Update app.py

app.py

@@ -1,15 +1,9 @@
 # app.py
-# 🎁 GIfty — Smart Gift Recommender (Embeddings + FAISS + LLM)
-# Dataset: ckandemir/amazon-products
-# UI: Gradio (English)
-#
-# 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
+# 🎁 GIfty — Smart Gift Recommender (Embeddings + FAISS + LLM + Image Gen)
+# Data: ckandemir/amazon-products
+# Retrieval: MiniLM embeddings + FAISS (cosine)
+# Generation: Flan-T5-small (text), SD-Turbo (image)
+# UI: Gradio; Quick Examples on top; Budget range: RangeSlider if present, else two sliders
 
 import os, re, json, random
 from typing import Dict, List, Tuple
@@ -18,68 +12,119 @@ import numpy as np
 import pandas as pd
 import gradio as gr
 from datasets import load_dataset
+
 from sentence_transformers import SentenceTransformer
 import faiss
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+
+import torch
+from diffusers import AutoPipelineForText2Image
 
 # --------------------- Config ---------------------
-MAX_ROWS = int(os.getenv("MAX_ROWS", "6000"))
-TITLE = "# 🎁 GIfty — Smart Gift Recommender\n*Top-3 similar picks + 1 generated idea + personalized message*"
+MAX_ROWS = int(os.getenv("MAX_ROWS", "8000"))
+TITLE = "# 🎁 GIfty — Smart Gift Recommender\n*Top-3 similar picks + 1 invented gift (with image) + personalized message*"
 
-OCCASION_OPTIONS = [
-    "birthday", "anniversary", "valentines", "graduation",
-    "housewarming", "christmas", "hanukkah", "thank_you",
+# ===== Updated Interests (exact) =====
+INTEREST_OPTIONS = [
+    "Sports","Travel","Cooking","Technology","Music","Art","Reading","Gardening","Fashion",
+    "Gaming","Photography","Hiking","Movies","Crafts","Pets","Wellness","Collecting","Food",
+    "Home decor","Science"
 ]
 
-RELATIONSHIP_OPTIONS = [
-    "friend", "close friend", "partner/spouse", "family", "parent",
-    "sibling", "child", "colleague", "manager", "client", "teacher"
+# ===== Updated Occasions (exact) =====
+OCCASION_UI = [
+    "Birthday","Wedding / Engagement","Anniversary","Graduation","New baby","Housewarming",
+    "Retirement","Holidays","Valentine’s Day","Promotion / New job","Get well soon"
 ]
-
-AGE_OPTIONS = {
-    "any": "any",
-    "kid (3–12)": "kids",
-    "teen (13–17)": "teens",
-    "adult (18–64)": "adult",
-    "senior (65+)": "senior",
+# Canonical tokens used in filtering/query
+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"
 }
 
-GENDER_OPTIONS = ["any", "female", "male", "nonbinary"]
-
-TONE_OPTIONS = [
-    "warm and friendly", "heartfelt and emotional", "playful and fun",
-    "formal and polite", "professional", "minimalist and concise"
+# ===== Updated Relationship & Tone =====
+RECIPIENT_RELATIONSHIPS = [
+    "Family - Parent",
+    "Family - Sibling",
+    "Family - Child",
+    "Family - Other relative",
+    "Friend",
+    "Colleague",
+    "Boss",
+    "Romantic partner",
+    "Teacher / Mentor",
+    "Neighbor",
+    "Client / Business partner",
 ]
 
-INTEREST_OPTIONS = [
-    "reading","writing","tech","travel","fitness","cooking","tea","coffee",
-    "games","movies","plants","music","design","stationery","home","experience",
-    "digital","aesthetic","premium","eco","practical","minimalist","social","party",
-    "photography","outdoors","pets","beauty","jewelry"
+MESSAGE_TONES = [
+    "Formal",
+    "Casual",
+    "Funny",
+    "Heartfelt",
+    "Inspirational",
+    "Playful",
+    "Romantic",
+    "Appreciative",
+    "Encouraging",
 ]
 
-# Query expansion (helps match catalog wording)
+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 = {
-    "music": ["audio", "headphones", "vinyl", "earbuds", "speaker"],
-    "tech": ["electronics", "gadgets", "computer", "smart", "device"],
-    "games": ["board game", "puzzle", "gaming", "toy"],
-    "home": ["home decor", "kitchen", "appliance", "furniture"],
-    "cooking": ["kitchen", "cookware", "chef", "bake"],
-    "fitness": ["sports", "yoga", "run", "workout"],
-    "photography": ["camera", "lens", "tripod"],
-    "travel": ["luggage", "passport", "map"],
-    "beauty": ["skincare", "makeup", "fragrance", "cosmetic"],
-    "jewelry": ["ring", "necklace", "bracelet"],
-    "coffee": ["espresso", "mug", "grinder"],
-    "tea": ["teapot", "infuser"],
-    "plants": ["garden", "planter", "indoor"],
-    "reading": ["book", "novel", "literature"],
-    "writing": ["notebook", "pen", "planner"],
-    "pets": ["pet", "dog", "cat"],
-    "outdoors": ["camping", "hiking", "outdoor"],
-    "eco": ["sustainable", "recycled", "eco"],
-    "digital": ["online", "voucher"],
-    "experience": ["voucher", "ticket", "workshop"],
+    "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"],
+}
+
+# Relationship tokens (soft guidance to retrieval)
+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 ---------------------
@@ -90,28 +135,34 @@ def _to_price_usd(x):
 
 def _infer_age_from_category(cat: str) -> str:
     s = (cat or "").lower()
-    if any(k in s for k in ["baby", "toddler", "infant"]): return "kids"
+    if any(k in s for k in ["baby","toddler","infant"]): return "kids"
     if "toys & games" in s or "board games" in s or "toy" in s: return "kids"
-    if any(k in s for k in ["teen", "young adult", "ya"]): return "teens"
+    if any(k in s for k in ["teen","young adult","ya"]): return "teens"
     return "any"
 
 def _infer_occasion_tags(cat: str) -> str:
     s = (cat or "").lower()
-    tags = set(["birthday"])
-    if any(k in s for k in ["home & kitchen","furniture","home décor","home decor","garden","tools","appliance","cookware","kitchen"]):
-        tags.update(["housewarming","thank_you"])
+    tags = set(["birthday"])  # default
+    if any(k in s for k in ["home & kitchen","furniture","home décor","home decor","garden","appliance","cookware","kitchen"]):
+        tags.update(["housewarming"])
     if any(k in s for k in ["beauty","jewelry","watch","fragrance","cosmetic","makeup","skincare"]):
         tags.update(["valentines","anniversary"])
-    if any(k in s for k in ["toys","board game","puzzle","kids","lego"]):
-        tags.update(["hanukkah","christmas"])
+    if any(k in s for k in ["toys","board game","puzzle","lego","kids"]):
+        tags.update(["holidays"])
     if any(k in s for k in ["office","stationery","notebook","pen","planner"]):
-        tags.update(["graduation","thank_you"])
+        tags.update(["graduation","promotion"])
     if any(k in s for k in ["electronics","camera","audio","headphones","gaming","computer"]):
-        tags.update(["birthday","christmas"])
+        tags.update(["holidays"])
     if any(k in s for k in ["book","novel","literature"]):
-        tags.update(["graduation","thank_you"])
-    if any(k in s for k in ["sports","fitness","outdoor","camping","hiking","run","yoga"]):
-        tags.update(["birthday"])
+        tags.update(["graduation"])
+    if any(k in s for k in ["baby","maternity","newborn","stroller"]):
+        tags.update(["new_baby"])
+    if any(k in s for k in ["wedding","engagement","bridal"]):
+        tags.update(["wedding"])
+    if any(k in s for k in ["retirement","senior gifts"]):
+        tags.update(["retirement"])
+    if any(k in s for k in ["health","wellness","get well","recovery"]):
+        tags.update(["get_well"])
     return ",".join(sorted(tags))
 
 def map_amazon_to_schema(df_raw: pd.DataFrame) -> pd.DataFrame:
@@ -133,19 +184,18 @@ def map_amazon_to_schema(df_raw: pd.DataFrame) -> pd.DataFrame:
     out["tags"] = out["tags"].astype(str).str.replace("|", ", ").str.lower()
     out["persona_fit"] = out["persona_fit"].astype(str).str.lower()
     out["occasion_tags"] = out["tags"].map(_infer_occasion_tags)
-    out["age_range"]    = out["tags"].map(_infer_age_from_category).fillna("any")
+    out["age_range"] = out["tags"].map(_infer_age_from_category).fillna("any")
     return out
 
 def build_doc(row: pd.Series) -> str:
-    parts = [
+    return " | ".join([
         str(row.get("name","")),
         str(row.get("short_desc","")),
         str(row.get("tags","")),
         str(row.get("persona_fit","")),
         str(row.get("occasion_tags","")),
         str(row.get("age_range","")),
-    ]
-    return " | ".join([p for p in parts if p])
+    ])
 
 def load_catalog() -> pd.DataFrame:
     try:
@@ -153,7 +203,7 @@ def load_catalog() -> pd.DataFrame:
         raw = ds.to_pandas()
     except Exception:
         raw = pd.DataFrame({
-            "Product Name": ["Wireless Earbuds", "Coffee Sampler", "Strategy Board Game"],
+            "Product Name": ["Wireless Earbuds","Coffee Sampler","Strategy Board Game"],
             "Description": [
                 "Compact earbuds with noise isolation and long battery life.",
                 "Four single-origin roasts from small roasters.",
@@ -174,18 +224,17 @@ CATALOG = load_catalog()
 # --------------------- 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)
+    return series.fillna("").str.contains(re.escape(needle), case=False, regex=True)
 
 def filter_business(df: pd.DataFrame, budget_min=None, budget_max=None,
-                    occasion: str=None, age_range: str="any") -> pd.DataFrame:
+                    occasion_canon: str=None, age_range: str="any") -> pd.DataFrame:
     m = pd.Series(True, index=df.index)
     if budget_min is not None:
         m &= df["price_usd"].fillna(0) >= float(budget_min)
     if budget_max is not None:
         m &= df["price_usd"].fillna(1e9) <= float(budget_max)
-    if occasion:
-        m &= _contains_ci(df["occasion_tags"], occasion)
+    if occasion_canon:
+        m &= _contains_ci(df["occasion_tags"], occasion_canon)
     if age_range and age_range != "any":
         m &= (df["age_range"].fillna("any").isin([age_range, "any"]))
     return df[m]
@@ -193,36 +242,20 @@ def filter_business(df: pd.DataFrame, budget_min=None, budget_max=None,
 # --------------------- Embeddings + FAISS ---------------------
 class EmbeddingIndex:
     def __init__(self, docs: List[str], model_id: str):
-        self.model_id = model_id
         self.model = SentenceTransformer(model_id)
         embs = self.model.encode(docs, convert_to_numpy=True, normalize_embeddings=True)
-        self.index = faiss.IndexFlatIP(embs.shape[1])  # cosine if normalized
+        self.index = faiss.IndexFlatIP(embs.shape[1])  # cosine via normalized vectors
         self.index.add(embs)
-        self.dim = embs.shape[1]
 
-    def search(self, query: str, topn: int) -> Tuple[np.ndarray, np.ndarray]:
+    def search(self, query: str, topn: int):
         qv = self.model.encode([query], convert_to_numpy=True, normalize_embeddings=True)
         sims, idxs = self.index.search(qv, topn)
         return sims[0], idxs[0]
 
-EMBED_MODEL_ID = "sentence-transformers/all-MiniLM-L6-v2"  # best balance CPU speed/quality
+EMBED_MODEL_ID = "sentence-transformers/all-MiniLM-L6-v2"  # fast & solid on CPU
 EMB_INDEX = EmbeddingIndex(CATALOG["doc"].tolist(), EMBED_MODEL_ID)
 
 # --------------------- 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:
@@ -233,47 +266,39 @@ 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 + relationship."""
-    interests = [t.strip().lower() for t in profile.get("interests", []) if t.strip()]
-    expanded = expand_with_synonyms(interests)
+    inter = [i.lower() for i in profile.get("interests", []) if i]
+    expanded = expand_with_synonyms(inter)
     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, [])
+    rel_tokens = REL_TO_TOKENS.get(profile.get("relationship","Friend"), [])
     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":
-        parts.append("women" if gender=="female" else ("men" if gender=="male" else "unisex"))
-    return " | ".join(parts).strip()
+    occ = OCCASION_CANON.get(profile.get("occ_ui","Birthday"), "birthday")
+    parts.append(occ)
+    age = profile.get("age_range","any")
+    if age != "any": parts.append(age)
+    g = (profile.get("gender","any") or "any").lower()
+    if g != "any": parts.append("women" if g=="female" else ("men" if g=="male" else "unisex"))
+    return " | ".join(parts)
 
 def recommend_topk(profile: Dict, k: int=3) -> pd.DataFrame:
     query = profile_to_query(profile)
-
-    # Global search
-    sims, idxs = EMB_INDEX.search(query, topn=min(max(k*80, k), len(CATALOG)))
-
-    # Business subset
+    sims, idxs = EMBB_INDEX.search(query, topn=min(max(k*80, k), len(CATALOG))) if False else EMB_INDEX.search(query, topn=min(max(k*80, k), len(CATALOG)))
     df_f = filter_business(
         CATALOG,
         budget_min=profile.get("budget_min"),
         budget_max=profile.get("budget_max"),
-        occasion=profile.get("occasion"),
+        occasion_canon=OCCASION_CANON.get(profile.get("occ_ui","Birthday"), "birthday"),
         age_range=profile.get("age_range","any"),
     )
-    if df_f.empty:
-        df_f = CATALOG
+    if df_f.empty: df_f = CATALOG
 
-    # Small gender-aware re-ranking
+    # soft gender boost
     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"]
+        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"))
@@ -281,12 +306,11 @@ def recommend_topk(profile: Dict, k: int=3) -> pd.DataFrame:
     for i, sim in zip(idxs, sims):
         i = int(i)
         if i in df_f.index:
-            blob = f"{CATALOG.loc[i, 'tags']} {CATALOG.loc[i, 'short_desc']}".lower()
+            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])
 
-    # Unique by name
     seen, picks = set(), []
     for gi, score in cand:
         nm = CATALOG.loc[gi, "name"]
@@ -302,11 +326,10 @@ def recommend_topk(profile: Dict, k: int=3) -> pd.DataFrame:
 
     sel = [gi for gi,_ in picks]
     res = CATALOG.loc[sel].copy()
-    sim_map = dict(picks)
-    res["similarity"] = [sim_map.get(int(gi), np.nan) for gi in sel]
+    res["similarity"] = [dict(picks).get(int(i), np.nan) for i in sel]
     return res[["name","short_desc","price_usd","occasion_tags","persona_fit","age_range","image_url","similarity"]]
 
-# --------------------- LLM generator (Flan-T5-small) ---------------------
+# --------------------- LLM (text) ---------------------
 LLM_ID = "google/flan-t5-small"
 try:
     _tok = AutoTokenizer.from_pretrained(LLM_ID)
@@ -316,9 +339,8 @@ except Exception as e:
     LLM = None
     print("LLM load failed, fallback to rule-based. Error:", e)
 
-def _run_llm(prompt: str, max_new_tokens=128) -> str:
-    if LLM is None:
-        return ""
+def _run_llm(prompt: str, max_new_tokens=160) -> str:
+    if LLM is None: return ""
     out = LLM(prompt, max_new_tokens=max_new_tokens, do_sample=False, temperature=0.0)
     return out[0]["generated_text"]
 
@@ -328,42 +350,36 @@ def _parse_json_maybe(s: str) -> dict:
     except Exception:
         m = re.search(r"\{.*\}", s, flags=re.S)
         if m:
-            try:
-                return json.loads(m.group(0))
-            except Exception:
-                return {}
+            try: return json.loads(m.group(0))
+            except Exception: return {}
         return {}
 
 def llm_generate_item(profile: Dict) -> Dict:
     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.
+You are GIfty. Invent ONE gift that matches the catalog style with keys:
+name, short_desc, price_usd, occasion_tags, persona_fit. Use JSON only.
 Constraints:
 - Fit the recipient profile and relationship.
-- price_usd must be numeric and within the given budget range.
-- Keep text concise, friendly, and realistic.
-
-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')}
-budget_min = {profile.get('budget_min', 10)}
-budget_max = {profile.get('budget_max', 100)}
-
-Return ONLY JSON.
+- price_usd must be numeric within the budget range.
+Profile:
+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('occ_ui','Birthday')}
+budget_min={profile.get('budget_min',10)}
+budget_max={profile.get('budget_max',100)}
 """
     txt = _run_llm(prompt, max_new_tokens=180)
     data = _parse_json_maybe(txt)
     if not data:
-        core = (profile.get("interests",["hobby"])[0] or "hobby").strip()
+        core = (profile.get("interests",["hobby"])[0] or "hobby").lower()
         return {
-            "name": f"Custom {core} accessory with initials ({profile.get('occasion','birthday')})",
-            "short_desc": f"Thoughtful personalized {core} accessory tailored to their taste.",
+            "name": f"{core.title()} starter bundle ({profile.get('occ_ui','Birthday')})",
+            "short_desc": f"A curated set to kickstart their {core} passion.",
             "price_usd": float(np.clip(profile.get("budget_max", 50) or 50, 10, 300)),
-            "occasion_tags": profile.get("occasion","birthday"),
+            "occasion_tags": OCCASION_CANON.get(profile.get("occ_ui","Birthday"), "birthday"),
             "persona_fit": ", ".join(profile.get("interests", [])) or "general",
             "age_range": profile.get("age_range","any"),
             "image_url": ""
@@ -374,10 +390,10 @@ Return ONLY JSON.
         p = float(profile.get("budget_max", 50) or 50)
     p = float(np.clip(p, profile.get("budget_min", 10) or 10, profile.get("budget_max", 300) or 300))
     return {
-        "name": data.get("name","Gift idea"),
-        "short_desc": data.get("short_desc","A thoughtful personalized idea."),
+        "name": data.get("name","Gift Idea"),
+        "short_desc": data.get("short_desc","A thoughtful idea."),
         "price_usd": p,
-        "occasion_tags": data.get("occasion_tags", profile.get("occasion","birthday")),
+        "occasion_tags": data.get("occasion_tags", OCCASION_CANON.get(profile.get("occ_ui","Birthday"), "birthday")),
         "persona_fit": data.get("persona_fit", ", ".join(profile.get("interests", [])) or "general"),
         "age_range": profile.get("age_range","any"),
         "image_url": ""
@@ -385,24 +401,55 @@ Return ONLY JSON.
 
 def llm_generate_message(profile: Dict) -> str:
     prompt = f"""
-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')}
+Write a short greeting (2–3 sentences) in English for a gift card.
+Tone: {profile.get('tone','Heartfelt')}
+Use the relationship to set warmth/formality.
+Recipient: {profile.get('recipient_name','Friend')} ({profile.get('relationship','Friend')})
+Occasion: {profile.get('occ_ui','Birthday')}
 Interests: {', '.join(profile.get('interests', []))}
-Age group: {profile.get('age_range','any')}
-Gender: {profile.get('gender','any')}
+Age group: {profile.get('age_range','any')}; Gender: {profile.get('gender','any')}
 Avoid emojis.
 """
     txt = _run_llm(prompt, max_new_tokens=90)
     if not txt:
         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')}.")
+                f"happy {profile.get('occ_ui','Birthday').lower()}! Wishing you joy and wonderful memories.")
     return txt.strip()
 
-# --------------------- Rendering (HTML cards with right thumbnail) ---------------------
+# --------------------- Image generation (SD-Turbo) ---------------------
+def load_image_pipeline():
+    try:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+        pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sd-turbo", torch_dtype=dtype)
+        pipe.to(device)
+        return pipe
+    except Exception as e:
+        print("Image pipeline load failed:", e)
+        return None
+
+IMG_PIPE = load_image_pipeline()
+
+def generate_gift_image(gift: Dict):
+    if IMG_PIPE is None:
+        return None
+    prompt = (
+        f"{gift.get('name','gift')}, {gift.get('short_desc','')}. "
+        f"Style: product photo, soft studio lighting, minimal background, realistic, high detail."
+    )
+    try:
+        img = IMG_PIPE(
+            prompt,
+            num_inference_steps=2,
+            guidance_scale=0.0,
+            width=512, height=512
+        ).images[0]
+        return img
+    except Exception as e:
+        print("Image generation failed:", e)
+        return None
+
+# --------------------- Rendering ---------------------
 def md_escape(text: str) -> str:
     return str(text).replace("|","\\|").replace("*","\\*").replace("_","\\_")
 
@@ -444,134 +491,137 @@ CSS = """
 with gr.Blocks(css=CSS) as demo:
     gr.Markdown(TITLE)
 
-    # We'll build the form first (so we can reference components), but show Examples on top via CSS order.
+    # top section (examples placeholder)
     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")
+            relationship = gr.Dropdown(label="Relationship", choices=RECIPIENT_RELATIONSHIPS, value="Friend")
 
         with gr.Row():
             interests = gr.CheckboxGroup(
-                label="Interests (select a few)",
-                choices=INTEREST_OPTIONS,
-                value=["tech","music"],
-                interactive=True
+                label="Interests (select a few)", choices=INTEREST_OPTIONS,
+                value=["Technology","Music"], interactive=True
             )
 
         with gr.Row():
-            occasion = gr.Dropdown(label="Occasion", choices=OCCASION_OPTIONS, value="birthday")
+            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="any")
 
-        # Budget: RangeSlider if available, else two sliders fallback
+        # Budget: try RangeSlider else two sliders
         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
+            budget_min, budget_max = None, None
         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")
+        tone = gr.Dropdown(label="Message tone", choices=MESSAGE_TONES, value="Heartfelt")
 
         go = gr.Button("Get GIfty 🎯")
 
         out_top3 = gr.HTML(label="Top-3 recommendations")
-        out_gen  = gr.Markdown(label="Generated item")
+        out_gen_text  = gr.Markdown(label="Invented gift")
+        out_gen_img   = gr.Image(label="Invented gift image", type="pil")
         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
+        # examples (render on top via CSS)
         if RangeSlider:
             example_inputs = [interests, occasion, budget_range, recipient_name, relationship, age, gender, tone]
+            EXAMPLES = [
+                [["Technology","Music"], "Birthday", [20,60], "Noa", "Friend", "adult (18–64)", "any", "Heartfelt"],
+                [["Home decor","Cooking"], "Housewarming", [25,45], "Daniel", "Neighbor", "adult (18–64)", "male", "Appreciative"],
+                [["Gaming","Photography"], "Birthday", [30,120], "Omer", "Family - Sibling", "teen (13–17)", "male", "Playful"],
+                [["Reading","Art"], "Graduation", [15,35], "Maya", "Romantic partner", "any", "female", "Romantic"],
+            ]
         else:
             example_inputs = [interests, occasion, budget_min, budget_max, recipient_name, relationship, age, gender, tone]
+            EXAMPLES = [
+                [["Technology","Music"], "Birthday", 20, 60, "Noa", "Friend", "adult (18–64)", "any", "Heartfelt"],
+                [["Home decor","Cooking"], "Housewarming", 25, 45, "Daniel", "Neighbor", "adult (18–64)", "male", "Appreciative"],
+                [["Gaming","Photography"], "Birthday", 30, 120, "Omer", "Family - Sibling", "teen (13–17)", "male", "Playful"],
+                [["Reading","Art"], "Graduation", 15, 35, "Maya", "Romantic partner", "any", "female", "Romantic"],
+            ]
 
-    # Insert the Examples widget into the top column now
     with gr.Column(elem_id="examples"):
         gr.Examples(EXAMPLES, inputs=example_inputs)
 
-    # --------- Predict function wiring ----------
+    # --- predict 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
+        interests_list, occasion_val,
+        budget_rng_or_min,
+        maybe_max_or_name,
+        maybe_name_or_rel,
+        rel_or_age,
+        age_or_gender,
+        gender_or_tone,
         tone_maybe=None
     ):
-        # Disambiguate inputs based on whether we used RangeSlider or not
+        # Disambiguate RangeSlider vs two Sliders
         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")
+            bmin = float(budget_rng_or_min[0]); bmax = float(budget_rng_or_min[1])
+            name = str(maybe_max_or_name or "Friend")
+            rel  = str(maybe_name_or_rel or "Friend")
+            age_label = str(rel_or_age or "any")
             gender_val = str(age_or_gender or "any")
-            tone_val = str(gender_or_tone or "warm and friendly")
+            tone_val   = str(gender_or_tone or "Heartfelt")
         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")
+            bmin = float(budget_rng_or_min if budget_rng_or_min is not None else 20)
+            bmax = float(maybe_max_or_name if maybe_max_or_name is not None else 60)
+            name = str(maybe_name_or_rel or "Friend")
+            rel  = str(rel_or_age or "Friend")
+            age_label = str(age_or_gender or "any")
             gender_val = str(gender_or_tone or "any")
-            tone_val = str(tone_maybe or "warm and friendly")
+            tone_val   = str(tone_maybe or "Heartfelt")
 
-        if budget_min_val > budget_max_val:
-            budget_min_val, budget_max_val = budget_max_val, budget_min_val
+        if bmin > bmax: bmin, bmax = bmax, bmin
 
-        age_range = AGE_OPTIONS.get(age_label_val, "any")
+        age_range = AGE_OPTIONS.get(age_label, "any")
         profile = {
-            "recipient_name": recipient_name_val or "Friend",
-            "relationship": relationship_val or "friend",
+            "recipient_name": name,
+            "relationship": rel,
             "interests": interests_list or [],
-            "occasion": occasion_val or "birthday",
-            "budget_min": budget_min_val,
-            "budget_max": budget_max_val,
-            "budget_usd": budget_max_val,
+            "occ_ui": occasion_val or "Birthday",
+            "budget_min": bmin,
+            "budget_max": bmax,
+            "budget_usd": bmax,
             "age_range": age_range,
             "gender": gender_val or "any",
-            "tone": tone_val or "warm and friendly",
+            "tone": tone_val or "Heartfelt",
         }
 
-        # Retrieval + generation
+        # retrieval
         top3 = recommend_topk(profile, k=3)
-        gen  = llm_generate_item(profile)
-        msg  = llm_generate_message(profile)
+        top3_html = render_top3_html(top3)
+
+        # invented gift + image
+        gen = llm_generate_item(profile)
+        gen_md = f"**{md_escape(gen['name'])}**\n\n{md_escape(gen['short_desc'])}\n\n~${gen['price_usd']:.0f}"
+        gen_img = generate_gift_image(gen)
+
+        # greeting
+        msg = llm_generate_message(profile)
 
-        return render_top3_html(top3), f"**{md_escape(gen['name'])}**\n\n{md_escape(gen['short_desc'])}\n\n~${gen['price_usd']:.0f}", msg
+        return top3_html, gen_md, gen_img, msg
 
-    # 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]
+            [out_top3, out_gen_text, out_gen_img, 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]
+            [out_top3, out_gen_text, out_gen_img, out_msg]
         )
 
 if __name__ == "__main__":