Update app.py

app.py

@@ -1,13 +1,15 @@
 # app.py
-# 🎁 GIfty — Smart Gift Recommender (Embeddings + FAISS + LLM generator)
-# Dataset: ckandemir/amazon-products (Hugging Face)
+# 🎁 GIfty — Smart Gift Recommender (Embeddings + FAISS + LLM)
+# Dataset: ckandemir/amazon-products
 # UI: Gradio (English)
 #
-# Notes:
-# - Embeddings: sentence-transformers/all-MiniLM-L6-v2 + FAISS IndexFlatIP
-# - LLM generator: google/flan-t5-small (local transformers, no API keys)
-# - Budget uses two sliders (compatible with older Gradio).
-# - Images are rendered as right-side thumbnails per result.
+# Features:
+# - Sentence-Transformers (MiniLM) + FAISS (cosine via normalized embeddings)
+# - LLM generator (Flan-T5-small) for the 4th gift + greeting
+# - Relationship & Tone inputs that affect both retrieval weighting and LLM outputs
+# - Image thumbnails on the right
+# - Quick Examples placed visually at the top via CSS order
+# - Budget range: RangeSlider if available, else two Sliders as fallback
 
 import os, re, json, random
 from typing import Dict, List, Tuple
@@ -18,12 +20,10 @@ import gradio as gr
 from datasets import load_dataset
 from sentence_transformers import SentenceTransformer
 import faiss
-
-# LLM (Flan-T5) for generation
 from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
 
-# ---------------- Config ----------------
-MAX_ROWS = int(os.getenv("MAX_ROWS", "6000"))  # keep index build fast on CPU
+# --------------------- Config ---------------------
+MAX_ROWS = int(os.getenv("MAX_ROWS", "6000"))
 TITLE = "# 🎁 GIfty — Smart Gift Recommender\n*Top-3 similar picks + 1 generated idea + personalized message*"
 
 OCCASION_OPTIONS = [
@@ -31,6 +31,11 @@ OCCASION_OPTIONS = [
     "housewarming", "christmas", "hanukkah", "thank_you",
 ]
 
+RELATIONSHIP_OPTIONS = [
+    "friend", "close friend", "partner/spouse", "family", "parent",
+    "sibling", "child", "colleague", "manager", "client", "teacher"
+]
+
 AGE_OPTIONS = {
     "any": "any",
     "kid (3–12)": "kids",
@@ -41,6 +46,11 @@ AGE_OPTIONS = {
 
 GENDER_OPTIONS = ["any", "female", "male", "nonbinary"]
 
+TONE_OPTIONS = [
+    "warm and friendly", "heartfelt and emotional", "playful and fun",
+    "formal and polite", "professional", "minimalist and concise"
+]
+
 INTEREST_OPTIONS = [
     "reading","writing","tech","travel","fitness","cooking","tea","coffee",
     "games","movies","plants","music","design","stationery","home","experience",
@@ -48,7 +58,7 @@ INTEREST_OPTIONS = [
     "photography","outdoors","pets","beauty","jewelry"
 ]
 
-# Query-expansion dictionary (helps matching catalog wording)
+# Query expansion (helps match catalog wording)
 SYNONYMS = {
     "music": ["audio", "headphones", "vinyl", "earbuds", "speaker"],
     "tech": ["electronics", "gadgets", "computer", "smart", "device"],
@@ -57,11 +67,11 @@ SYNONYMS = {
     "cooking": ["kitchen", "cookware", "chef", "bake"],
     "fitness": ["sports", "yoga", "run", "workout"],
     "photography": ["camera", "lens", "tripod"],
-    "travel": ["luggage", "passport", "map", "travel"],
+    "travel": ["luggage", "passport", "map"],
     "beauty": ["skincare", "makeup", "fragrance", "cosmetic"],
     "jewelry": ["ring", "necklace", "bracelet"],
-    "coffee": ["espresso", "mug", "grinder", "coffee"],
-    "tea": ["teapot", "infuser", "tea"],
+    "coffee": ["espresso", "mug", "grinder"],
+    "tea": ["teapot", "infuser"],
     "plants": ["garden", "planter", "indoor"],
     "reading": ["book", "novel", "literature"],
     "writing": ["notebook", "pen", "planner"],
@@ -72,13 +82,11 @@ SYNONYMS = {
     "experience": ["voucher", "ticket", "workshop"],
 }
 
-# ---------------- Data loading & schema ----------------
+# --------------------- Data loading & schema ---------------------
 def _to_price_usd(x):
-    s = str(x).strip().replace("$", "").replace(",", "")
-    try:
-        return float(s)
-    except Exception:
-        return np.nan
+    s = str(x).strip().replace("$","").replace(",","")
+    try: return float(s)
+    except: return np.nan
 
 def _infer_age_from_category(cat: str) -> str:
     s = (cat or "").lower()
@@ -120,12 +128,10 @@ def map_amazon_to_schema(df_raw: pd.DataFrame) -> pd.DataFrame:
         "persona_fit": get("category"),
         "image_url": get("image") if "image" in cols else "",
     })
-    # clean
     out["name"] = out["name"].astype(str).str.strip().str.slice(0, 120)
     out["short_desc"] = out["short_desc"].astype(str).str.strip().str.slice(0, 500)
     out["tags"] = out["tags"].astype(str).str.replace("|", ", ").str.lower()
     out["persona_fit"] = out["persona_fit"].astype(str).str.lower()
-    # infer occasion & age
     out["occasion_tags"] = out["tags"].map(_infer_occasion_tags)
     out["age_range"]    = out["tags"].map(_infer_age_from_category).fillna("any")
     return out
@@ -146,7 +152,6 @@ def load_catalog() -> pd.DataFrame:
         ds = load_dataset("ckandemir/amazon-products", split="train")
         raw = ds.to_pandas()
     except Exception:
-        # Fallback (keeps the app alive if internet is blocked)
         raw = pd.DataFrame({
             "Product Name": ["Wireless Earbuds", "Coffee Sampler", "Strategy Board Game"],
             "Description": [
@@ -166,25 +171,12 @@ def load_catalog() -> pd.DataFrame:
 
 CATALOG = load_catalog()
 
-# ---------------- Business filters ----------------
+# --------------------- Business filters ---------------------
 def _contains_ci(series: pd.Series, needle: str) -> pd.Series:
     if not needle: return pd.Series(True, index=series.index)
     pat = re.escape(needle)
     return series.fillna("").str.contains(pat, case=False, regex=True)
 
-def gender_tokens(gender: str) -> List[str]:
-    gender = (gender or "any").lower()
-    if gender == "female": return ["women", "woman", "female", "her"]
-    if gender == "male": return ["men", "man", "male", "him"]
-    if gender == "nonbinary": return ["unisex", "gender neutral", "they"]
-    return ["unisex"]  # "any"
-
-def soft_gender_boost(row: pd.Series, gender: str) -> float:
-    if not gender or gender == "any": return 0.0
-    tokens = gender_tokens(gender)
-    blob = f"{row.get('tags','')} {row.get('short_desc','')}".lower()
-    return 0.08 if any(t in blob for t in tokens) else 0.0
-
 def filter_business(df: pd.DataFrame, budget_min=None, budget_max=None,
                     occasion: str=None, age_range: str="any") -> pd.DataFrame:
     m = pd.Series(True, index=df.index)
@@ -198,7 +190,7 @@ def filter_business(df: pd.DataFrame, budget_min=None, budget_max=None,
         m &= (df["age_range"].fillna("any").isin([age_range, "any"]))
     return df[m]
 
-# ---------------- Embeddings + FAISS (MiniLM) ----------------
+# --------------------- Embeddings + FAISS ---------------------
 class EmbeddingIndex:
     def __init__(self, docs: List[str], model_id: str):
         self.model_id = model_id
@@ -213,10 +205,24 @@ class EmbeddingIndex:
         sims, idxs = self.index.search(qv, topn)
         return sims[0], idxs[0]
 
-EMBED_MODEL_ID = "sentence-transformers/all-MiniLM-L6-v2"  # fast & solid on CPU
+EMBED_MODEL_ID = "sentence-transformers/all-MiniLM-L6-v2"  # best balance CPU speed/quality
 EMB_INDEX = EmbeddingIndex(CATALOG["doc"].tolist(), EMBED_MODEL_ID)
 
-# ---------------- Query building ----------------
+# --------------------- Query building ---------------------
+REL_TO_TOKENS = {
+    "partner/spouse": ["romantic", "couple"],
+    "close friend": ["personal", "fun"],
+    "friend": ["friendly"],
+    "family": ["family"],
+    "parent": ["parent"],
+    "sibling": ["sibling"],
+    "child": ["kids", "play"],
+    "colleague": ["office", "work"],
+    "manager": ["professional"],
+    "client": ["professional", "thank_you"],
+    "teacher": ["teacher", "thank_you"]
+}
+
 def expand_with_synonyms(tokens: List[str]) -> List[str]:
     out = []
     for t in tokens:
@@ -227,15 +233,18 @@ def expand_with_synonyms(tokens: List[str]) -> List[str]:
     return out
 
 def profile_to_query(profile: Dict) -> str:
-    # Weighted, doc-aligned query (interests + synonyms) + occasion + age + gender signal
+    """Weighted, doc-aligned query (interests+synonyms) + occasion + age + gender + relationship."""
     interests = [t.strip().lower() for t in profile.get("interests", []) if t.strip()]
     expanded = expand_with_synonyms(interests)
     expanded = expanded + expanded  # weight x2
     occasion = (profile.get("occasion", "") or "").lower()
     age = profile.get("age_range", "any")
     gender = (profile.get("gender", "any") or "any").lower()
+    rel = (profile.get("relationship","friend") or "friend").lower()
+    rel_tokens = REL_TO_TOKENS.get(rel, [])
     parts = []
     if expanded: parts.append(", ".join(expanded))
+    if rel_tokens: parts.append(", ".join(rel_tokens))
     if occasion: parts.append(occasion)
     if age and age != "any": parts.append(age)
     if gender and gender != "any":
@@ -259,16 +268,25 @@ def recommend_topk(profile: Dict, k: int=3) -> pd.DataFrame:
     if df_f.empty:
         df_f = CATALOG
 
-    # Gather candidates within the subset and apply a small gender boost
+    # Small gender-aware re-ranking
+    def gender_tokens(g: str) -> List[str]:
+        g = (g or "any").lower()
+        if g == "female": return ["women", "woman", "female", "her"]
+        if g == "male": return ["men", "man", "male", "him"]
+        if g == "nonbinary": return ["unisex", "gender neutral", "they"]
+        return ["unisex"]
+
+    gts = gender_tokens(profile.get("gender","any"))
     cand = []
     for i, sim in zip(idxs, sims):
         i = int(i)
         if i in df_f.index:
-            boost = soft_gender_boost(CATALOG.loc[i], profile.get("gender","any"))
+            blob = f"{CATALOG.loc[i, 'tags']} {CATALOG.loc[i, 'short_desc']}".lower()
+            boost = 0.08 if any(t in blob for t in gts) else 0.0
             cand.append((i, float(sim) + boost))
     cand.sort(key=lambda x: -x[1])
 
-    # Pick unique by name
+    # Unique by name
     seen, picks = set(), []
     for gi, score in cand:
         nm = CATALOG.loc[gi, "name"]
@@ -288,7 +306,7 @@ def recommend_topk(profile: Dict, k: int=3) -> pd.DataFrame:
     res["similarity"] = [sim_map.get(int(gi), np.nan) for gi in sel]
     return res[["name","short_desc","price_usd","occasion_tags","persona_fit","age_range","image_url","similarity"]]
 
-# ---------------- LLM generator (Flan-T5) ----------------
+# --------------------- LLM generator (Flan-T5-small) ---------------------
 LLM_ID = "google/flan-t5-small"
 try:
     _tok = AutoTokenizer.from_pretrained(LLM_ID)
@@ -296,7 +314,7 @@ try:
     LLM = pipeline("text2text-generation", model=_mdl, tokenizer=_tok)
 except Exception as e:
     LLM = None
-    print("LLM load failed, will fallback to rule-based. Error:", e)
+    print("LLM load failed, fallback to rule-based. Error:", e)
 
 def _run_llm(prompt: str, max_new_tokens=128) -> str:
     if LLM is None:
@@ -308,7 +326,6 @@ def _parse_json_maybe(s: str) -> dict:
     try:
         return json.loads(s)
     except Exception:
-        # try to extract {...}
         m = re.search(r"\{.*\}", s, flags=re.S)
         if m:
             try:
@@ -318,33 +335,32 @@ def _parse_json_maybe(s: str) -> dict:
         return {}
 
 def llm_generate_item(profile: Dict) -> Dict:
-    # Prompt to produce a single gift idea JSON
     prompt = f"""
 You are GIfty, a gift recommender. Create ONE gift idea as JSON with keys:
 name, short_desc, price_usd, occasion_tags, persona_fit.
 Constraints:
-- Fit the recipient profile.
-- price_usd must be a number within the budget range.
+- Fit the recipient profile and relationship.
+- price_usd must be numeric and within the given budget range.
 - Keep text concise, friendly, and realistic.
 
-Recipient profile:
+Recipient:
+name = {profile.get('recipient_name','Friend')}
+relationship = {profile.get('relationship','friend')}
+gender = {profile.get('gender','any')}
+age_group = {profile.get('age_range','any')}
 interests = {profile.get('interests', [])}
 occasion = {profile.get('occasion','birthday')}
-age_group = {profile.get('age_range','any')}
-gender = {profile.get('gender','any')}
 budget_min = {profile.get('budget_min', 10)}
 budget_max = {profile.get('budget_max', 100)}
 
 Return ONLY JSON.
 """
-    txt = _run_llm(prompt, max_new_tokens=160)
+    txt = _run_llm(prompt, max_new_tokens=180)
     data = _parse_json_maybe(txt)
     if not data:
-        # fallback rule-based if LLM unavailable or malformed
-        core = (profile.get("interests",[ "hobby" ])[0] or "hobby").strip()
-        name = f"Custom {core} accessory with initials ({profile.get('occasion','birthday')})"
+        core = (profile.get("interests",["hobby"])[0] or "hobby").strip()
         return {
-            "name": name,
+            "name": f"Custom {core} accessory with initials ({profile.get('occasion','birthday')})",
             "short_desc": f"Thoughtful personalized {core} accessory tailored to their taste.",
             "price_usd": float(np.clip(profile.get("budget_max", 50) or 50, 10, 300)),
             "occasion_tags": profile.get("occasion","birthday"),
@@ -352,7 +368,6 @@ Return ONLY JSON.
             "age_range": profile.get("age_range","any"),
             "image_url": ""
         }
-    # ensure numeric price and bounds
     try:
         p = float(data.get("price_usd", profile.get("budget_max", 50)))
     except Exception:
@@ -370,31 +385,30 @@ Return ONLY JSON.
 
 def llm_generate_message(profile: Dict) -> str:
     prompt = f"""
-Write a short, warm greeting message (2–3 sentences) in English for a gift card.
+Write a {profile.get('tone','warm and friendly')} greeting in English (2–3 short sentences) for a gift card.
+Use the relationship to set the level of warmth/formality.
 Recipient name: {profile.get('recipient_name','Friend')}
+Relationship: {profile.get('relationship','friend')}
 Occasion: {profile.get('occasion','birthday')}
 Interests: {', '.join(profile.get('interests', []))}
 Age group: {profile.get('age_range','any')}
 Gender: {profile.get('gender','any')}
-Tone: {profile.get('tone','warm and friendly')}
-Avoid emojis. Keep it sincere and concise.
+Avoid emojis.
 """
     txt = _run_llm(prompt, max_new_tokens=90)
     if not txt:
-        # fallback
-        return (f"Dear {profile.get('recipient_name','Friend')},\n"
-                f"Happy {profile.get('occasion','birthday')}! Wishing you health, joy, and wonderful memories. "
+        return (f"Dear {profile.get('recipient_name','Friend')}, "
+                f"happy {profile.get('occasion','birthday')}! Wishing you health, joy, and wonderful memories. "
                 f"With {profile.get('tone','warm and friendly')}.")
     return txt.strip()
 
-# ---------------- Rendering helpers (HTML with right-side thumbnail) ----------------
+# --------------------- Rendering (HTML cards with right thumbnail) ---------------------
 def md_escape(text: str) -> str:
     return str(text).replace("|","\\|").replace("*","\\*").replace("_","\\_")
 
 def render_top3_html(df: pd.DataFrame) -> str:
     if df is None or df.empty:
         return "<em>No results found.</em>"
-    # Simple cards with image on the right
     rows = []
     for _, r in df.iterrows():
         name = md_escape(r.get("name",""))
@@ -407,7 +421,7 @@ def render_top3_html(df: pd.DataFrame) -> str:
         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"""
-<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;">
+<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="font-size:0.95em;margin-top:4px;">{desc}</div>
@@ -421,87 +435,144 @@ def render_top3_html(df: pd.DataFrame) -> str:
         rows.append(card)
     return "\n".join(rows)
 
-# ---------------- Gradio UI ----------------
-EXAMPLES = [
-    [["tech","music"], "birthday", 20, 60, "Noa", "adult (18–64)", "any", "warm and friendly"],
-    [["home","cooking","practical"], "housewarming", 25, 45, "Daniel", "adult (18–64)", "male", "warm"],
-    [["games","photography"], "birthday", 30, 120, "Omer", "teen (13–17)", "male", "fun"],
-    [["reading","design","aesthetic"], "thank_you", 15, 35, "Maya", "any", "female", "friendly"],
-]
+# --------------------- Gradio UI ---------------------
+CSS = """
+#examples { order: 1; }
+#form { order: 2; }
+"""
 
-def ui_predict(interests_list: List[str], occasion: str, budget_min: float, budget_max: float,
-               recipient_name: str, age_label: str, gender: str, tone: str):
-    try:
-        # sanity
-        if budget_min is None: budget_min = 20.0
-        if budget_max is None: budget_max = 60.0
-        if budget_min > budget_max:
-            budget_min, budget_max = budget_max, budget_min
+with gr.Blocks(css=CSS) as demo:
+    gr.Markdown(TITLE)
 
-        age_range = AGE_OPTIONS.get(age_label, "any")
+    # We'll build the form first (so we can reference components), but show Examples on top via CSS order.
+    with gr.Column(elem_id="examples"):
+        gr.Markdown("### Quick examples")
+        # Placeholders; we will link them after creating components.
+        # (We will create Examples at the end once components exist.)
+
+    with gr.Column(elem_id="form"):
+        with gr.Row():
+            recipient_name = gr.Textbox(label="Recipient name", value="Noa")
+            relationship = gr.Dropdown(label="Relationship", choices=RELATIONSHIP_OPTIONS, value="friend")
+
+        with gr.Row():
+            interests = gr.CheckboxGroup(
+                label="Interests (select a few)",
+                choices=INTEREST_OPTIONS,
+                value=["tech","music"],
+                interactive=True
+            )
+
+        with gr.Row():
+            occasion = gr.Dropdown(label="Occasion", choices=OCCASION_OPTIONS, 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="any")
+
+        # Budget: RangeSlider if available, else two sliders fallback
+        RangeSlider = getattr(gr, "RangeSlider", None)
+        if RangeSlider is not None:
+            budget_range = RangeSlider(label="Budget range (USD)", minimum=5, maximum=500, step=1, value=[20, 60])
+            budget_min, budget_max = None, None  # placeholders for signature compatibility
+        else:
+            with gr.Row():
+                budget_min = gr.Slider(label="Min budget (USD)", minimum=5, maximum=500, step=1, value=20)
+                budget_max = gr.Slider(label="Max budget (USD)", minimum=5, maximum=500, step=1, value=60)
+            budget_range = gr.State(value=None)
+
+        tone = gr.Dropdown(label="Message tone", choices=TONE_OPTIONS, value="warm and friendly")
+
+        go = gr.Button("Get GIfty 🎯")
+
+        out_top3 = gr.HTML(label="Top-3 recommendations")
+        out_gen  = gr.Markdown(label="Generated item")
+        out_msg  = gr.Markdown(label="Personalized message")
+
+        # Now that all inputs exist, render Examples at the top container:
+        EXAMPLES = [
+            # interests, occasion, (budget), (or min,max), name, relationship, age, gender, tone
+            [["tech","music"], "birthday", [20, 60] if RangeSlider else None, 20 if budget_min else None, 60 if budget_max else None, "Noa", "friend", "adult (18–64)", "any", "warm and friendly"],
+            [["home","cooking","practical"], "housewarming", [25, 45] if RangeSlider else None, 25 if budget_min else None, 45 if budget_max else None, "Daniel", "colleague", "adult (18–64)", "male", "professional"],
+            [["games","photography"], "birthday", [30, 120] if RangeSlider else None, 30 if budget_min else None, 120 if budget_max else None, "Omer", "close friend", "teen (13–17)", "male", "playful and fun"],
+            [["reading","design","aesthetic"], "thank_you", [15, 35] if RangeSlider else None, 15 if budget_min else None, 35 if budget_max else None, "Maya", "partner/spouse", "any", "female", "heartfelt and emotional"],
+        ]
+
+        # Build the list of components according to the active budget control
+        if RangeSlider:
+            example_inputs = [interests, occasion, budget_range, recipient_name, relationship, age, gender, tone]
+        else:
+            example_inputs = [interests, occasion, budget_min, budget_max, recipient_name, relationship, age, gender, tone]
+
+    # Insert the Examples widget into the top column now
+    with gr.Column(elem_id="examples"):
+        gr.Examples(EXAMPLES, inputs=example_inputs)
+
+    # --------- Predict function wiring ----------
+    def ui_predict(
+        interests_list: List[str], occasion_val: str,
+        budget_rng_or_min,  # either [min,max] or min
+        maybe_max_or_name,  # when RangeSlider -> recipient_name; else -> budget_max
+        maybe_name_or_rel,  # when RangeSlider -> relationship; else -> recipient_name
+        rel_or_age,         # when RangeSlider -> age; else -> relationship
+        age_or_gender,      # when RangeSlider -> gender; else -> age
+        gender_or_tone,     # when RangeSlider -> tone; else -> gender
+        tone_maybe=None
+    ):
+        # Disambiguate inputs based on whether we used RangeSlider or not
+        use_range = isinstance(budget_rng_or_min, (list, tuple))
+        if use_range:
+            budget_min_val = float(budget_rng_or_min[0])
+            budget_max_val = float(budget_rng_or_min[1])
+            recipient_name_val = str(maybe_max_or_name or "Friend")
+            relationship_val = str(maybe_name_or_rel or "friend")
+            age_label_val = str(rel_or_age or "any")
+            gender_val = str(age_or_gender or "any")
+            tone_val = str(gender_or_tone or "warm and friendly")
+        else:
+            budget_min_val = float(budget_rng_or_min if budget_rng_or_min is not None else 20)
+            budget_max_val = float(maybe_max_or_name if maybe_max_or_name is not None else 60)
+            recipient_name_val = str(maybe_name_or_rel or "Friend")
+            relationship_val = str(rel_or_age or "friend")
+            age_label_val = str(age_or_gender or "any")
+            gender_val = str(gender_or_tone or "any")
+            tone_val = str(tone_maybe or "warm and friendly")
+
+        if budget_min_val > budget_max_val:
+            budget_min_val, budget_max_val = budget_max_val, budget_min_val
+
+        age_range = AGE_OPTIONS.get(age_label_val, "any")
         profile = {
-            "recipient_name": recipient_name or "Friend",
+            "recipient_name": recipient_name_val or "Friend",
+            "relationship": relationship_val or "friend",
             "interests": interests_list or [],
-            "occasion": occasion or "birthday",
-            "budget_min": float(budget_min),
-            "budget_max": float(budget_max),
-            "budget_usd": float(budget_max),
+            "occasion": occasion_val or "birthday",
+            "budget_min": budget_min_val,
+            "budget_max": budget_max_val,
+            "budget_usd": budget_max_val,
             "age_range": age_range,
-            "gender": gender or "any",
-            "tone": tone or "warm and friendly",
+            "gender": gender_val or "any",
+            "tone": tone_val or "warm and friendly",
         }
 
+        # Retrieval + generation
         top3 = recommend_topk(profile, k=3)
         gen  = llm_generate_item(profile)
         msg  = llm_generate_message(profile)
 
-        top3_html = render_top3_html(top3)
-        gen_md  = f"**{md_escape(gen['name'])}**\n\n{md_escape(gen['short_desc'])}\n\n~${gen['price_usd']:.0f}"
-        return top3_html, gen_md, msg
-    except Exception as e:
-        return f"<div style='color:#b00;'>⚠️ Error: {e}</div>", "", ""
+        return render_top3_html(top3), f"**{md_escape(gen['name'])}**\n\n{md_escape(gen['short_desc'])}\n\n~${gen['price_usd']:.0f}", msg
 
-with gr.Blocks() as demo:
-    gr.Markdown(TITLE)
-
-    with gr.Row():
-        interests = gr.CheckboxGroup(
-            label="Interests (select a few)",
-            choices=INTEREST_OPTIONS,
-            value=["tech","music"],
-            interactive=True
+    # Wire the button
+    if RangeSlider:
+        go.click(
+            ui_predict,
+            [interests, occasion, budget_range, recipient_name, relationship, age, gender, tone],
+            [out_top3, out_gen, out_msg]
+        )
+    else:
+        go.click(
+            ui_predict,
+            [interests, occasion, budget_min, budget_max, recipient_name, relationship, age, gender, tone],
+            [out_top3, out_gen, out_msg]
         )
-    with gr.Row():
-        occasion = gr.Dropdown(label="Occasion", choices=OCCASION_OPTIONS, 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="any")
-
-    # Two budget sliders (compatible with older Gradio)
-    with gr.Row():
-        budget_min = gr.Slider(label="Min budget (USD)", minimum=5, maximum=500, step=1, value=20)
-        budget_max = gr.Slider(label="Max budget (USD)", minimum=5, maximum=500, step=1, value=60)
-
-    with gr.Row():
-        recipient_name = gr.Textbox(label="Recipient name", value="Noa")
-        tone = gr.Textbox(label="Message tone", value="warm and friendly")
-
-    go = gr.Button("Get GIfty 🎯")
-
-    out_top3 = gr.HTML(label="Top-3 recommendations")   # HTML to support right-side thumbnails
-    out_gen  = gr.Markdown(label="Generated item")
-    out_msg  = gr.Markdown(label="Personalized message")
-
-    gr.Examples(
-        EXAMPLES,
-        [interests, occasion, budget_min, budget_max, recipient_name, age, gender, tone],
-        label="Quick examples",
-    )
-
-    go.click(
-        ui_predict,
-        [interests, occasion, budget_min, budget_max, recipient_name, age, gender, tone],
-        [out_top3, out_gen, out_msg]
-    )
 
 if __name__ == "__main__":
     demo.launch()