Update app.py

app.py

@@ -21,7 +21,7 @@ import torch
 print(f"===== Application Startup at {pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')} =====")
 
 # --------------------- Config ---------------------
-TITLE = "# 🎁 GIfty+ — Smart Gift Recommender\n*Top-3 catalog picks + 1 DIY gift + personalized message*"
+TITLE = "# 🎁 GIfty+   Smart Gift Recommender\n*Top-3 catalog picks + 1 DIY gift + personalized message*"
 
 DATASET_ID    = os.getenv("DATASET_ID", "Danielos100/Amazon_products_clean")
 DATASET_SPLIT = os.getenv("DATASET_SPLIT", "train")
@@ -246,7 +246,6 @@ if "tok_set" not in CATALOG.columns:
     ).map(_tok_set)
 
 # ====================== Recommendations — Hybrid Ranker v2 ======================
-# דגש חזק יותר על תחביבים; Gender/Age מסננים בלבד; חיזוק Occasion; ריראנקר אופציונלי; גיוון (MMR)
 try:
     from sentence_transformers import CrossEncoder
 except Exception:
@@ -292,7 +291,6 @@ def expand_with_synonyms(tokens: List[str]) -> List[str]:
     return out
 
 def profile_to_query(profile: Dict) -> str:
-    # דגש ×3 לתחביבים; ללא אזכור גיל/מגדר בנוסח כדי לא לתת משקל סמנטי
     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"), [])
@@ -318,7 +316,6 @@ def _gender_ok_mask(gender: str) -> np.ndarray:
     return np.ones(len(blob), dtype=bool)
 
 def _mask_by_age(age: str, blob: pd.Series) -> np.ndarray:
-    # סינון בלבד (ללא עונש/משקל)
     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)
@@ -339,7 +336,7 @@ def _interest_bonus(profile: Dict, idx: np.ndarray) -> np.ndarray:
         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  # משקל חזק לתחביבים
+    return 0.10 * counts
 
 def _occasion_bonus(idx: np.ndarray, occ_ui: str) -> np.ndarray:
     slug = OCCASION_CANON.get(occ_ui or "Birthday", "birthday")
@@ -389,7 +386,6 @@ def _mmr_select(cand_idx: np.ndarray, scores: np.ndarray, k: int, lambda_: float
     return cand_idx[np.array(picked, dtype=int)]
 
 def recommend_top3_budget_first(profile: Dict) -> pd.DataFrame:
-    # 1) סינון לפי תקציב + גיל (סינון בלבד) + מגדר (סינון בלבד)
     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)
@@ -409,7 +405,6 @@ def recommend_top3_budget_first(profile: Dict) -> pd.DataFrame:
         res["similarity"] = np.nan
         return res[["name","short_desc","price_usd","image_url","similarity"]].reset_index(drop=True)
 
-    # 2) אמבדינג + מחיר + תחביבים + Occasion
     q = profile_to_query(profile)
     qv = EMB.query_vec(q).astype("float32")
     X = np.asarray(EMB.embs, dtype="float32")[idx]
@@ -424,7 +419,6 @@ def recommend_top3_budget_first(profile: Dict) -> pd.DataFrame:
 
     pre_score = emb_sims + price_bonus + int_bonus + occ_bonus
 
-    # 3) מועמדים
     K1 = min(64, idx.size)
     top_local = np.argpartition(-pre_score, K1-1)[:K1]
     cand_idx = idx[top_local]
@@ -432,9 +426,8 @@ def recommend_top3_budget_first(profile: Dict) -> pd.DataFrame:
     emb_norm   = _minmax(emb_sims[top_local])
     price_norm = _minmax(price_bonus[top_local])
     int_norm   = _minmax(int_bonus[top_local])
-    occ_norm   = _minmax(occ_bonus[top_local])
+    occ_norm   = _minmax(oc_bonus := occ_bonus[top_local])
 
-    # 4) ריראנקר אופציונלי (אם אפשר)
     try:
         from sentence_transformers import CrossEncoder as _CE
         ce = _load_cross_encoder()
@@ -448,7 +441,6 @@ def recommend_top3_budget_first(profile: Dict) -> pd.DataFrame:
     except Exception:
         ce_norm = np.zeros_like(emb_norm)
 
-    # 5) ציון סופי (דגש חזק לתחביבים דרך int_norm + השאילתה)
     final = (
         0.56 * emb_norm +
         0.26 * ce_norm +
@@ -457,7 +449,6 @@ def recommend_top3_budget_first(profile: Dict) -> pd.DataFrame:
         0.03 * price_norm
     ).astype("float32")
 
-    # 6) גיוון MMR ובחירת טופ-3
     pick_idx = _mmr_select(cand_idx, final, k=min(3, cand_idx.size), lambda_=0.7)
 
     res = CATALOG.loc[pick_idx].copy()
@@ -626,7 +617,6 @@ def diy_generate(profile: Dict) -> Tuple[dict, str]:
     lang = "English"
     ints_str = ", ".join(p["interests"]) or "general"
 
-    # 1) NAME
     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'}. "
@@ -641,7 +631,6 @@ def diy_generate(profile: Dict) -> Tuple[dict, str]:
     raw_name = _gen(tok, mdl, prompt_name, max_new_tokens=24, do_sample=False)
     name = _sanitize_name(raw_name, p["interests"])
 
-    # 2) OVERVIEW
     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']}). "
@@ -650,7 +639,6 @@ def diy_generate(profile: Dict) -> Tuple[dict, str]:
     )
     overview = _gen(tok, mdl, prompt_over, max_new_tokens=80, do_sample=True, temperature=0.9, top_p=0.95)
 
-    # 3) MATERIALS
     prompt_mat = (
         f"List 6 concise materials with quantities to make '{name}' cheaply. "
         f"Keep total within {p['budget_min']}-{p['budget_max']} USD. "
@@ -659,7 +647,6 @@ def diy_generate(profile: Dict) -> Tuple[dict, str]:
     mats_txt = _gen(tok, mdl, prompt_mat, max_new_tokens=96, do_sample=False)
     materials = _split_list_text(mats_txt, [",", ";"])
 
-    # 4) STEPS
     prompt_steps = (
         f"Write 6 short imperative steps to make '{name}'. "
         "Output ONLY a semicolon-separated list."
@@ -667,14 +654,12 @@ def diy_generate(profile: Dict) -> Tuple[dict, str]:
     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"])
 
-    # 5) COST
     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)
 
-    # 6) MINUTES
     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)
@@ -690,7 +675,6 @@ def diy_generate(profile: Dict) -> Tuple[dict, str]:
     return idea, "ok"
 
 # --------------------- Personalized Message (FLAN, מגוון + ולידציה) ---------------------
-# מבוסס אחד-לאחד על הקוד מהקולאב שלך, מותאם לשימוש ישיר באפליקציה
 MSG_MODEL_ID = "google/flan-t5-small"
 MSG_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 TEMP_RANGE  = (0.88, 1.10)
@@ -721,9 +705,9 @@ TONE_STYLES: Dict[str, Dict[str, List[str]]] = {
     "Funny": {
         "system": "Write 2–3 witty sentences with playful humor.",
         "rules": [
-            "Add one subtle pun linked to the occasion or interests.",
-            "No slapstick; keep it tasteful.",
-            "End with a cheeky nudge."
+                "Add one subtle pun linked to the occasion or interests.",
+                "No slapstick; keep it tasteful.",
+                "End with a cheeky nudge."
         ],
     },
     "Heartfelt": {
@@ -951,6 +935,24 @@ with gr.Blocks(
 /* הסתרת מסגרת/היילייט כתומים של טבלת הדוגמאות */
 .handsontable .wtBorder, .handsontable .htBorders, .handsontable .wtBorder.current { display: none !important; }
 .gr-dataframe table td:focus { outline: none !important; box-shadow: none !important; }
+
+/* === הופך כל שורת דוגמה ל"כפתור" === */
+.gr-dataframe thead { display:none; }
+.gr-dataframe table { border-collapse: separate !important; border-spacing: 0 8px !important; }
+.gr-dataframe tbody tr {
+  cursor: pointer;
+  background: #fff;
+  border-radius: 12px;
+  box-shadow: 0 1px 0 rgba(0,0,0,.06);
+}
+.gr-dataframe tbody tr td {
+  border-top: 1px solid #eee !important;
+  border-bottom: 1px solid #eee !important;
+}
+.gr-dataframe tbody tr:hover {
+  background: #f7fafc;
+  box-shadow: 0 0 0 2px #e2e8f0 inset;
+}
 """
 ) as demo:
     gr.Markdown(TITLE)
@@ -991,7 +993,7 @@ with gr.Blocks(
         tone           = gr.Dropdown(label="Message tone", choices=MESSAGE_TONES, value="Funny")
 
     # Action button and outputs
-    go = gr.Button("Get GIfty")
+    go = gr.Button("Get GIfty!")
     gr.Markdown("### 🎯 Recommendations")
     out_top3   = gr.HTML()
     gr.Markdown("### 🛠️ DIY Gift")
@@ -999,6 +1001,9 @@ with gr.Blocks(
     gr.Markdown("### 💌 Personalized Message")
     out_msg    = gr.Markdown()
 
+    # --- 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
@@ -1016,7 +1021,7 @@ with gr.Blocks(
         outputs=[interests, occasion, budget_min, budget_max, recipient_name, relationship, age, gender, tone]
     )
 
-    # ---- UI predict ----
+    # ---- helper: render DIY markdown (unchanged logic) ----
     def render_diy_md(j: dict) -> str:
         if not j: return "_DIY generation failed._"
         steps = j.get('step_by_step_instructions', j.get('steps', []))
@@ -1035,14 +1040,14 @@ with gr.Blocks(
         ]
         return "\n".join(parts)
 
-    def ui_predict(interests_list, occasion_val, bmin, bmax, name, rel, age_label, gender_val, tone_val):
+    # ---- 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
-
-        profile = {
+        return {
             "recipient_name": name or "Friend",
             "relationship": rel or "Friend",
             "interests": interests_list or [],
@@ -1054,21 +1059,60 @@ with gr.Blocks(
             "tone": tone_val or "Heartfelt",
         }
 
+    # ---- NEW: split into 3 functions (partial results) + token check ----
+    def start_run(curr_token):
+        # increments token (very fast; allows immediate second click)
+        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):
+        # compute only recommendations; ignore if token is stale
+        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 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
+
+    # --- Wire events: one short "start", then 3 parallel tasks that each update its output ASAP ---
+    # Start: bump token
+    ev_start = go.click(
+        start_run,
+        inputs=[run_token],
+        outputs=[run_token],
+        queue=True,
+    )
 
-        return top3_html, diy_md, msg
-
-    go.click(
-        ui_predict,
-        [interests, occasion, budget_min, budget_max, recipient_name, relationship, age, gender, tone],
-        [out_top3, out_diy_md, out_msg]
+    # Run three tasks in parallel (each returns its output + echoes token to keep it "fresh")
+    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,
     )
 
 if __name__ == "__main__":