Delete app.py

app.py

@@ -1,692 +0,0 @@
-# -*- 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,
-)
-from huggingface_hub import snapshot_download
-
-"""Paths"""
-
-try:
-    BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-except NameError:
-    BASE_DIR = os.getcwd()
-
-# HuggingFace Model Repos
-INTENT_REPO   = "Youmnaaaa/intent-arabert-ff"
-ENTITY_REPO   = "Youmnaaaa/entity-hybrid-ff"
-SEMANTIC_REPO = "Youmnaaaa/semantic-search-ff"
-
-# ملف الأماكن جوا الـ Space
-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"],
-}
-
-_CAT_MAP = {
-    "مطعم":"restaurant","مطاعم":"restaurant","طعام":"restaurant","اكل":"restaurant",
-    "صيدليه":"pharmacy","صيدلية":"pharmacy","صيدله":"pharmacy","دواء":"pharmacy","دوا":"pharmacy",
-    "كافيه":"cafe","كافية":"cafe","كوفي":"cafe","قهوه":"cafe","قهوة":"cafe","كافيتيريا":"cafe",
-    "سوبرماركت":"supermarket","ماركت":"supermarket","بقاله":"supermarket","بقالة":"supermarket","هايبر":"supermarket",
-    "شقه":"housing","شقة":"housing","ايجار":"housing","إيجار":"housing",
-    "فندق":"housing","سكن":"housing","هوستل":"housing",
-}
-
-def normalize_category(cat):
-    if not cat: return cat
-    cat_s = str(cat).strip()
-    if cat_s in ("restaurant","pharmacy","cafe","supermarket","housing"):
-        return cat_s
-    if cat_s in _CAT_MAP:
-        return _CAT_MAP[cat_s]
-    for ar, en in _CAT_MAP.items():
-        if ar in cat_s or cat_s in ar:
-            return en
-    return cat_s
-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 إيجار، 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    = normalize_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
-
-@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("⏳ Downloading models from HuggingFace …")
-
-    # تحميل الموديلز من HuggingFace Model Hub
-    intent_local   = snapshot_download(INTENT_REPO)
-    entity_local   = snapshot_download(ENTITY_REPO)
-    semantic_local = snapshot_download(SEMANTIC_REPO)
-
-    print("⏳ Loading Intent model …")
-    intent_tokenizer = AutoTokenizer.from_pretrained(intent_local)
-    intent_model     = AutoModelForSequenceClassification.from_pretrained(intent_local)
-    label_encoder    = joblib.load(os.path.join(intent_local, "label_encoder.pkl"))
-    id2intent        = {i: lbl for i, lbl in enumerate(label_encoder.classes_)}
-    intent_model.eval()
-
-    print("⏳ Loading Entity model …")
-    with open(os.path.join(entity_local, "label2id.json"), encoding="utf-8") as f: label2id = json.load(f)
-    with open(os.path.join(entity_local, "id2label.json"), encoding="utf-8") as f: id2label = json.load(f)
-    etok = AutoTokenizer.from_pretrained(entity_local)
-    emod = AutoModelForTokenClassification.from_pretrained(entity_local)
-    ner_pipeline = pipeline("token-classification", model=emod, tokenizer=etok, aggregation_strategy="first")
-
-    print("⏳ Loading Semantic model …")
-    semantic_model = SentenceTransformer("Youmnaaaa/semantic-search-ff")
-    from huggingface_hub import hf_hub_download
-    pkl_path = hf_hub_download(
-        repo_id="Youmnaaaa/semantic-search-ff",
-        filename="semantic_data.pkl"
-    )
-    with open(pkl_path, "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
-    place_cards: list = []
-
-
-@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"]}
-
-# بعد
-    all_cards = []
-    for p in result.get("all_results", []):
-        card = {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 p.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"]}
-        all_cards.append(card)
-
-    return ChatResponse(reply=result["reply"], intent=result["intent"],
-                        confidence=result["confidence"], entities=result["entities"],
-                        session_id=req.session_id, best_place=best,
-                        place_cards=all_cards)
-@app.delete("/session/{session_id}")
-def reset_session(session_id: str):
-    SESSIONS.pop(session_id, None)
-    return {"status": "reset", "session_id": session_id}