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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+ENV PORT=7860
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+# -*- coding: utf-8 -*-
+"""app.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1iPAjeI3M04kA13lYenlROS96tUeCYakB
+"""
+
+import os, re, json, math, random, pickle, joblib
+import numpy as np
+import pandas as pd
+import torch
+
+from datetime import datetime
+from zoneinfo import ZoneInfo
+from contextlib import asynccontextmanager
+
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing import Optional
+
+from sentence_transformers import SentenceTransformer, util
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    AutoModelForTokenClassification,
+    pipeline,
+)
+
+"""Paths"""
+
+try:
+    BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+except NameError:
+    BASE_DIR = os.getcwd()
+
+INTENT_REPO = os.getenv("INTENT_REPO", "YOUR_USERNAME/intent_arabert_saved_ff")
+ENTITY_DIR   = os.path.join(BASE_DIR, "entity_hybrid_saved_ff")
+SEMANTIC_DIR = os.path.join(BASE_DIR, "semantic_search_saved_ff")
+PLACES_FILE  = os.path.join(BASE_DIR, "beni_suef_100_places_v5ff.xlsx")
+
+intent_tokenizer = intent_model = label_encoder = id2intent = None
+ner_pipeline = label2id = id2label = None
+semantic_model = corpus_df = corpus_embeddings = places_df = None
+SESSIONS: dict = {}
+
+def clean_text(text):
+    text = str(text).strip().lower()
+    text = re.sub(r"ـ+", "", text)
+    for old, new in [("[إأآا]","ا"),("ى","ي"),("ة","ه"),("ؤ","و"),("ئ","ي")]:
+        text = re.sub(old, new, text)
+    text = re.sub(r"[^\w\s]", " ", text)
+    return re.sub(r"\s+", " ", text).strip()
+
+
+def norm(text):
+    text = str(text).strip().lower()
+    text = re.sub(r"ـ+", "", text)
+    for old, new in [("[إأآا]","ا"),("ى","ي"),("ة","ه"),("ؤ","و"),("ئ","ي")]:
+        text = re.sub(old, new, text)
+    for old, new in [("صباحًا","ص"),("صباحا","ص"),("مساءً","م"),("مساءا","م"),
+                     ("ليلًا","م"),("ليلا","م"),("إلى","-"),("الى","-"),("حتى","-"),
+                     ("–","-"),("—","-")]:
+        text = text.replace(old, new)
+    return re.sub(r"\s+", " ", text).strip()
+
+#  INTENT MAPS
+SEARCH_INTENTS = {"nearest_restaurant","nearest_pharmacy","nearest_cafe",
+                  "nearest_supermarket","housing_search","recommend_place",
+                  "open_now","place_details"}
+STATIC_INTENTS = {"greeting","thanks","goodbye","confirm","deny"}
+
+INTENT_TO_CATEGORY = {
+    "nearest_restaurant":"restaurant","nearest_pharmacy":"pharmacy",
+    "nearest_cafe":"cafe","nearest_supermarket":"supermarket",
+    "housing_search":"housing",
+}
+INTENT_TEMPLATE_MAP = {
+    "nearest_restaurant":"find_restaurant","nearest_pharmacy":"find_pharmacy",
+    "nearest_cafe":"find_cafe","nearest_supermarket":"find_supermarket",
+    "housing_search":"find_housing","recommend_place":"find_restaurant",
+    "open_now":"find_restaurant","place_details":"find_restaurant",
+    "greeting":"greeting","thanks":"thanks","goodbye":"goodbye",
+    "confirm":"clarification","deny":"clarification","fallback":"fallback",
+}
+ENTITY_FIELD_MAP = {
+    "location":"location","place_type":"category","cuisine_or_item":"sub_category",
+    "food_type":"sub_category","price":"price","price_range":"price",
+    "category":"category","sub_category":"sub_category","facility_type":"category",
+    "housing_type":"category","status":"status","time":"time",
+}
+KEYWORD_OVERRIDE = {
+    "goodbye": ["مع السلامة","مع السلامه","باي","وداعا","bye","goodbye","تصبح على خير",
+                "في امان الله","الله يسلمك","سلامتك"],
+    "greeting":["السلام عليكم","وعليكم السلام","اهلا","أهلا","هلا","هلو","مرحبا","مرحباً",
+                "صباح الخير","مساء الخير","هاي","hi","hello","صباح","مساء"],
+    "thanks":  ["شكرا","شكراً","تسلم","يسلمو","ممنون","مشكور","thanks","thank","الف شكر"],
+}
+CATEGORY_KEYWORDS = {
+    "restaurant":["مطعم","اكل","وجبات","مشويات","كباب","شاورما","كريب","برجر","سمك","فرايد"],
+    "pharmacy":  ["صيدليه","صيدلية","دوا","ادويه","دواء"],
+    "cafe":      ["كافيه","كوفي","قهوه","قهوة","كافيتيريا"],
+    "supermarket":["سوبرماركت","ماركت","بقاله","هايبر"],
+    "housing":   ["شقه","شقة","ايجار","إيجار","فندق","هوستل","سكن"],
+}
+CLARIFICATION_Q = {
+    "nearest_restaurant":"أي نوع أكل؟ مشويات، شاورما، كريب، برجر؟",
+    "nearest_pharmacy":"في أي منطقة بتدور على صيدلية؟",
+    "nearest_cafe":"في أي منطقة بتدور على كافيه؟",
+    "nearest_supermarket":"في أي منطقة بتدور على ماركت؟",
+    "housing_search":"بتدور على إيه — شقة ��يجار، فندق؟ وفين؟",
+}
+OUT_OF_SCOPE_KW = ["الجو","طقس","درجه","كوره","كرة","أهلي","زمالك","مباريات",
+                    "سياسه","سياسة","أخبار","رصيد","بنك","تحويل","امتحان","مدرسه",
+                    "جامعه","وظيفه","برمجه","كود","python","java","رياضيات","ترجمه","translate"]
+NEXT_WORDS   = ["تاني","غيره","غيرها","بديل","حاجة تانية","مش عاجبني","فيه تاني","عايز غيره"]
+DETAIL_WORDS = ["بيفتح","بتفتح","مواعيده","مواعيدها","امتى","امتي","عنوانه","عنوانها",
+                "تليفونه","تليفونها","رقمه","رقمها","تقييمه","تقييمها","سعره","سعرها"]
+REF_WORDS    = ["هو","هي","ده","دي","المكان ده"]
+_LOC_CUES    = ["الحي","بني سويف","الاباصيري","الكورنيش","مقبل","الزراعيين",
+                "صلاح سالم","شرق النيل","سيتي سنتر","عرابي","الروضه"]
+
+#  HELPER FUNCTIONS
+
+def apply_keyword_override(text):
+    t = norm(text); tw = set(t.split())
+    for intent, kws in KEYWORD_OVERRIDE.items():
+        for k in sorted(kws, key=len, reverse=True):
+            kn = norm(k)
+            if (" " in kn and kn in t) or (kn in tw): return intent
+    return None
+
+def get_template_key(intent, category=None):
+    if category:
+        k = {"restaurant":"find_restaurant","pharmacy":"find_pharmacy",
+             "cafe":"find_cafe","supermarket":"find_supermarket",
+             "housing":"find_housing"}.get(category)
+        if k: return k
+    return INTENT_TEMPLATE_MAP.get(intent, "fallback")
+
+def infer_category(query):
+    q = norm(query)
+    for cat, words in CATEGORY_KEYWORDS.items():
+        if any(norm(w) in q for w in words): return cat
+    return None
+
+def is_out_of_scope(text):
+    t = norm(text)
+    return any(norm(k) in t for k in OUT_OF_SCOPE_KW)
+
+def detect_ref_type(text):
+    t = norm(text); tw = set(t.split())
+    if any(norm(w) in t for w in NEXT_WORDS):   return "next"
+    if any(norm(w) in t for w in DETAIL_WORDS): return "detail"
+    for w in REF_WORDS:
+        wn = norm(w)
+        if (" " in wn and wn in t) or (wn in tw): return "reference"
+    return "new"
+
+def _loc_continuation(text):
+    t = norm(text); words = t.split()
+    if len(words) <= 4 and any(norm(c) in t for c in _LOC_CUES): return True
+    return bool(words and words[0] == "في")
+
+def normalize_rating(r):
+    try:
+        r = float(r)
+        return round(r/2, 1) if r > 5 else round(r, 1) if r > 0 else 0.0
+    except: return 0.0
+
+#  TIME UTILS
+
+def get_cairo_now():
+    return datetime.now(ZoneInfo("Africa/Cairo"))
+
+def parse_time(token):
+    token = norm(token).replace(" ", "")
+    m = re.match(r"^(\d{1,2})(?::(\d{1,2}))?(ص|م|ظهر)?$", token)
+    if not m: return None
+    h = int(m.group(1)); mn = int(m.group(2)) if m.group(2) else 0; suf = m.group(3)
+    if not (0 <= mn <= 59): return None
+    if suf == "ص":
+        if h == 12: h = 0
+        elif not (1 <= h <= 11): return None
+    elif suf in ("م","ظهر"):
+        if h != 12 and 1 <= h <= 11: h += 12
+    else:
+        if h == 24: h = 0
+        elif not (0 <= h <= 23): return None
+    return f"{h:02d}:{mn:02d}"
+
+def check_open_now(opening_hours_str):
+    if not opening_hours_str or str(opening_hours_str).strip() in ("","nan","none"): return None
+    text = norm(str(opening_hours_str))
+    if any(k in text for k in ["24","always","طول اليوم","24/7"]): return 1
+    sep = re.search(r"(.+?)\s*-\s*(.+)", text)
+    if not sep: return None
+    t1 = parse_time(sep.group(1).strip()); t2 = parse_time(sep.group(2).strip())
+    if not t1 or not t2: return None
+    now_t = f"{get_cairo_now().hour:02d}:{get_cairo_now().minute:02d}"
+    if t1 <= t2: return 1 if t1 <= now_t <= t2 else 0
+    return 1 if (now_t >= t1 or now_t <= t2) else 0
+
+#  SEARCH + FILTER + RANK
+def semantic_candidates(query, top_k=20):
+    q_emb  = semantic_model.encode(clean_text(query), convert_to_tensor=True)
+    scores = util.cos_sim(q_emb, corpus_embeddings)[0]
+    top_k  = min(top_k, len(corpus_df))
+    top_r  = torch.topk(scores, k=top_k)
+    res    = corpus_df.iloc[top_r.indices.cpu().numpy()].copy()
+    res["semantic_score"] = top_r.values.cpu().numpy()
+    keep = [c for c in ["place_id","doc_id","name","category","sub_category","location",
+                         "address","price_range","opening_hours","description","semantic_score"]
+            if c in res.columns]
+    return res[keep].reset_index(drop=True)
+
+def merge_places(df):
+    extra = [c for c in ["lat","lon","rating","phone","social_media","status",
+                          "category_clean","sub_category_clean","location_clean",
+                          "address_clean","price_range_clean","search_text_clean"]
+             if c in places_df.columns]
+    slim = places_df[["place_id"] + extra].copy()
+    return df.merge(slim, on="place_id", how="left")
+
+def apply_filters(df, query, category=None, sub_category=None, location=None,
+                   price_range=None, open_now_only=False, min_rating=None):
+    f = df.copy()
+    if category:     f = f[f["category_clean"].astype(str).str.contains(re.escape(clean_text(category)), na=False)]
+    if sub_category: f = f[f["sub_category_clean"].astype(str).str.contains(re.escape(clean_text(sub_category)), na=False)]
+    if location:     f = f[f["location_clean"].astype(str).str.contains(re.escape(clean_text(location)), na=False)]
+    if price_range:  f = f[f["price_range_clean"].astype(str).str.contains(re.escape(clean_text(price_range)), na=False)]
+    f["open_now"]     = f["opening_hours"].apply(check_open_now)
+    f["rating_num"]   = pd.to_numeric(f.get("rating", pd.Series()), errors="coerce").fillna(0)
+    f["rating_norm"]  = f["rating_num"].apply(normalize_rating)
+    f["rating_score"] = f["rating_norm"] / 5.0
+    f["open_score"]   = f["open_now"].apply(lambda x: 1.0 if x==1 else (0.5 if x is None else 0.0))
+    if open_now_only: f = f[f["open_now"] == 1]
+    if min_rating:    f = f[f["rating_norm"] >= min_rating]
+    return f
+
+def haversine(lat1, lon1, lat2, lon2):
+    R=6371; p=math.pi/180
+    a = (math.sin((lat2-lat1)*p/2)**2 + math.cos(lat1*p)*math.cos(lat2*p)*math.sin((lon2-lon1)*p/2)**2)
+    return 2*R*math.asin(math.sqrt(a))
+
+def rank(df, query, user_lat=None, user_lon=None):
+    df = df.copy()
+    if user_lat and user_lon and "lat" in df.columns:
+        def dist(row):
+            try: return haversine(float(user_lat), float(user_lon), float(row["lat"]), float(row["lon"]))
+            except: return 999
+        df["distance_km"]    = df.apply(dist, axis=1)
+        mx                   = df["distance_km"].replace(999, np.nan).max() or 1
+        df["distance_score"] = 1 - (df["distance_km"] / (mx + 1))
+    else:
+        df["distance_km"] = 999; df["distance_score"] = 0.0
+    q_clean = clean_text(query)
+    df["name_match_score"] = df["name"].apply(
+        lambda n: 1.0 if clean_text(str(n)) in q_clean or q_clean in clean_text(str(n)) else 0.0)
+    w = dict(semantic=0.40, rating=0.25, open=0.15, distance=0.10, name=0.10)
+    df["final_score"] = (
+        w["semantic"]*df.get("semantic_score", pd.Series(0,index=df.index)).fillna(0) +
+        w["rating"]  *df.get("rating_score",   pd.Series(0,index=df.index)).fillna(0) +
+        w["open"]    *df.get("open_score",      pd.Series(0,index=df.index)).fillna(0) +
+        w["distance"]*df["distance_score"] + w["name"]*df["name_match_score"]
+    )
+    return df.sort_values("final_score", ascending=False).reset_index(drop=True)
+
+def search_places(query, top_k_final=5, category=None, sub_category=None,
+                   location=None, price_range=None, open_now_only=False,
+                   min_rating=None, user_lat=None, user_lon=None):
+    cands  = semantic_candidates(query, top_k=20)
+    merged = merge_places(cands)
+    for attempt in [
+        dict(category=category, sub_category=sub_category, location=location,
+             price_range=price_range, open_now_only=open_now_only, min_rating=min_rating),
+        dict(category=category, sub_category=None, location=location,
+             price_range=price_range, open_now_only=open_now_only, min_rating=min_rating),
+        dict(category=category, sub_category=None, location=location,
+             price_range=None, open_now_only=False, min_rating=min_rating),
+        dict(category=category, sub_category=None, location=None,
+             price_range=None, open_now_only=False, min_rating=None),
+    ]:
+        filtered = apply_filters(merged, query, **attempt)
+        if not filtered.empty: break
+    if filtered.empty: return pd.DataFrame()
+    ranked = rank(filtered, query, user_lat, user_lon)
+    keep = [c for c in ["place_id","name","category","sub_category","location","address",
+                         "price_range","rating","rating_norm","opening_hours","description",
+                         "phone","lat","lon","semantic_score","final_score","open_now"]
+            if c in ranked.columns]
+    return ranked[keep].head(top_k_final).reset_index(drop=True)
+
+#  RESPONSE TEMPLATES + FORMATTERS
+RESPONSE_TEMPLATES = {
+    "find_restaurant":[
+        "🍽️ لقيتلك {name} في {location}. {price_info}{rating_info}{hours_info}",
+        "أنصحك بـ {name} — هتلاقيه في {location}. {price_info}{rating_info}{hours_info}",
+        "في {location} فيه {name}. {description_short}{price_info}{hours_info}",
+    ],
+    "find_pharmacy":[
+        "💊 {name} في {location}.{hours_info}{rating_info}",
+        "أقرب صيدلية ليك: {name} — {address_info}{hours_info}",
+    ],
+    "find_cafe":[
+        "☕ {name} في {location}. {price_info}{rating_info}{hours_info}",
+        "جرب {name} — في {location}. {description_short}{hours_info}",
+    ],
+    "find_supermarket":[
+        "🛒 {name} في {location}.{hours_info}{rating_info}",
+        "أقرب ماركت: {name} — {address_info}{hours_info}",
+    ],
+    "find_housing":[
+        "🏠 {name} في {location}. {price_info}{description_short}",
+        "فيه {name} في {location}. {price_info}{rating_info}",
+    ],
+    "greeting":     ["أهلاً! 😊 أنا بساعدك تلاقي أي مكان في بني سويف. عايز إيه؟",
+                     "وعليكم السلام! قولي محتاج إيه — مطعم، صيدلية، كافيه؟",
+                     "هلا بيك! محتاج إيه في بني سويف؟ 😊"],
+    "thanks":       ["العفو! 😊 في حاجة تانية أساعدك فيها؟","أي خدمة! 😊","بكل سرور! 😊"],
+    "goodbye":      ["مع السلامة! 👋","سلامتك! أي وقت محتاج مساعدة أنا هنا.","باي! ربنا يوفقك 😊"],
+    "clarification":["😊 قصدك إيه بالظبط؟","ممكن توضح أكتر؟","تمام! بتدور على إيه بالظبط؟"],
+    "no_result":    ["😔 مش لاقي حاجة مناسبة. جرب تغير المنطقة أو تسأل بطريقة تانية.",
+                     "معلش، مفيش نتايج. ممكن تحدد المنطقة أو النوع أكتر؟"],
+    "fallback":     ["آسف، مش فاهم قصدك. 😊 قولي محتاج إيه — مطعم، صيدلية، كافيه؟",
+                     "ممكن تسألني عن أي مكان في بني سويف وأنا هساعدك! 😊"],
+}
+
+def fmt_price(x):
+    p = str(x).strip().lower()
+    if not p or p in ("","nan","none"): return ""
+    m = {"cheap":"الأسعار رخيصة","رخيص":"الأسعار رخيصة","اقتصادي":"الأسعار اقتصادية",
+         "medium":"الأسعار متوسطة","متوسط":"الأسعار متوسطة",
+         "expensive":"الأسعار غالية","غالي":"الأسعار غالية"}
+    for k,v in m.items():
+        if k in p: return v+". "
+    return f"السعر: {x}. "
+
+def fmt_rating(x):
+    try:
+        r = normalize_rating(float(x)); stars = min(round(r), 5)
+        return f"تقييمه {r} {'⭐'*stars}. " if r > 0 else ""
+    except: return ""
+
+def fmt_hours(x):
+    h = str(x).strip()
+    if not h or h in ("","nan","none"): return ""
+    if any(k in h.lower() for k in ["24","always","طول اليوم"]): return "مفتوح 24 ساعة. "
+    return f"بيفتح: {h}. "
+
+def fmt_addr(address, location):
+    a=str(address).strip(); l=str(location).strip()
+    if a and a not in ("","nan","none"): return f"عنوانه: {a}. "
+    if l and l not in ("","nan","none"): return f"في {l}. "
+    return ""
+
+def fmt_desc(x, max_words=12):
+    d = str(x).strip()
+    if not d or d in ("","nan","none"): return ""
+    words = d.split()
+    return (" ".join(words[:max_words])+"...") if len(words)>max_words else d+" "
+
+def build_response(place, intent, category=None):
+    if not place: return random.choice(RESPONSE_TEMPLATES["no_result"])
+    tk = get_template_key(intent, category)
+    reply = random.choice(RESPONSE_TEMPLATES[tk]).format(
+        name             = str(place.get("name","")).strip(),
+        location         = str(place.get("location","")).strip() or "بني سويف",
+        price_info       = fmt_price(place.get("price_range","")),
+        rating_info      = fmt_rating(place.get("rating_norm", place.get("rating", 0))),
+        hours_info       = fmt_hours(place.get("opening_hours","")),
+        address_info     = fmt_addr(place.get("address",""), place.get("location","")),
+        description_short= fmt_desc(place.get("description","")),
+    )
+    on = place.get("open_now")
+    if on == 1:   reply += "\n🟢 مفتوح دلوقتي."
+    elif on == 0: reply += "\n🔴 مغلق دلوقتي."
+    return reply
+
+def handle_detail(text, place):
+    if not place: return "مش فاكر إحنا اتكلمنا عن مكان. ممكن تسألني من الأول؟"
+    t = norm(text); name = str(place.get("name","")).strip()
+    if any(w in t for w in ["امتي","امتى","مواعيد","يفتح","تفتح","يقفل"]):
+        st = "🟢 مفتوح" if place.get("open_now")==1 else "🔴 مغلق"
+        return f"⏰ {name} — {fmt_hours(place.get('opening_hours',''))}\n{st} دلوقتي."
+    if any(w in t for w in ["عنوان","فين","وصول","اوصل"]):
+        return f"📍 {name} في {place.get('location','')}.\\nالعنوان: {place.get('address','')}"
+    if any(w in t for w in ["سعر","بكام","تكلف","غالي","رخيص"]):
+        return f"💰 {name} — {fmt_price(place.get('price_range',''))}"
+    if any(w in t for w in ["تقييم","نجوم"]):
+        return f"⭐ {name} — {fmt_rating(place.get('rating_norm', place.get('rating',0)))}"
+    if any(w in t for w in ["رقم","تليفون"]):
+        phone = str(place.get("phone","")).strip()
+        return f"📞 {name} — {phone}" if phone else f"معنديش رقم {name}."
+    return f"📋 {name}:\n{fmt_desc(place.get('description',''), 20)}\n{fmt_hours(place.get('opening_hours',''))}{fmt_rating(place.get('rating_norm',0))}"
+
+#  PREDICT FUNCTIONS
+
+def predict_intent(text, threshold=0.5):
+    override = apply_keyword_override(text)
+    if override: return {"intent": override, "confidence": 1.0}
+    inputs = intent_tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+    with torch.no_grad():
+        outputs = intent_model(**inputs)
+    probs = torch.softmax(outputs.logits, dim=1)
+    pid   = torch.argmax(probs, dim=1).item()
+    conf  = probs[0][pid].item()
+    return {"intent": id2intent[pid] if conf >= threshold else "fallback", "confidence": round(conf, 4)}
+
+def extract_entities(text, min_score=0.40):
+    raw = ner_pipeline([text])[0]; entities = {}
+    for item in raw:
+        rtype = item["entity_group"].lower().strip()
+        val   = re.sub(r"##", "", item["word"].strip()).strip()
+        val   = re.sub(r"\s+", " ", val).strip()
+        score = float(item["score"])
+        if len(val) < 2 or score < min_score: continue
+        mapped = ENTITY_FIELD_MAP.get(rtype, rtype)
+        val_c  = clean_text(val)
+        if mapped not in entities or len(val_c) > len(clean_text(entities[mapped])):
+            entities[mapped] = val_c
+    return entities
+
+#  SESSION
+class Session:
+    def __init__(self, sid="default"):
+        self.sid = sid; self.history=[]; self.last_intent=None
+        self.last_entities={}; self.last_place=None
+        self.last_results=[]; self.result_pointer=0
+        self.context_slots={}; self.turns=0
+
+    def add(self, user, bot, intent, entities, place, results):
+        self.history.append({"turn":self.turns,"user":user,"bot":bot,
+                              "intent":intent,"entities":entities})
+        if intent and intent not in ("fallback","no_result","out_of_scope"):
+            self.last_intent = intent
+            if intent in SEARCH_INTENTS:
+                self.last_entities = entities
+                if place is not None: self.last_place = place
+                if results: self.last_results=results; self.result_pointer=0
+                self._slots(entities)
+        self.turns += 1
+
+    def _slots(self, ents):
+        for s in ["location","category","sub_category","price"]:
+            v = ents.get(s)
+            if v and str(v).strip(): self.context_slots[s] = str(v).strip()
+
+    def merge(self, new_ents):
+        merged = dict(self.context_slots)
+        for k,v in new_ents.items():
+            if v and str(v).strip(): merged[k]=str(v).strip()
+        self._slots(new_ents)
+        return merged
+
+#  MAIN CHAT
+
+def chat(text: str, session: Session, user_lat=None, user_lon=None):
+    result = dict(reply="", intent="", confidence=0.0, entities={}, best_place=None, all_results=[])
+
+    if not text or not text.strip():
+        result.update(reply="الرجاء إدخال سؤال 😊", intent="fallback")
+        session.add("", result["reply"], "fallback", {}, None, [])
+        return result
+
+    if is_out_of_scope(text):
+        reply = "أنا متخصص في إيجاد الأماكن في بني سويف فقط. 😊\nممكن أساعدك تلاقي مطعم، صيدلية، كافيه، ماركت، أو سكن."
+        result.update(reply=reply, intent="out_of_scope")
+        session.add(text, reply, "out_of_scope", {}, None, [])
+        return result
+
+    ref = detect_ref_type(text)
+    if ref == "detail" and session.last_place:
+        reply = handle_detail(text, session.last_place)
+        result.update(reply=reply, intent=session.last_intent or "detail", best_place=session.last_place)
+        session.add(text, reply, result["intent"], {}, session.last_place, [])
+        return result
+
+    if ref == "next" and session.last_results:
+        ptr = session.result_pointer + 1
+        if ptr < len(session.last_results):
+            session.result_pointer = ptr; nxt = session.last_results[ptr]; session.last_place = nxt
+            reply = build_response(nxt, session.last_intent, category=nxt.get("category"))
+            result.update(reply=reply, intent=session.last_intent, best_place=nxt)
+        else:
+            result.update(reply="😔 مفيش نتايج تانية. عايز أدور من الأول؟", intent="no_result")
+        session.add(text, result["reply"], result["intent"], {}, result["best_place"], [])
+        return result
+
+    ir = predict_intent(text); intent = ir["intent"]; conf = ir["confidence"]
+    result["intent"] = intent; result["confidence"] = conf
+
+    if intent in STATIC_INTENTS:
+        result["reply"] = random.choice(RESPONSE_TEMPLATES[get_template_key(intent)])
+        session.add(text, result["reply"], intent, {}, None, [])
+        return result
+
+    if intent == "fallback":
+        if session.last_intent in SEARCH_INTENTS and _loc_continuation(text):
+            intent = session.last_intent; result["intent"] = intent
+        else:
+            result["reply"] = random.choice(RESPONSE_TEMPLATES["fallback"])
+            session.add(text, result["reply"], "fallback", {}, None, [])
+            return result
+
+    if intent not in SEARCH_INTENTS:
+        result["reply"] = random.choice(RESPONSE_TEMPLATES.get(get_template_key(intent), RESPONSE_TEMPLATES["fallback"]))
+        session.add(text, result["reply"], intent, {}, None, [])
+        return result
+
+    ents   = extract_entities(text); result["entities"] = ents
+    merged = session.merge(ents)
+
+    category    = merged.get("category")     or INTENT_TO_CATEGORY.get(intent) or infer_category(text)
+    sub_cat     = merged.get("sub_category")
+    location    = merged.get("location")
+    price_range = merged.get("price")
+    open_only   = ("open_now" in intent or "place_details" in intent)
+
+    df = search_places(text, top_k_final=5, category=category, sub_category=sub_cat,
+                        location=location, price_range=price_range, open_now_only=open_only,
+                        user_lat=user_lat, user_lon=user_lon)
+
+    if df.empty:
+        cl = CLARIFICATION_Q.get(intent, "")
+        reply = random.choice(RESPONSE_TEMPLATES["no_result"]) + (f"\n\n💬 {cl}" if cl else "")
+        result.update(reply=reply, intent="no_result")
+        session.add(text, reply, "no_result", ents, None, [])
+        return result
+
+    all_res = df.to_dict(orient="records"); best = all_res[0]
+    reply   = build_response(best, intent, category=category)
+    if len(all_res) > 1: reply += f"\n\n💬 فيه {len(all_res)} نتيجة — قولي 'تاني' لو عايز غيره."
+
+    result.update(reply=reply, best_place=best, all_results=all_res)
+    session.add(text, reply, intent, ents, best, all_res)
+    return result
+
+#  STARTUP
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    global intent_tokenizer, intent_model, label_encoder, id2intent
+    global ner_pipeline, label2id, id2label
+    global semantic_model, corpus_df, corpus_embeddings, places_df
+
+    print("⏳ Loading models …")
+    intent_tokenizer = AutoTokenizer.from_pretrained(INTENT_DIR)
+    intent_model     = AutoModelForSequenceClassification.from_pretrained(INTENT_DIR)
+    label_encoder    = joblib.load(os.path.join(INTENT_DIR, "label_encoder.pkl"))
+    id2intent        = {i: lbl for i, lbl in enumerate(label_encoder.classes_)}
+    intent_model.eval()
+
+    with open(os.path.join(ENTITY_DIR, "label2id.json"), encoding="utf-8") as f: label2id = json.load(f)
+    with open(os.path.join(ENTITY_DIR, "id2label.json"), encoding="utf-8") as f: id2label = json.load(f)
+    etok = AutoTokenizer.from_pretrained(ENTITY_DIR, local_files_only=True)
+    emod = AutoModelForTokenClassification.from_pretrained(ENTITY_DIR, local_files_only=True)
+    ner_pipeline = pipeline("token-classification", model=emod, tokenizer=etok, aggregation_strategy="first")
+
+    semantic_model = SentenceTransformer(os.path.join(SEMANTIC_DIR, "model"))
+    with open(os.path.join(SEMANTIC_DIR, "semantic_data.pkl"), "rb") as f:
+        sd = pickle.load(f)
+    corpus_df = sd["corpus_df"]; corpus_embeddings = sd["corpus_embeddings"]
+
+    places_df = pd.read_excel(PLACES_FILE)
+    for col in ["place_id","name","category","sub_category","location","address",
+                "price_range","rating","opening_hours","description","lat","lon"]:
+        if col not in places_df.columns: places_df[col] = ""
+    places_df = places_df.fillna("")
+    places_df["category_clean"]     = places_df["category"].apply(clean_text)
+    places_df["sub_category_clean"] = places_df["sub_category"].apply(clean_text)
+    places_df["location_clean"]     = places_df["location"].apply(clean_text)
+    places_df["address_clean"]      = places_df["address"].apply(clean_text)
+    places_df["price_range_clean"]  = places_df["price_range"].apply(clean_text)
+    places_df["description_clean"]  = places_df["description"].apply(clean_text)
+    places_df["search_text_clean"]  = (
+        places_df["name"].astype(str)+" "+places_df["category"].astype(str)+" "+
+        places_df["sub_category"].astype(str)+" "+places_df["location"].astype(str)+" "+
+        places_df["description"].astype(str)
+    ).apply(clean_text)
+
+    print("✅ All models loaded!")
+    yield
+    print("Shutting down.")
+
+#  FASTAPI
+app = FastAPI(title="Beni Suef Chatbot API", version="1.0.0", lifespan=lifespan)
+app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
+
+
+class ChatRequest(BaseModel):
+    message: str
+    session_id: str = "default"
+    user_lat: Optional[float] = None
+    user_lon: Optional[float] = None
+
+class ChatResponse(BaseModel):
+    reply: str
+    intent: str
+    confidence: float
+    entities: dict
+    session_id: str
+    best_place: Optional[dict] = None
+
+
+@app.get("/")
+def root():
+    return {"status": "ok", "message": "Beni Suef Chatbot is running 🚀"}
+
+@app.get("/health")
+def health():
+    return {"status": "healthy",
+            "models_loaded": all([intent_model, ner_pipeline, semantic_model, places_df is not None])}
+
+@app.post("/chat", response_model=ChatResponse)
+def chat_endpoint(req: ChatRequest):
+    if req.session_id not in SESSIONS:
+        SESSIONS[req.session_id] = Session(req.session_id)
+    session = SESSIONS[req.session_id]
+    try:
+        result = chat(req.message, session, req.user_lat, req.user_lon)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+    best = result.get("best_place")
+    if best:
+        best = {k: (float(v) if isinstance(v, (np.floating, np.integer)) else
+                    (None if (isinstance(v, float) and np.isnan(v)) else v))
+                for k, v in best.items()
+                if k in ["place_id","name","category","sub_category","location","address",
+                          "price_range","rating","opening_hours","description","phone",
+                          "lat","lon","open_now","final_score"]}
+
+    return ChatResponse(reply=result["reply"], intent=result["intent"],
+                        confidence=result["confidence"], entities=result["entities"],
+                        session_id=req.session_id, best_place=best)
+
+@app.delete("/session/{session_id}")
+def reset_session(session_id: str):
+    SESSIONS.pop(session_id, None)
+    return {"status": "reset", "session_id": session_id}

requirements.txt

@@ -0,0 +1,11 @@
+fastapi==0.115.0
+uvicorn[standard]==0.30.6
+pydantic==2.8.2
+transformers==4.44.2
+sentence-transformers==3.0.1
+torch==2.4.1
+numpy==1.26.4
+pandas==2.2.2
+openpyxl==3.1.5
+scikit-learn==1.5.2
+joblib==1.4.2