr td {
-  margin-right: 2px;
-}
-.gr-dataframe tbody tr td:last-child {
-  margin-right: 0;
-}
-
-/* העלמת היילייט/מסגרות מיותרות של Handsontable */
-.handsontable .wtBorder, .handsontable .htBorders, .handsontable .wtBorder.current { display: none !important; }
-.gr-dataframe table td:focus { outline: none !important; box-shadow: none !important; }
-"""
-) as demo:
+with gr.Blocks(title="🎁 GIfty — Recommender + DIY", css="""
+#explain{opacity:.85;font-size:.92em;margin-bottom:8px;}
+.gr-dataframe thead{display:none;}
+.gr-dataframe table{border-collapse:separate!important;border-spacing:0 10px!important;table-layout:fixed;width:100%;}
+.gr-dataframe tbody tr{cursor:pointer;display:block;background:linear-gradient(180deg,#fff,#fafafa);border-radius:14px;border:1px solid #e9eef5;box-shadow:0 1px 1px rgba(16,24,40,.04),0 1px 2px rgba(16,24,40,.06);padding:10px 12px;transition:transform .06s ease, box-shadow .12s ease, background .12s ease;}
+.gr-dataframe tbody tr:hover{transform:translateY(-1px);background:#f8fafc;box-shadow:0 3px 10px rgba(16,24,40,.08);}
+.gr-dataframe tbody tr td{border:0!important;padding:4px 8px!important;vertical-align:middle;font-size:.92rem;line-height:1.3;}
+.gr-dataframe tbody tr td:nth-child(1){font-weight:700;font-size:1rem;letter-spacing:.2px;}
+.gr-dataframe tbody tr td:nth-child(2),.gr-dataframe tbody tr td:nth-child(4){opacity:.8;}
+.gr-dataframe tbody tr td:nth-child(3),.gr-dataframe tbody tr td:nth-child(9),.gr-dataframe tbody tr td:nth-child(6),.gr-dataframe tbody tr td:nth-child(5){display:inline-block;background:#eff4ff;color:#243b6b;border:1px solid #dbe5ff;border-radius:999px;padding:2px 10px!important;font-size:.84rem;margin:2px 6px 2px 0;}
+.gr-dataframe tbody tr td:nth-child(7),.gr-dataframe tbody tr td:nth-child(8){display:inline-block;background:#f1f5f9;border:1px solid #e2e8f0;color:#0f172a;border-radius:10px;padding:2px 8px!important;font-variant-numeric:tabular-nums;margin:2px 6px 2px 0;}
+.handsontable .wtBorder,.handsontable .htBorders,.handsontable .wtBorder.current{display:none!important;}
+.gr-dataframe table td:focus{outline:none!important;box-shadow:none!important;}
+""") as demo:
     gr.Markdown(TITLE)
-
-    # ===== EXAMPLES TABLE (Top, clickable) =====
     gr.Markdown("### Quick examples (click a row to auto-fill)", elem_id="explain")
+    EXAMPLES=[(["Technology","Movies"],"Birthday",25,45,"Daniel","Friend","adult (18–64)","male","Funny"),
+              (["Art","Reading","Home decor"],"Anniversary",30,60,"Rotem","Romantic partner","adult (18–64)","female","Romantic"),
+              (["Gaming","Photography"],"Birthday",30,120,"Omer","Family - Sibling","teen (13–17)","male","Playful"),
+              (["Reading","Art"],"Graduation",15,35,"Maya","Friend","adult (18–64)","female","Heartfelt"),
+              (["Science","Crafts"],"Holidays",15,30,"Adam","Family - Child","kid (3–12)","any","Encouraging")]
+    EX_COLS=["Recipient","Relationship","Interests","Occasion","Age group","Gender","Min $","Max $","Tone"]
+    EX_DF=pd.DataFrame([[name,rel," + ".join(interests),occ,age,gender,bmin,bmax,tone] for (interests,occ,bmin,bmax,name,rel,age,gender,tone) in EXAMPLES], columns=EX_COLS)
+    ex_df=gr.Dataframe(value=EX_DF, interactive=False, wrap=True); gr.Markdown("---")
 
-    EXAMPLES = [
-        # interests, occasion, bmin, bmax, name, relationship, age_label, gender, tone
-        (["Technology","Movies"],            "Birthday",        25, 45, "Daniel", "Friend",           "adult (18–64)", "male",   "Funny"),
-        (["Art","Reading","Home decor"], "Anniversary", 30, 60, "Rotem",  "Romantic partner", "adult (18–64)", "female", "Romantic"),
-        (["Gaming","Photography"],           "Birthday",        30,120, "Omer",   "Family - Sibling", "teen (13–17)",  "male",   "Playful"),
-        (["Reading","Art"],                  "Graduation",      15, 35, "Maya",   "Friend",           "adult (18–64)", "female", "Heartfelt"),
-        (["Science","Crafts"],               "Holidays",        15, 30, "Adam",   "Family - Child",   "kid (3–12)",    "any",    "Encouraging"),
-    ]
-    EX_COLS = ["Recipient","Relationship","Interests","Occasion","Age group","Gender","Min $","Max $","Tone"]
-    EX_DF = pd.DataFrame([
-        [name, rel, " + ".join(interests), occ, age, gender, bmin, bmax, tone]
-        for (interests, occ, bmin, bmax, name, rel, age, gender, tone) in EXAMPLES
-    ], columns=EX_COLS)
-
-    ex_df = gr.Dataframe(value=EX_DF, interactive=False, wrap=True)
-    gr.Markdown("---")
-
-    # === Inputs (always open) ===
     with gr.Row():
-        recipient_name = gr.Textbox(label="Recipient name", value="Daniel")
-        relationship   = gr.Dropdown(label="Relationship", choices=RECIPIENT_RELATIONSHIPS, value="Friend")
+        recipient_name=gr.Textbox(label="Recipient name", value="Daniel")
+        relationship=gr.Dropdown(label="Relationship", choices=RECIPIENT_RELATIONSHIPS, value="Friend")
     with gr.Row():
-        occasion       = gr.Dropdown(label="Occasion", choices=OCCASION_UI, value="Birthday")
-        age            = gr.Dropdown(label="Age group", choices=list(AGE_OPTIONS.keys()), value="adult (18–64)")
-        gender         = gr.Dropdown(label="Recipient gender", choices=GENDER_OPTIONS, value="male")
-    interests      = gr.CheckboxGroup(label="Interests (select a few)", choices=INTEREST_OPTIONS,
-                                      value=["Technology","Movies"], interactive=True)
+        occasion=gr.Dropdown(label="Occasion", choices=OCCASION_UI, value="Birthday")
+        age=gr.Dropdown(label="Age group", choices=list(AGE_OPTIONS.keys()), value="adult (18–64)")
+        gender=gr.Dropdown(label="Recipient gender", choices=GENDER_OPTIONS, value="male")
+    interests=gr.CheckboxGroup(label="Interests (select a few)", choices=INTEREST_OPTIONS, value=["Technology","Movies"], interactive=True)
     with gr.Row():
-        budget_min     = gr.Slider(label="Min budget (USD)", minimum=5, maximum=500, step=1, value=25)
-        budget_max     = gr.Slider(label="Max budget (USD)", minimum=5, maximum=500, step=1, value=45)
-        tone           = gr.Dropdown(label="Message tone", choices=MESSAGE_TONES, value="Funny")
+        budget_min=gr.Slider(label="Min budget (USD)", minimum=5, maximum=500, step=1, value=25)
+        budget_max=gr.Slider(label="Max budget (USD)", minimum=5, maximum=500, step=1, value=45)
+        tone=gr.Dropdown(label="Message tone", choices=MESSAGE_TONES, value="Funny")
 
-    # Action button and outputs
-    go = gr.Button("Get GIfty!")
-    gr.Markdown("### 🎯 Recommendations")
-    out_top3   = gr.HTML()
-    gr.Markdown("### 🛠️ DIY Gift")
-    out_diy_md = gr.Markdown()
-    gr.Markdown("### 💌 Personalized Message")
-    out_msg    = gr.Markdown()
+    go=gr.Button("Get GIfty!")
+    gr.Markdown("### 🎯 Recommendations"); out_top3=gr.HTML()
+    gr.Markdown("### 🛠️ DIY Gift"); out_diy_md=gr.Markdown()
+    gr.Markdown("### 💌 Personalized Message"); out_msg=gr.Markdown()
+    run_token=gr.State(0)
 
-    # --- State: run token (to "override" previous runs safely) ---
-    run_token = gr.State(0)
-
-    # ---- row click handler (fill form) ----
     def _on_example_select(evt: gr.SelectData):
-        r = evt.index[0] if isinstance(evt.index, (list, tuple)) else evt.index
-        if isinstance(r, (list, tuple)): r = r[0]
-        r = int(r)
-        row = EX_DF.iloc[r]
-        ints = [s.strip() for s in str(row["Interests"]).split("+")]
-        return (
-            ints, row["Occasion"], int(row["Min $"]), int(row["Max $"]),
-            row["Recipient"], row["Relationship"], row["Age group"], row["Gender"], row["Tone"]
-        )
-
-    ex_df.select(
-        _on_example_select,
-        outputs=[interests, occasion, budget_min, budget_max, recipient_name, relationship, age, gender, tone]
-    )
+        r=int(evt.index[0] if isinstance(evt.index,(list,tuple)) else evt.index); row=EX_DF.iloc[r]; ints=[s.strip() for s in str(row["Interests"]).split("+")]
+        return (ints,row["Occasion"],int(row["Min $"]),int(row["Max $"]),row["Recipient"],row["Relationship"],row["Age group"],row["Gender"],row["Tone"])
+    ex_df.select(_on_example_select, outputs=[interests, occasion, budget_min, budget_max, recipient_name, relationship, age, gender, tone])
 
-    # ---- helper: render DIY markdown (unchanged logic) ----
-    def render_diy_md(j: dict) -> str:
+    def render_diy_md(j:dict)->str:
         if not j: return "_DIY generation failed._"
-        steps = j.get('step_by_step_instructions', j.get('steps', []))
+        steps=j.get('step_by_step_instructions', j.get('steps', []))
         parts = [
-            f"**{j.get('gift_name','(no name)')}**",
-            "",
-            j.get("overview","").strip(),
-            "",
-            "**Materials**",
-            "\n".join(f"- {m}" for m in j.get("materials_needed", [])),
-            "",
-            "**Steps**",
-            "\n".join(f"{i+1}. {s}" for i, s in enumerate(steps)),
-            "",
+            f"**{j.get('gift_name','(no name)')}**","",
+            j.get('overview','').strip(),"",
+            "**Materials**","\n".join(f"- {m}" for m in j.get('materials_needed',[])),"",
+            "**Steps**","\n".join(f"{i+1}. {s}" for i,s in enumerate(steps)),"",
             f"**Estimated cost:** ${j.get('estimated_cost_usd','?')} · **Time:** {j.get('estimated_time_minutes','?')} min"
         ]
         return "\n".join(parts)
 
-    # ---- Build profile dict (shared) ----
-    def _build_profile(interests_list, occasion_val, bmin, bmax, name, rel, age_label, gender_val, tone_val):
-        try:
-            bmin = float(bmin); bmax = float(bmax)
-        except Exception:
-            bmin, bmax = 5.0, 500.0
-        if bmin > bmax: bmin, bmax = bmax, bmin
-        return {
-            "recipient_name": name or "Friend",
-            "relationship": rel or "Friend",
-            "interests": interests_list or [],
-            "occ_ui": occasion_val or "Birthday",
-            "budget_min": bmin,
-            "budget_max": bmax,
-            "age_range": AGE_OPTIONS.get(age_label, "any"),
-            "gender": (gender_val or "any").lower(),
-            "tone": tone_val or "Heartfelt",
-        }
-
-    # ---- NEW: split into 3 functions (partial results) + token check ----
-    def start_run(curr_token):
-        return int(curr_token or 0) + 1
-
-    def predict_recs_only(rt, interests_list, occasion_val, bmin, bmax, name, rel, age_label, gender_val, tone_val):
-        latest = rt
-        profile = _build_profile(interests_list, occasion_val, bmin, bmax, name, rel, age_label, gender_val, tone_val)
-        top3 = recommend_top3_budget_first(profile)
-        top3_html = render_top3_html(top3, age_label)
-        return gr.update(value=top3_html, visible=True), latest
-
-    def predict_diy_only(rt, interests_list, occasion_val, bmin, bmax, name, rel, age_label, gender_val, tone_val):
-        latest = rt
-        profile = _build_profile(interests_list, occasion_val, bmin, bmax, name, rel, age_label, gender_val, tone_val)
-        diy_json, _status = diy_generate(profile)
-        diy_md = render_diy_md(diy_json)
-        return gr.update(value=diy_md, visible=True), latest
+    def _build_profile(ints, occ, bmin, bmax, name, rel, age_label, gender_val, tone_val):
+        try: bmin=float(bmin); bmax=float(bmax)
+        except: bmin,bmax=5.0,500.0
+        if bmin>bmax: bmin,bmax=bmax,bmin
+        return {"recipient_name":name or "Friend","relationship":rel or "Friend","interests":ints or [],"occ_ui":occ or "Birthday","budget_min":bmin,"budget_max":bmax,"age_range":AGE_OPTIONS.get(age_label,"any"),"gender":(gender_val or "any").lower(),"tone":tone_val or "Heartfelt"}
 
-    def predict_msg_only(rt, interests_list, occasion_val, bmin, bmax, name, rel, age_label, gender_val, tone_val):
-        latest = rt
-        profile = _build_profile(interests_list, occasion_val, bmin, bmax, name, rel, age_label, gender_val, tone_val)
-        msg_obj = generate_personal_message(profile)
-        msg = msg_obj["message"]
-        return gr.update(value=msg, visible=True), latest
+    def start_run(curr): return int(curr or 0)+1
 
-    # --- Wire events: one short "start", then 3 parallel tasks that each update its output ASAP ---
-    ev_start = go.click(
-        start_run,
-        inputs=[run_token],
-        outputs=[run_token],
-        queue=True,
-    )
+    def predict_recs_only(rt, *args):
+        p=_build_profile(*args); top3=recommend_top3_budget_first(p); return gr.update(value=render_top3_html(top3, args[5]), visible=True), rt
+    def predict_diy_only(rt, *args):
+        p=_build_profile(*args); diy_json,_=diy_generate(p); return gr.update(value=render_diy_md(diy_json), visible=True), rt
+    def predict_msg_only(rt, *args):
+        p=_build_profile(*args); msg_obj=generate_personal_message(p); return gr.update(value=msg_obj["message"], visible=True), rt
 
-    ev_rec = ev_start.then(
-        predict_recs_only,
-        inputs=[run_token, interests, occasion, budget_min, budget_max, recipient_name, relationship, age, gender, tone],
-        outputs=[out_top3, run_token],
-        queue=True,
-    )
-    ev_diy = ev_start.then(
-        predict_diy_only,
-        inputs=[run_token, interests, occasion, budget_min, budget_max, recipient_name, relationship, age, gender, tone],
-        outputs=[out_diy_md, run_token],
-        queue=True,
-    )
-    ev_msg = ev_start.then(
-        predict_msg_only,
-        inputs=[run_token, interests, occasion, budget_min, budget_max, recipient_name, relationship, age, gender, tone],
-        outputs=[out_msg, run_token],
-        queue=True,
-    )
+    ev_start=go.click(start_run, inputs=[run_token], outputs=[run_token], queue=True)
+    ev_start.then(predict_recs_only, inputs=[run_token, interests, occasion, budget_min, budget_max, recipient_name, relationship, age, gender, tone], outputs=[out_top3, run_token], queue=True)
+    ev_start.then(predict_diy_only, inputs=[run_token, interests, occasion, budget_min, budget_max, recipient_name, relationship, age, gender, tone], outputs=[out_diy_md, run_token], queue=True)
+    ev_start.then(predict_msg_only, inputs=[run_token, interests, occasion, budget_min, budget_max, recipient_name, relationship, age, gender, tone], outputs=[out_msg, run_token], queue=True)
 
-if __name__ == "__main__":
+if __name__=="__main__":
     demo.launch()