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 = "# 🎁 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_SPLIT = os.getenv("DATASET_SPLIT", "train")
@@ -673,8 +673,11 @@ def diy_generate(profile: Dict) -> Tuple[dict, str]:
         "estimated_time_minutes": minutes,
     })
     return idea, "ok"
+# --------------------- END DIY ---------------------
+
 
 # --------------------- 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"
 TEMP_RANGE  = (0.88, 1.10)
@@ -705,9 +708,9 @@ 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": {
@@ -796,7 +799,7 @@ def _sentences_n(s: str) -> int:
 
 def _contains_any(text: str, terms: List[str]) -> bool:
     t = text.lower()
-    return any(term and term.lower() in t for term in terms)
+    return any(term for term in terms if term) and any((term or "").lower() in t for term in terms)
 
 def _too_similar(a: str, b: str, n=3, thr=0.85) -> bool:
     def ngrams(txt):
@@ -870,9 +873,10 @@ def generate_personal_message(profile: Dict[str, Any], seed: Optional[int]=None,
         )
         text = _norm(tok.decode(out_ids[0], skip_special_tokens=True))
 
+        # ===== Validators (כמו בקולאב) =====
         ok_len = 1 <= _sentences_n(text) <= 3
-        name_ok = _contains_any(text, [need["name"]])
-        occ_ok  = _contains_any(text, [need["occ"], need["occ"].split()[0]])
+        name_ok = _contains_any(text, [need["name"].lower()])
+        occ_ok  = _contains_any(text, [need["occ"].lower(), need["occ"].split()[0].lower()])
         ban_ok  = not _contains_any(text, BAN_PHRASES)
         prev    = previous_message or _last_msg
         dup_ok  = (prev is None) or (not _too_similar(text, prev, n=3, thr=0.85))
@@ -887,6 +891,7 @@ def generate_personal_message(profile: Dict[str, Any], seed: Optional[int]=None,
     fallback = tried[-1]["text"] if tried else f"Happy {(_clean_occasion(profile.get('occ_ui') or 'day')).lower()}, {profile.get('recipient_name','Friend')}!"
     _last_msg = fallback
     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:
@@ -931,28 +936,91 @@ def render_top3_html(df: pd.DataFrame, age_label: str) -> str:
 with gr.Blocks(
     title="🎁 GIfty — Recommender + DIY",
     css="""
+/* כותרת ההסבר */
 #explain {opacity:.85;font-size:.92em;margin-bottom:8px;}
-/* 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" === */
+/* --- שדרוג דוגמאות: הופך כל שורה לכרטיס מודרני ודק --- */
 .gr-dataframe thead { display:none; }
-.gr-dataframe table { border-collapse: separate !important; border-spacing: 0 8px !important; }
+.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;
-  background: #fff;
-  border-radius: 12px;
-  box-shadow: 0 1px 0 rgba(0,0,0,.06);
+  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-top: 1px solid #eee !important;
-  border-bottom: 1px solid #eee !important;
+  border: 0 !important;
+  padding: 4px 8px !important;
+  vertical-align: middle;
+  font-size: .92rem;
+  line-height: 1.3;
 }
-.gr-dataframe tbody tr:hover {
-  background: #f7fafc;
-  box-shadow: 0 0 0 2px #e2e8f0 inset;
+
+/* 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:
     gr.Markdown(TITLE)
@@ -1061,11 +1129,9 @@ with gr.Blocks(
 
     # ---- 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)
@@ -1087,7 +1153,6 @@ with gr.Blocks(
         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],
@@ -1095,7 +1160,6 @@ with gr.Blocks(
         queue=True,
     )
 
-    # 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],