Update app.py

app.py

@@ -51,6 +51,7 @@ INTENT_THRESHOLD = float(os.getenv("INTENT_THRESHOLD", "0.70"))
 SEMANTIC_MIN_SCORE = float(os.getenv("SEMANTIC_MIN_SCORE", "0.35"))
 SEMANTIC_TOP_K = int(os.getenv("SEMANTIC_TOP_K", "25"))
 SESSION_TTL_SEC = int(os.getenv("SESSION_TTL_SEC", "3600"))
+MAX_CARDS = int(os.getenv("MAX_CARDS", "4"))
 
 intent_pipe = None
 ner_pipe = None
@@ -98,7 +99,7 @@ CATEGORY_KEYWORDS = {
         "طعمية", "سمك", "restaurant", "food", "burger", "pizza", "shawarma", "crepe",
     ],
     "pharmacy": [
-        "صيدليه", "صيدلية", "صديليه", "صيدليات", "صيدله", "صديليه", "صديليه", "دوا", "دواء", "ادويه", "أدوية",
+        "صيدليه", "صيدلية", "صيدليات", "صيدله", "صديليه", "صديليه", "دوا", "دواء", "ادويه", "أدوية",
         "ادوية", "علاج", "pharmacy", "drugstore", "medicine",
     ],
     "cafe": [
@@ -173,18 +174,18 @@ ITEM_CATEGORY_HINTS = {
 
 DYNAMIC_TEMPLATES = {
     "category": [
-        "من عينيا! جمعتلك اختيارات {cat_ar} متاحة في بني سويف، شوف الكروت تحت واختار اللي يناسبك 👇",
+        "من عينيا! جمعتلك أحسن اختيارات {cat_ar} متاحة في بني سويف، شوف الكروت تحت واختار اللي يناسبك 👇",
         "جاهز لمساعدتك! دي لستة {cat_ar} مناسبة ومترتبة عشان تختار بسرعة 🗺️",
         "تأمر يا هندسة! لقيتلك شوية اختيارات كويسة من {cat_ar} في بني سويف ✨",
         "تمام، دورتلك وطلعتلك أنسب {cat_ar} حسب طلبك 👇",
-        "دي أفضل نتايج لقيتها ليك من {cat_ar}، بص على الكروت وشوف الأنسب ",
+        "دي أفضل نتايج لقيتها ليك من {cat_ar}، بص على الكروت وشوف الأنسب 😊",
     ],
     "nearest": [
         "حسبتلك المسافات بالملي! دي الأماكن مترتبة من الأقرب للأبعد من موقعك الحالي 📍",
         "علطول كده! أول كارت هو الأقرب ليك، وبعده الباقي حسب المسافة 🚗",
         "جبتلك الأقرب فالأقرب عشان المشوار يبقى سهل وسريع ⏱️",
         "دي أقرب اختيارات حوالين موقعك الحالي، مرتبة من الأقرب للأبعد 👇",
-        "رتبتهم حسب المسافة  من مكانك، شوف أول كارت هيبقى الأقرب 📌",
+        "رتبتهم حسب المسافة من مكانك، شوف أول كارت هيبقى الأقرب 📌",
     ],
     "best": [
         "بناءً على التقييمات، {best_name} ظاهر كأقوى اختيار، وباقي الكروت مرتبة حسب الأعلى تقييمًا 🏆",
@@ -193,17 +194,24 @@ DYNAMIC_TEMPLATES = {
         "الناس بتقول إن دول الأفضل، رتبتهم حسب التقييم عشان تختار وانت مطمن ✨",
         "جمعتلك الاختيارات الأعلى تقييمًا في بني سويف، أول كارت هو الأقوى تقييمًا 👇",
     ],
+    "best_near": [
+        "جمعتلك المعادلة الصح: أماكن تقييمها عالي وقريبة منك في نفس الوقت، أول كارت هو الأفضل توازنًا بين الجودة والمسافة ⭐📍",
+        "رتبتلك الاختيارات حسب التقييم مع مراعاة القرب، عشان تختار مكان كويس ومش بعيد 👇",
+        "دي أماكن قوية في الريتنج وقريبة نسبيًا من موقعك، يعني جودة ومشوار أسهل في نفس الوقت 🚗⭐",
+        "عملتلك ترتيب ذكي بين الأعلى تقييمًا والأقرب، شوف أول كارت هيبقى أنسب اختيار متوازن ✨",
+        "لو عايز حاجة كويسة وقريبة، دي أفضل النتائج حسب الريتنج والمسافة مع بعض 📌",
+    ],
     "item": [
-        "طلبك عندي! دورت في الأماكن المتاحة ولقيتلك اللي بيقدموا {item} 👇",
-        "جبتلك المفيد! دي الأماكن اللي ظاهر عندها {item}   🎯",
+        "طلبك عندي! دورت في الأماكن المتاحة ولقيتلك اللي بيقدموا {item} 😋👇",
+        "جبتلك المفيد! دي الأماكن اللي ظاهر عندها {item}  🎯",
         "بال هنا مقدمًا! دي اختيارات مناسبة لطلب {item} 👇",
         "دورتلك على {item} وطلعتلك الأماكن الأقرب للطلب ده ✨",
-        "دي الأماكن اللي ينفع تبدأ بيها لو نفسك في {item} ",
+        "دي الأماكن اللي ينفع تبدأ بيها لو نفسك في {item} 😋",
     ],
     "open": [
         "فلترتلك الأماكن المفتوحة دلوقتي قدر الإمكان، شوف الكروت تحت 🟢",
         "دورت على المتاح دلوقتي، والنتايج تحت حسب حالة المواعيد المسجلة ⏰",
-        "دي الأماكن اللي باين إنها شغالة أو متاحة حاليًا في الداتا 👇",
+        "دي الأماكن اللي باين إنها شغالة أو متاحة حاليًا   👇",
         "تمام، ركزت على الأماكن المفتوحة دلوقتي حسب المواعيد المتاحة 🟢",
         "شوف دول، حاولت أجيبلك المتاح دلوقتي بأفضل ترتيب 👇",
     ],
@@ -224,7 +232,7 @@ DYNAMIC_TEMPLATES = {
     "area_fallback": [
         "مفيش {cat_ar} متاحة حاليًا في {location}، بس لقيت بدائل قريبة/متاحة في بني سويف 👇",
         "المنطقة دي مش ظاهر فيها {cat_ar} دلوقتي، فبعرضلك اختيارات تانية ممكن تنفعك 👇",
-        "مش لاقي {cat_ar} في {location} حاليًا، لكن دي نتائج بديلة  👇",
+        "مش لاقي {cat_ar} في {location} حاليًا، لكن دي نتائج بديلة   👇",
         "في {location} مفيش نتيجة مطابقة، فدي أقرب بدائل مناسبة عندي 👇",
         "مفيش {cat_ar} مسجلة في {location} حاليًا، شوف البدائل دي 👇",
     ],
@@ -263,42 +271,47 @@ def normalize_category(cat: Any) -> str:
     c = clean_text(cat)
     if not c:
         return ""
-    if "pharmacy" in c or "صيدلي" in c or "صيدله" in c or "دواء" in c or "دوا" in c:
+    has_rest = "restaurant" in c or "مطعم" in c or "مطاعم" in c or "اكل" in c
+    has_cafe = "cafe" in c or "coffee" in c or "كافيه" in c or "كوفي" in c or "قهوه" in c or "قهوة" in c
+    if has_rest and has_cafe:
+        return "restaurant_cafe"
+    if "pharmacy" in c or "صيدلي" in c or "صيدله" in c or "صيدليه" in c or "صيدلية" in c or "دواء" in c or "دوا" in c:
         return "pharmacy"
     if "supermarket" in c or "market" in c or "grocery" in c or "ماركت" in c or "بقال" in c or "هايبر" in c:
         return "supermarket"
-    if "cafe" in c or "coffee" in c or "كافيه" in c or "كوفي" in c or "قهوه" in c or "قهوة" in c:
+    if has_cafe:
         return "cafe"
     if "housing" in c or "hotel" in c or "hostel" in c or "سكن" in c or "شقه" in c or "فندق" in c:
         return "housing"
-    if "restaurant" in c or "مطعم" in c or "مطاعم" in c or "اكل" in c:
+    if has_rest:
         return "restaurant"
     return c
 
 
 def category_matches(raw_category: Any, target: str) -> bool:
+    """Strict but fair category matching.
+    Mixed categories like 'restaurant & café' should match both restaurant and cafe.
     """
-    Strict but multi-label aware category matching.
-    Important: DB categories can be mixed like "restaurant & café".
-    Normalizing that to one label only makes cafe/restaurant queries miss valid cards.
-    """
-    target = normalize_category(target)
     if not target:
         return True
+    target = normalize_category(target)
     raw = clean_text(raw_category)
-    normed = normalize_category(raw_category)
-
+    norm_cat = normalize_category(raw_category)
+    if norm_cat == target:
+        return True
+    if norm_cat == "restaurant_cafe" and target in {"restaurant", "cafe"}:
+        return True
     if target == "restaurant":
-        return ("restaurant" in raw or "مطعم" in raw or "مطاعم" in raw or "food" in raw or normed == "restaurant")
+        return "restaurant" in raw or "مطعم" in raw or "مطاعم" in raw
     if target == "cafe":
-        return ("cafe" in raw or "coffee" in raw or "كافيه" in raw or "كوفي" in raw or "قهوه" in raw or "قهوة" in raw or normed == "cafe")
+        return "cafe" in raw or "coffee" in raw or "كافيه" in raw or "كوفي" in raw or "قهوه" in raw or "قهوة" in raw
     if target == "pharmacy":
-        return ("pharmacy" in raw or "صيدلي" in raw or "صيدله" in raw or "صديلي" in raw or normed == "pharmacy")
+        return "pharmacy" in raw or "صيدلي" in raw or "صيدله" in raw or "صيدليه" in raw or "صيدلية" in raw
     if target == "supermarket":
-        return ("supermarket" in raw or "market" in raw or "grocery" in raw or "ماركت" in raw or "بقال" in raw or "هايبر" in raw or normed == "supermarket")
+        return "supermarket" in raw or "market" in raw or "ماركت" in raw or "بقال" in raw or "هايبر" in raw
     if target == "housing":
-        return ("housing" in raw or "hotel" in raw or "hostel" in raw or "سكن" in raw or "شقه" in raw or "فندق" in raw or normed == "housing")
-    return normed == target or target in raw
+        return "housing" in raw or "hotel" in raw or "hostel" in raw or "سكن" in raw or "شقه" in raw or "فندق" in raw
+    return norm_cat == target or target in raw
 
 
 def infer_category(text: str, intent: str = "", entities: Optional[dict] = None) -> str:
@@ -591,7 +604,7 @@ def clean_item_query(text: str) -> str:
     remove_words = [
         "عايز", "عايزه", "عايزة", "عاوز", "عاوزه", "عاوزة", "محتاج", "محتاجة", "نفسي",
         "في", "فين", "مطعم", "مطاعم", "كافيه", "مكان", "بيعمل", "بتعمل", "عنده", "عندها",
-        "فيه", "فيها", "بيقدم", "بتقدم", "لو سمحت", "ممكن", "من فضلك", "قريب", "اقرب", "طب", "احسن", "افضل", "أفضل", "اعلى", "أعلى", "تقييم", "التوب", "توب", "كويس",
+        "فيه", "فيها", "بيقدم", "بتقدم", "لو سمحت", "ممكن", "من فضلك", "قريب", "اقرب",
     ]
     for w in remove_words:
         q = re.sub(rf"\b{re.escape(clean_text(w))}\b", " ", q)
@@ -618,6 +631,18 @@ def extract_item_query(text: str) -> str:
     return cleaned
 
 
+
+
+def is_generic_category_item(value: str) -> bool:
+    q = clean_text(value)
+    generic = {
+        "مطعم", "مطاعم", "اكل", "أكل", "مكان",
+        "كافيه", "كافيهات", "قهوه", "قهوة", "كوفي",
+        "صيدليه", "صيدلية", "صيدليات", "صيدله",
+        "ماركت", "سوبر", "سوبرماركت", "سوبر ماركت",
+    }
+    return q in {clean_text(x) for x in generic}
+
 def extract_menu_place_name(text: str) -> str:
     t = clean_text(text)
     for w in ["هات", "ابعت", "وريني", "اعرض", "منيو", "مينيو", "menu", "بتاع", "قائمة"]:
@@ -673,9 +698,10 @@ def extract_place_name_from_text(text: str) -> str:
     stop_phrases = [
         "فين", "مكان", "مكانه", "مكانها", "عنوان", "عنوانه", "عنوانها", "لوكيشن", "رقم", "تليفون",
         "مطعم", "صيدليه", "صيدلية", "صديليه", "كافيه", "ماركت", "سوبر", "سوبرماركت", "هات", "منيو", "عايز", "عايزة",
-        "عاوز", "عايزه", "عاوزه", "مواعيد", "بيفتح", "بتفتح", "امتى", "امتي", "عايزة", "اروح", "ازاي", "قريب", "اقرب", "مفتوح", "فاتح", "دلوقتي", "في", "من", "عند",
+        "عايزه", "عاوزه", "عاوز", "اروح", "ازاي", "قريب", "اقرب", "مفتوح", "فاتح", "دلوقتي", "في", "من", "عند",
         "رقمه", "رقمها", "تليفونه", "تليفونها", "ايه", "إيه", "احسن", "أحسن", "افضل", "أفضل", "اعلى", "أعلى", "توب", "التوب",
         "طب", "طيب", "تمام", "ماشي",
+        "مواعيد", "معاد", "ميعاد", "بيفتح", "بتفتح", "بيقفل", "بتقفل", "امتى", "امتي", "شغال", "شغاله", "شغالة",
     ]
     for w in stop_phrases:
         t = re.sub(rf"\b{re.escape(clean_text(w))}\b", " ", t)
@@ -686,9 +712,10 @@ def extract_place_name_from_text(text: str) -> str:
         return ""
     if infer_category(t) and len(t.split()) <= 2:
         return ""
-    # Named-place shortcut: "on public عايزه", "lamera عايزه", "pizza bike مواعيد".
-    # If removing request/category words leaves a short phrase, treat it as a candidate place name.
-    if len(t.split()) <= 4:
+    # English place names like "on public عايزه" or "lamera مواعيد" should beat old context.
+    if re.search(r"[a-zA-Z]", t) and len(t.split()) <= 5:
+        return t
+    if any(w in original for w in ["فين", "مكان", "عنوان", "لوكيشن", "منيو", "رقم", "مواعيد", "بيفتح", "بتفتح"]) and len(t.split()) <= 5:
         return t
     return ""
 
@@ -747,6 +774,10 @@ def is_best_query(text: str, intent: str = "") -> bool:
     return has_any(text, BEST_WORDS) or intent in {"best_of", "recommend_place"}
 
 
+def is_best_near_query(text: str, intent: str = "") -> bool:
+    return is_best_query(text, intent) and is_near_query(text)
+
+
 def compare_place_names(text: str) -> list:
     t = clean_text(text)
     for w in ["قارن", "قارني", "مقارنة", "بين", "ما بين", "مين احسن", "احسن"]:
@@ -781,17 +812,14 @@ def semantic_candidates(query: str, top_k: int = SEMANTIC_TOP_K) -> pd.DataFrame
     return result.reset_index(drop=True)
 
 
-def name_search(place_name: str, category: str = "", top_k: int = 5) -> pd.DataFrame:
+def name_search(place_name: str, category: str = "", top_k: int = 12) -> pd.DataFrame:
     if places_df is None or places_df.empty or not place_name:
         return pd.DataFrame()
     q = clean_text(place_name)
     q_words = [w for w in q.split() if len(w) > 1]
     df = places_df.copy()
     if category:
-        if "category" in df.columns:
-            df = df[df["category"].apply(lambda c: category_matches(c, category))]
-        elif "category_norm" in df.columns:
-            df = df[df["category_norm"].apply(lambda c: category_matches(c, category))]
+        df = df[df["category_norm"].apply(lambda c: category_matches(c, category))]
     if df.empty:
         return df
 
@@ -817,11 +845,8 @@ def apply_filters(df: pd.DataFrame, category: str = "", location: str = "", pric
     if df is None or df.empty:
         return pd.DataFrame()
     f = df.copy()
-    if category:
-        if "category" in f.columns:
-            f = f[f["category"].apply(lambda c: category_matches(c, category))]
-        elif "category_norm" in f.columns:
-            f = f[f["category_norm"].apply(lambda c: category_matches(c, category))]
+    if category and "category_norm" in f.columns:
+        f = f[f["category_norm"].apply(lambda c: category_matches(c, category))]
     if location and not f.empty:
         loc = clean_text(location)
         parts = [p for p in loc.split() if len(p) > 1]
@@ -878,6 +903,9 @@ def rank_places(df: pd.DataFrame, query: str, user_lat=None, user_lon=None, inte
         if col not in f.columns:
             f[col] = default
         f[col] = pd.to_numeric(f[col], errors="coerce").fillna(default)
+    if best and near and user_lat is not None and user_lon is not None:
+        f["final_score"] = 0.70 * f["rating_score"] + 0.30 * f["distance_score"]
+        return f.sort_values(["final_score", "rating_count", "semantic_score"], ascending=[False, False, False]).reset_index(drop=True)
     if near and user_lat is not None and user_lon is not None:
         return f.sort_values(["distance_km", "rating_norm"], ascending=[True, False], na_position="last").reset_index(drop=True)
     if best:
@@ -890,7 +918,7 @@ def rank_places(df: pd.DataFrame, query: str, user_lat=None, user_lon=None, inte
         f["final_score"] = 0.45 * f["semantic_score"] + 0.25 * f["rating_score"] + 0.15 * f["distance_score"] + 0.10 * f["open_score"] + 0.05 * f["name_score"]
     return f.sort_values("final_score", ascending=False).reset_index(drop=True)
 
-def search_places(query: str, category: str = "", item_query: str = "", location: str = "", price_range: str = "", open_now_only: bool = False, place_name: str = "", user_lat=None, user_lon=None, intent: str = "", top_k: int = 5) -> pd.DataFrame:
+def search_places(query: str, category: str = "", item_query: str = "", location: str = "", price_range: str = "", open_now_only: bool = False, place_name: str = "", user_lat=None, user_lon=None, intent: str = "", top_k: int = 12) -> pd.DataFrame:
     category = normalize_category(category)
     if item_query and not category:
         category = intended_item_category(item_query) or "restaurant"
@@ -919,15 +947,22 @@ def search_places(query: str, category: str = "", item_query: str = "", location
             out["semantic_score"] = 0.80
         out = apply_filters(out, category=category or "restaurant", location="", price_range=price_range, open_now_only=open_now_only)
         return rank_places(out, query, user_lat, user_lon, intent).drop_duplicates("place_id").head(top_k).reset_index(drop=True)
-    # Generic category/ranking requests should not depend on semantic score.
-    # Examples: "عايزه كافيه", "احسن مطعم", "توب كافيه".
-    # Use all DB places in that category, then rank/filter them.
-    if category:
+    # For broad category requests ("عايز كافيه", "احسن مطعم"), use the live DB directly.
+    # Semantic threshold can wrongly hide valid category results, especially mixed categories like restaurant & café.
+    if category and places_df is not None and not places_df.empty:
+        base = apply_filters(places_df.copy(), category=category, location=location, price_range=price_range, open_now_only=open_now_only)
+        if not base.empty:
+            if "semantic_score" not in base.columns:
+                base["semantic_score"] = 0.80
+            return rank_places(base, query, user_lat, user_lon, intent).drop_duplicates("place_id").head(top_k).reset_index(drop=True)
+
+    # Pure category queries like "عايزه كافيه" or "احسن مطعم" should not be killed by semantic threshold.
+    if category and not item_query and not place_name:
         base = places_df.copy() if places_df is not None else pd.DataFrame()
         filtered = apply_filters(base, category=category, location=location, price_range=price_range, open_now_only=open_now_only)
         if not filtered.empty:
             if "semantic_score" not in filtered.columns:
-                filtered["semantic_score"] = 0.60
+                filtered["semantic_score"] = 0.75
             return rank_places(filtered, query, user_lat, user_lon, intent).drop_duplicates("place_id").head(top_k).reset_index(drop=True)
 
     candidates = semantic_candidates(query, top_k=SEMANTIC_TOP_K)
@@ -974,7 +1009,7 @@ def _dynamic_reply(kind: str, **kwargs) -> str:
         return random.choice(templates)
 
 
-def build_response(results: List[dict], intent: str, category: str = "", item_query: str = "", place_name: str = "", location: str = "", open_now_only: bool = False, soft_reason: str = "") -> str:
+def build_response(results: List[dict], intent: str, category: str = "", item_query: str = "", place_name: str = "", location: str = "", open_now_only: bool = False, soft_reason: str = "", query: str = "") -> str:
     category = normalize_category(category)
     if not results:
         if place_name:
@@ -994,9 +1029,11 @@ def build_response(results: List[dict], intent: str, category: str = "", item_qu
         header = _dynamic_reply("area_fallback", category=category, location=location)
     elif item_query:
         header = _dynamic_reply("item", category=category or "restaurant", item_query=item_query)
-    elif is_near_query(intent) or intent in {"nearby_search", "nearest_restaurant", "nearest_cafe", "nearest_pharmacy", "nearest_supermarket"}:
+    elif is_best_near_query(query, intent):
+        header = _dynamic_reply("best_near", category=category, best_place=results[0])
+    elif is_near_query(query) or intent in {"nearby_search", "nearest_restaurant", "nearest_cafe", "nearest_pharmacy", "nearest_supermarket"}:
         header = _dynamic_reply("nearest", category=category, best_place=results[0])
-    elif intent in {"best_of", "recommend_place"}:
+    elif is_best_query(query, intent):
         header = _dynamic_reply("best", category=category, best_place=results[0])
     elif open_now_only:
         header = _dynamic_reply("open", category=category, best_place=results[0])
@@ -1004,8 +1041,8 @@ def build_response(results: List[dict], intent: str, category: str = "", item_qu
         header = _dynamic_reply("one", category=category, best_place=results[0])
     else:
         header = _dynamic_reply("category", category=category, best_place=results[0])
-    # Flutter already renders the place cards, so keep the chat bubble clean.
-    # Do not duplicate results as numbered text inside the reply.
+    if len(results) > MAX_CARDS:
+        header += "\n\nفي أماكن تانية كمان، اكتبي «كمان» لو عايزاهم."
     return header
 
 def handle_detail(text: str, place: Optional[dict]) -> str:
@@ -1095,7 +1132,8 @@ class Session:
                 self.last_place = place
             if results:
                 self.last_results = results
-                self.result_pointer = 0
+                # First response shows MAX_CARDS cards; "كمان" starts after them.
+                self.result_pointer = min(MAX_CARDS, len(results))
             for key in ["category", "location", "price_range", "item_query", "place_name"]:
                 if entities and entities.get(key):
                     self.context_slots[key] = entities[key]
@@ -1133,15 +1171,24 @@ def chat(text: str, session: Session, user_lat=None, user_lon=None) -> dict:
 
     ref_type = detect_ref_type(text)
     if ref_type == "next" and session.last_results:
-        if session.result_pointer + 1 >= len(session.last_results):
+        start = int(session.result_pointer or 0)
+        if start >= len(session.last_results):
             reply = "دي كانت كل النتائج المتاحة حاليًا 😊\nلو حابب، جرّب منطقة تانية أو نوع تاني."
             session.add(text, reply, "show_more", session.last_entities, session.last_place, session.last_results)
+            session.result_pointer = len(session.last_results)
             return {"reply": reply, "intent": "show_more", "confidence": 1.0, "entities": session.last_entities, "best_place": session.last_place, "all_results": []}
-        session.result_pointer += 1
-        place = session.last_results[session.result_pointer]
-        reply = "تمام، شوف الاختيار اللي بعده 👇\n" + format_place_line(place)
+        end = min(start + MAX_CARDS, len(session.last_results))
+        batch = session.last_results[start:end]
+        session.result_pointer = end
+        place = batch[0] if batch else session.last_place
+        reply = "دي أماكن تانية كمان 👇"
+        if session.result_pointer < len(session.last_results):
+            reply += "\nوفيه كمان لو حبيتي."
+        else:
+            reply += "\nدي آخر الأماكن المتاحة عندي حاليًا."
         session.add(text, reply, session.last_intent or "show_more", session.last_entities, place, session.last_results)
-        return {"reply": reply, "intent": "show_more", "confidence": 1.0, "entities": session.last_entities, "best_place": place, "all_results": [place]}
+        session.result_pointer = end
+        return {"reply": reply, "intent": "show_more", "confidence": 1.0, "entities": session.last_entities, "best_place": place, "all_results": batch}
 
     if ref_type == "detail" and session.last_place and not extract_place_name_from_text(text):
         reply = handle_detail(text, session.last_place)
@@ -1157,7 +1204,6 @@ def chat(text: str, session: Session, user_lat=None, user_lon=None) -> dict:
         session.add(text, reply, "missing_info", entities, None, [])
         return {"reply": reply, "intent": "missing_info", "confidence": confidence, "entities": entities, "best_place": None, "all_results": []}
     category = infer_category(text, intent, entities)
-    explicit_category = category  # category mentioned in the current user message only
     if category:
         entities["category"] = category
     location_from_text = entities.get("location", "")
@@ -1224,9 +1270,7 @@ def chat(text: str, session: Session, user_lat=None, user_lon=None) -> dict:
         return {"reply": reply, "intent": "show_menu", "confidence": confidence, "entities": entities, "best_place": place, "all_results": [place], "menu_items": items}
 
     if intent == "place_details" and entities.get("place_name"):
-        # Explicit place-name details must not be polluted by previous session category.
-        explicit_detail_category = infer_category(text, intent="", entities={})
-        hits = name_search(entities["place_name"], category=explicit_detail_category, top_k=1)
+        hits = name_search(entities["place_name"], category=category, top_k=1)
         if not hits.empty:
             place = row_to_dict(hits.iloc[0])
             reply = handle_detail(text, place)
@@ -1234,26 +1278,20 @@ def chat(text: str, session: Session, user_lat=None, user_lon=None) -> dict:
             return {"reply": reply, "intent": intent, "confidence": confidence, "entities": entities, "best_place": place, "all_results": [place]}
 
     merged = session.merge(entities)
-    place_name = entities.get("place_name", "")
-    # Do not let previous category pollute explicit place-name searches.
-    # Example: after asking for a pharmacy, "on public عايزه" must search all places, not only pharmacies.
-    if not category and merged.get("category") and not place_name:
+    if not category and merged.get("category"):
         category = merged.get("category")
         entities["category"] = category
     item_query = entities.get("item_query", "")
+    if item_query and is_generic_category_item(item_query):
+        item_query = ""
+        entities.pop("item_query", None)
+    place_name = entities.get("place_name", "")
     location = entities.get("location") or merged.get("location", "")
     price_range = entities.get("price_range", "")
     if intent == "search_by_item" and not item_query:
         item_query = extract_item_query(text)
         if item_query:
             entities["item_query"] = item_query
-
-    # Guard: words like "احسن مطعم" / "توب كافيه" are ranking requests, not menu items.
-    invalid_item_words = {"احسن", "افضل", "أفضل", "اعلى", "أعلى", "تقييم", "التوب", "توب", "كويس", "مطعم", "كافيه", "صيدليه", "صيدلية", "صديليه", "ماركت", "مكان", "طب"}
-    if item_query and (clean_text(item_query) in invalid_item_words or has_any(item_query, list(invalid_item_words & {"احسن", "افضل", "أفضل", "اعلى", "أعلى", "تقييم", "التوب", "توب", "كويس"}))):
-        item_query = ""
-        entities.pop("item_query", None)
-
     if item_query and not category:
         category = intended_item_category(item_query) or "restaurant"
         entities["category"] = category
@@ -1267,21 +1305,21 @@ def chat(text: str, session: Session, user_lat=None, user_lon=None) -> dict:
         category = infer_category(text) or "cafe"
         entities["category"] = category
 
-    results_df = search_places(text, category=category, item_query=item_query, location=location, price_range=price_range, open_now_only=open_now_only, place_name=place_name, user_lat=user_lat, user_lon=user_lon, intent=intent, top_k=5)
+    results_df = search_places(text, category=category, item_query=item_query, location=location, price_range=price_range, open_now_only=open_now_only, place_name=place_name, user_lat=user_lat, user_lon=user_lon, intent=intent, top_k=12)
     soft_reason = ""
     if results_df.empty and open_now_only and category:
-        results_df = search_places(text, category=category, item_query=item_query, location=location, price_range=price_range, open_now_only=False, place_name=place_name, user_lat=user_lat, user_lon=user_lon, intent=intent, top_k=5)
+        results_df = search_places(text, category=category, item_query=item_query, location=location, price_range=price_range, open_now_only=False, place_name=place_name, user_lat=user_lat, user_lon=user_lon, intent=intent, top_k=12)
         soft_reason = "open" if not results_df.empty else ""
     if results_df.empty and location and category:
-        results_df = search_places(text, category=category, item_query=item_query, location="", price_range=price_range, open_now_only=False, place_name=place_name, user_lat=user_lat, user_lon=user_lon, intent=intent, top_k=5)
+        results_df = search_places(text, category=category, item_query=item_query, location="", price_range=price_range, open_now_only=False, place_name=place_name, user_lat=user_lat, user_lon=user_lon, intent=intent, top_k=12)
         soft_reason = "area" if not results_df.empty else soft_reason
     if results_df.empty and item_query and is_near_query(text):
-        results_df = search_places(text, category=category or "restaurant", item_query=item_query, location="", price_range=price_range, open_now_only=False, place_name="", user_lat=user_lat, user_lon=user_lon, intent=intent, top_k=5)
+        results_df = search_places(text, category=category or "restaurant", item_query=item_query, location="", price_range=price_range, open_now_only=False, place_name="", user_lat=user_lat, user_lon=user_lon, intent=intent, top_k=12)
         soft_reason = "near_item" if not results_df.empty else soft_reason
     results = [row_to_dict(r) for _, r in results_df.iterrows()] if not results_df.empty else []
     best = results[0] if results else None
     result_intent = intent if results else "no_result"
-    reply = build_response(results, intent, category=category, item_query=item_query, place_name=place_name, location=location, open_now_only=open_now_only, soft_reason=soft_reason)
+    reply = build_response(results, intent, category=category, item_query=item_query, place_name=place_name, location=location, open_now_only=open_now_only, soft_reason=soft_reason, query=text)
     session.add(text, reply, result_intent, entities, best, results)
     return {"reply": reply, "intent": result_intent, "confidence": confidence, "entities": entities, "best_place": best, "all_results": results}
 @asynccontextmanager
@@ -1501,7 +1539,7 @@ def chat_endpoint(req: ChatRequest):
         session_id=req.session_id,
         ui_type=ui_type,
         best_place=unique_cards[0] if unique_cards else None,
-        place_cards=unique_cards,
+        place_cards=unique_cards[:MAX_CARDS],
         menu_items=menu_items,
     )