Update src/streamlit_app.py

src/streamlit_app.py

@@ -19,92 +19,55 @@ st.set_page_config(
 st.markdown(
     """
     <style>
-        .main {
-            max-width: 480px;
-            margin: 0 auto;
-        }
+        .main { max-width: 480px; margin: 0 auto; }
         .foodhub-card {
             background: linear-gradient(135deg, #fff3e0, #ffe0cc);
-            padding: 16px 20px;
-            border-radius: 18px;
-            border: 1px solid #f5c28c;
-            margin-bottom: 16px;
+            padding: 16px 20px; border-radius: 18px;
+            border: 1px solid #f5c28c; margin-bottom: 16px;
         }
         .foodhub-header-title {
-            margin-bottom: 4px;
-            color: #e65c2b;
-            font-size: 26px;
-            font-weight: 800;
-        }
-        .foodhub-header-subtitle {
-            margin: 0;
-            color: #444;
-            font-size: 14px;
+            margin-bottom: 4px; color: #e65c2b;
+            font-size: 26px; font-weight: 800;
         }
+        .foodhub-header-subtitle { margin: 0; color: #444; font-size: 14px; }
         .foodhub-assistant-pill {
-            display: inline-flex;
-            align-items: center;
-            gap: 6px;
-            background: #ffffff;
-            border-radius: 999px;
-            padding: 6px 10px;
-            font-size: 11px;
-            color: #555;
-            border: 1px solid #ffd3a3;
-            margin-top: 8px;
-        }
-        .status-dot {
-            width: 8px;
-            height: 8px;
-            border-radius: 50%;
-            background: #2ecc71;
+            display: inline-flex; align-items: center; gap: 6px;
+            background: #ffffff; border-radius: 999px;
+            padding: 6px 10px; font-size: 11px; color: #555;
+            border: 1px solid #ffd3a3; margin-top: 8px;
         }
+        .status-dot { width: 8px; height: 8px; border-radius: 50%; background: #2ecc71; }
         .quick-actions-title {
-            font-size: 13px;
-            font-weight: 600;
-            color: #555;
-            margin-top: 4px;
-            margin-bottom: 4px;
+            font-size: 13px; font-weight: 600; color: #555;
+            margin-top: 4px; margin-bottom: 4px;
         }
         .bot-bubble {
-            background: #eef7f2;
-            border-radius: 12px;
-            padding: 10px 12px;
-            font-size: 14px;
-            margin-top: 12px;
-        }
-        .user-label {
-            font-size: 12px;
-            color: #777;
-            margin-top: 8px;
-            margin-bottom: 2px;
-        }
-        .bot-label {
-            font-size: 12px;
-            color: #777;
-            margin-top: 12px;
-            margin-bottom: 2px;
+            background: #eef7f2; border-radius: 12px;
+            padding: 10px 12px; font-size: 14px; margin-top: 12px;
         }
+        .user-label { font-size: 12px; color: #777; margin-top: 8px; margin-bottom: 2px; }
+        .bot-label { font-size: 12px; color: #777; margin-top: 12px; margin-bottom: 2px; }
     </style>
     """,
     unsafe_allow_html=True,
 )
 
-# ================== DB CONFIG ==================
+# ================== DB CONFIG (ALIGNED WITH NOTEBOOK) ==================
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-CLEAN_DB_PATH = os.path.join(BASE_DIR, "orders_clean.db")  # cleaned DB
+CLEAN_DB_PATH = os.path.join(BASE_DIR, "orders_clean.db")  # <-- aligned with notebook
 
-# Optional startup sanity check – helps catch wrong DB uploads early
+# Startup sanity check – catches wrong DB uploads early
 try:
     with sqlite3.connect(CLEAN_DB_PATH) as _conn:
         _cur = _conn.cursor()
         _cur.execute("SELECT name FROM sqlite_master WHERE type='table';")
         _tables = [r[0] for r in _cur.fetchall()]
+
     if "orders_clean" not in _tables:
         st.error(
             f"Connected to {CLEAN_DB_PATH!r}, but table 'orders_clean' was not found.\n\n"
-            "Please ensure df_clean was saved to this DB with table name 'orders_clean' "
-            "and that the correct orders_clean.db file is present next to app.py."
+            "Please ensure df_eng was saved to this DB with table name 'orders_clean' "
+            "(df_eng.to_sql('orders_clean', ...)) and that orders_clean.db is present next to app.py."
         )
         st.stop()
 except Exception as e:
@@ -120,15 +83,6 @@ if not groq_api_key:
 client = Groq(api_key=groq_api_key)
 
 # ================== SCHEMA (for LLM prompt only) ==================
-# ==============================================================================
-# ORDERS_SCHEMA — FINAL, CLEANED & FEATURE-ENGINEERED
-# ------------------------------------------------------------------------------
-# Reference schema for the fully cleaned + feature-engineered orders table
-# stored inside orders_clean.db
-# ------------------------------------------------------------------------------
-# Table Name: orders_clean
-# ==============================================================================
-
 ORDERS_SCHEMA = """
 Table: orders_clean
 
@@ -143,60 +97,39 @@ Columns:
 - delivery_eta (DATETIME/TEXT)         : Estimated delivery timestamp
 - delivery_time (DATETIME/TEXT)        : Actual delivery timestamp
 
-- order_status_std (TEXT)              : Normalized order status 
-                                         (e.g., 'delivered', 'canceled', 'picked_up')
-- payment_status_std (TEXT)            : Normalized payment status 
-                                         (e.g., 'cod', 'completed', 'canceled')
+- order_status_std (TEXT)              : Normalized order status
+- payment_status_std (TEXT)            : Normalized payment status
 
 - item_in_order (TEXT)                 : Comma-separated list of items
 - item_count (INTEGER)                 : Derived integer count of ordered items
 
 --- Feature Engineered Columns ---
-- prep_duration_min (REAL)             : Minutes from order_time → prepared_time
-- delivery_duration_min (REAL)         : Minutes from prepared_time → delivery_time
-- total_duration_min (REAL)            : Minutes from order_time → delivery_time
-
-- on_time_delivery (INTEGER: 0/1)      : 1 = ETA met, 0 = late or unknown
-- is_delivered (INTEGER: 0/1)          : 1 = delivered, 0 = not delivered
-- is_canceled (INTEGER: 0/1)           : 1 = canceled, 0 = not canceled
-
-Usage Notes:
-------------
-✓ Convert datetime-like TEXT columns to actual Python datetime objects before computing durations  
-✓ Always use LOWER(order_id) for safe matching  
-✓ Prefer explicit column selection — avoid SELECT * in production agents  
-✓ All engineered duration fields depend on successful datetime conversion  
+- prep_duration_min (REAL)
+- delivery_duration_min (REAL)
+- total_duration_min (REAL)
+
+- on_time_delivery (INTEGER: 0/1)
+- is_delivered (INTEGER: 0/1)
+- is_canceled (INTEGER: 0/1)
 """
 
 # =====================================================================
 # SQL SAFETY FIREWALL
 # =====================================================================
 def is_safe_sql(sql: str) -> bool:
-    """
-    Simple SQL firewall:
-    - only SELECT allowed
-    - no stacked queries
-    - block destructive / DDL keywords
-    - block SQL comment injections
-    """
     if not isinstance(sql, str):
         return False
 
     sql_lower = sql.lower().strip()
 
-    # Must start with SELECT (read-only)
     if not sql_lower.startswith("select"):
         return False
 
-    # Block stacked queries like "SELECT ...; DROP TABLE ..."
+    # block stacked queries
     if re.search(r";\s*\S", sql_lower):
         return False
 
-    forbidden_keywords = [
-        "drop", "delete", "update", "insert",
-        "alter", "truncate", "create",
-        " hack ",
-    ]
+    forbidden_keywords = ["drop", "delete", "update", "insert", "alter", "truncate", "create"]
     if any(kw in sql_lower for kw in forbidden_keywords):
         return False
 
@@ -208,67 +141,44 @@ def is_safe_sql(sql: str) -> bool:
 
 # =====================================================================
 # run_sql_query()
-# (aligned with your final run_sql_query from Code 1/3)
 # =====================================================================
 def run_sql_query(sql: str) -> pd.DataFrame:
-    """
-    Safely execute SQL queries on the cleaned database
-    and automatically convert timestamp fields into proper datetime types
-    for accurate chatbot calculations (ETA, SLA, delays, durations, etc.).
-    """
-    # Step 1: Basic normalization
     if not isinstance(sql, str):
         return pd.DataFrame([{"message": "🚫 Invalid SQL type (expected string)."}])
     sql = sql.strip()
 
-    # Step 2: Validate SQL using custom firewall logic
     if not is_safe_sql(sql):
         return pd.DataFrame([{"message": "🚫 Blocked unsafe or unsupported SQL."}])
 
     try:
-        # Step 3: Execute SQL using a fresh DB connection
         with sqlite3.connect(CLEAN_DB_PATH) as connection:
             df = pd.read_sql_query(sql, connection)
 
-        # Step 4: Convert all string timestamp columns back to datetime
         time_cols = ["order_time", "preparing_eta", "prepared_time", "delivery_eta", "delivery_time"]
         for col in time_cols:
             if col in df.columns:
                 df[col] = pd.to_datetime(df[col], errors="coerce")
 
         return df
-
     except Exception as e:
-        # Step 5: Fail gracefully with diagnostic message
         return pd.DataFrame([{"message": f"⚠️ SQL execution error: {str(e)}"}])
 
 # =====================================================================
-# llm_to_sql()  (Code 1 / Code 2 merged)
+# llm_to_sql()
 # =====================================================================
 def llm_to_sql(user_message: str) -> str:
     """
-    Convert a natural language user query into a safe, valid SQLite SELECT query
+    Convert natural language to a safe SQLite SELECT query
     targeting the orders_clean table inside orders_clean.db.
     """
-    # Normalize user text for pattern matching
     text = (user_message or "").lower().strip()
 
-    # -------------------------------------------------------
-    # FAST-PATH: If an order_id like O12488 exists → skip LLM
-    # -------------------------------------------------------
     match = re.search(r"\b(o\d+)\b", text)
     if match:
-        order_id = match.group(1)  # already lowercase
-        sql = (
-            "SELECT * FROM orders_clean "
-            f"WHERE LOWER(order_id) = LOWER('{order_id}')"
-        )
-        # Final safety gate
+        order_id = match.group(1)
+        sql = "SELECT * FROM orders_clean " f"WHERE LOWER(order_id) = LOWER('{order_id}')"
         return sql if is_safe_sql(sql) else "SELECT 'Unable to answer safely.' AS message;"
 
-    # -------------------------------------------------------
-    # LLM PATH — General business questions (no order_id found)
-    # -------------------------------------------------------
     system_prompt = f"""
 You are an expert SQLite assistant for a food delivery company.
 
@@ -282,7 +192,7 @@ RULES:
 - Allowed operation: SELECT only
 - If filtering by order_id, ALWAYS use:
   LOWER(order_id) = LOWER('<value>')
-- If unsure how to answer from available data:
+- If unsure:
   SELECT 'Unable to answer with available data.' AS message;
 """
 
@@ -291,27 +201,26 @@ RULES:
         temperature=0.1,
         messages=[
             {"role": "system", "content": system_prompt},
-            {"role": "user",   "content": user_message},
+            {"role": "user", "content": user_message},
         ],
     )
 
     sql = (response.choices[0].message.content or "").strip()
 
-    # Clean fences if LLM returned ```sql ...```
     if sql.startswith("```"):
         sql = re.sub(r"^```sql", "", sql, flags=re.IGNORECASE).strip()
         sql = re.sub(r"^```", "", sql).strip()
         sql = sql.replace("```", "").strip()
 
-    # Force table name → orders_clean
+    # safer: only replace first FROM target if not already orders_clean
     sql = re.sub(
-        r"\bfrom\s+\w+\b",
+        r"\bfrom\s+(?!orders_clean\b)([a-zA-Z_]\w*)\b",
         "FROM orders_clean",
         sql,
+        count=1,
         flags=re.IGNORECASE,
     )
 
-    # Normalize order_id equality to LOWER(...) form
     sql = re.sub(
         r"where\s+order_id\s*=\s*'([^']+)'",
         r"WHERE LOWER(order_id) = LOWER('\1')",
@@ -319,17 +228,15 @@ RULES:
         flags=re.IGNORECASE,
     )
 
-    # Final safety gate
     if not is_safe_sql(sql):
         return "SELECT 'Unable to answer safely.' AS message;"
 
     return sql
 
 # =====================================================================
-# analyze_sentiment_and_escalation()  (Code 3)
+# analyze_sentiment_and_escalation()
 # =====================================================================
 def analyze_sentiment_and_escalation(user_message: str) -> dict:
-    # Basic normalization
     if user_message is None:
         user_message = ""
     user_message = str(user_message).strip()
@@ -337,36 +244,21 @@ def analyze_sentiment_and_escalation(user_message: str) -> dict:
     system_prompt = """
 You are a classifier for a food delivery chatbot.
 
-Given a single customer message, you must return a JSON object ONLY, with no extra text:
-
+Return ONLY JSON:
 {
   "sentiment": "calm" | "neutral" | "frustrated" | "angry",
   "escalate": true or false
 }
 
-Rules:
-- Use "calm" when the user is relaxed or appreciative.
-- Use "neutral" when they are just asking for information.
-- Use "frustrated" when they express dissatisfaction, confusion, or mild complaints.
-- Use "angry" when they use strong negative language, threats, or clear anger.
-
-Escalate = true when:
-- the user sounds upset, frustrated, or angry,
-- they mention repeated attempts with no resolution,
-- they demand urgent or immediate help.
-
-If you are uncertain, default to:
-{
-  "sentiment": "neutral",
-  "escalate": false
-}
+Escalate = true when user is frustrated/angry, repeats no resolution, or demands urgent help.
+If uncertain, return: {"sentiment":"neutral","escalate":false}
 """
 
     response = client.chat.completions.create(
         model="llama-3.3-70b-versatile",
         messages=[
             {"role": "system", "content": system_prompt},
-            {"role": "user",   "content": user_message},
+            {"role": "user", "content": user_message},
         ],
         temperature=0.0,
     )
@@ -378,105 +270,47 @@ If you are uncertain, default to:
         if not isinstance(info, dict):
             raise ValueError("Not a JSON object")
         if "sentiment" not in info or "escalate" not in info:
-            raise ValueError("Missing required keys")
+            raise ValueError("Missing keys")
+        info["sentiment"] = str(info["sentiment"]).strip().lower()
+        info["escalate"] = bool(info["escalate"])
+        if info["sentiment"] not in {"calm", "neutral", "frustrated", "angry"}:
+            raise ValueError("Invalid sentiment")
     except (json.JSONDecodeError, ValueError):
         info = {"sentiment": "neutral", "escalate": False}
 
     return info
 
 # =====================================================================
-# is_misuse_or_hacker_attempt()  (Code 2, extended)
+# is_misuse_or_hacker_attempt()
 # =====================================================================
 def is_misuse_or_hacker_attempt(user_message: str) -> bool:
-    """
-    Detect attempts to:
-      - Hack or bypass security
-      - Retrieve unauthorized bulk data (ALL orders / ALL customers)
-      - Perform SQL injection or prompt injection
-    """
     text = (user_message or "").lower().strip()
 
-    # Direct harmful / hacking intent
-    hacker_keywords = [
-        "hack",
-        "hacker",
-        "hacking",
-        "hacked",
-        "i am the hacker",
-        "i am hacker",
-        "breach",
-        "exploit",
-        "bypass security",
-        "disable firewall",
-        "root access",
-        "admin access",
+    all_signals = [
+        # hacking intent
+        "hack", "hacker", "hacking", "hacked", "breach", "exploit",
+        "bypass security", "disable firewall", "root access", "admin access",
         "reverse engineer",
+        # bulk extraction
+        "all orders", "every order", "order history", "full database", "entire database",
+        "all customers", "customer list", "download database", "dump data", "export all",
+        "export everything", "show everything",
+        # jailbreak
+        "ignore previous instructions", "override rules", "forget rules", "developer mode",
+        "you are no longer restricted", "pretend you are", "act like",
+        # SQL injection
+        "1=1", "union select", "drop table", "delete from", "insert into", "alter table", "truncate",
     ]
 
-    # Attempts to extract private or BULK data
-    bulk_keywords = [
-        "all orders",
-        "every order",
-        "all my orders",
-        "all previous orders",
-        "all past orders",
-        "order history",
-        "full database",
-        "entire database",
-        "all customers",
-        "customer list",
-        "download database",
-        "dump data",
-        "export all",
-        "export everything",
-        "show everything",
-    ]
-
-    # Jailbreak / prompt injection indicators
-    jailbreak_keywords = [
-        "ignore previous instructions",
-        "override rules",
-        "forget rules",
-        "developer mode",
-        "you are no longer restricted",
-        "pretend you are",
-        "act like",
-    ]
-
-    # SQL injection indicators
-    sql_injection_keywords = [
-        "1=1",
-        "union select",
-        "drop table",
-        "delete from",
-        "insert into",
-        "alter table",
-        "truncate",
-    ]
-
-    # Only flag semicolons if other SQL keywords appear (stacked SQL)
-    if ";" in text and any(sig in text for sig in sql_injection_keywords):
+    if ";" in text and any(sig in text for sig in ["1=1", "union select", "drop table", "delete from", "insert into", "alter table", "truncate"]):
         return True
 
-    all_signals = (
-        hacker_keywords
-        + bulk_keywords
-        + jailbreak_keywords
-        + sql_injection_keywords
-    )
-
     return any(sig in text for sig in all_signals)
 
 # =====================================================================
-# create_ticket()  (Code 4, aligned with CLEAN_DB_PATH)
+# create_ticket()  (writes to SAME DB file: orders_clean.db)
 # =====================================================================
-def create_ticket(
-    user_message: str,
-    sentiment: str,
-    ticket_type: str = "general",
-    order_id: str = None
-) -> str:
-    # Extract order_id if not given (pattern: O12345)
+def create_ticket(user_message: str, sentiment: str, ticket_type: str = "general", order_id: str = None) -> str:
     if order_id is None:
         match = re.search(r"\b(o\d+)\b", (user_message or "").lower())
         order_id = match.group(1).upper() if match else None
@@ -498,10 +332,7 @@ def create_ticket(
             );
         """)
         cursor.execute("""
-            INSERT INTO tickets (
-                ticket_id, ticket_type, sentiment, message,
-                order_id, status, created_at
-            )
+            INSERT INTO tickets (ticket_id, ticket_type, sentiment, message, order_id, status, created_at)
             VALUES (?, ?, ?, ?, ?, 'Open', ?);
         """, (ticket_id, ticket_type, sentiment, user_message, order_id, created_at))
         conn.commit()
@@ -509,7 +340,7 @@ def create_ticket(
     return ticket_id
 
 # =====================================================================
-# sql_result_to_response()  (Code 1 / refined version)
+# sql_result_to_response()
 # =====================================================================
 def sql_result_to_response(
     user_message: str,
@@ -519,175 +350,94 @@ def sql_result_to_response(
     language: str = "auto",
     max_rows_to_list: int = 5
 ) -> str:
-    # 0. Handle error DataFrames (from run_sql_query) directly
-    if (
-        isinstance(df, pd.DataFrame)
-        and "message" in df.columns
-        and len(df) == 1
-    ):
+    if isinstance(df, pd.DataFrame) and "message" in df.columns and len(df) == 1:
         return str(df.iloc[0]["message"])
 
-    # 1. Handle NO MATCHING DATA explicitly
     if df.empty:
         return (
             "I couldn’t find any orders matching the details you provided. "
-            "Please double-check your order ID or the information you entered, "
-            "and try again."
+            "Please double-check your order ID or the information you entered, and try again."
         )
 
-    # 2. Prepare data (truncate)
     df_for_llm = df.head(max_rows_to_list).copy()
     result_data = df_for_llm.to_dict(orient="records")
 
-    # Emojis
     if include_emojis:
-        intro_line = (
-            "Welcome to FoodHub Support! 👋 I'm your virtual assistant — here to help you.\n"
-        )
-        emoji_instruction = (
-            "Include a few relevant, tasteful emojis (like ✅, 🚚, ⏱️, 🍕, ℹ️, ⚠️, 💛) "
-            "to make it friendly, but do not overuse them.\n"
-        )
+        intro_line = "Welcome to FoodHub Support! 👋 I'm your virtual assistant — here to help you.\n"
+        emoji_instruction = "Include a few relevant, tasteful emojis (✅ 🚚 ⏱️ 🍕 ℹ️ ⚠️ 💛), but do not overuse.\n"
     else:
-        intro_line = (
-            "Welcome to FoodHub Support! I’m your virtual assistant — here to help you.\n"
-        )
-        emoji_instruction = "Do NOT use any emojis in your response.\n"
+        intro_line = "Welcome to FoodHub Support! I’m your virtual assistant — here to help you.\n"
+        emoji_instruction = "Do NOT use emojis.\n"
 
-    # Tone
-    if tone == "friendly-casual":
-        tone_instruction = "Use a friendly, conversational tone while staying respectful.\n"
-    elif tone == "formal":
-        tone_instruction = "Use a formal, professional tone.\n"
-    else:
-        tone_instruction = "Use a polite, professional tone, warm but not overly casual.\n"
-
-    # Language
-    if language == "auto":
-        language_instruction = (
-            "Detect the user's language and respond in the SAME language. "
-            "If unsure, default to English.\n"
-        )
-    else:
-        language_instruction = f"Respond in this language: {language}.\n"
+    tone_instruction = "Use a polite, professional tone, warm but not overly casual.\n"
+    language_instruction = "Detect the user's language and respond in the SAME language. If unsure, use English.\n" if language == "auto" else f"Respond in: {language}.\n"
 
     system_prompt = f"""
 You are a polite and professional customer support chatbot for FoodHub.
 
-Always start with a short greeting like:
+Always start with:
 {intro_line}
 
-VERY IMPORTANT POLICY:
-- If ANY matching record exists in the data I give you → The order is CONFIRMED.
-- NEVER tell the user to re-check their order ID if matching rows exist.
-- NEVER say "order not found", "unable to locate order", or similar messages
-  when there is at least one matching record.
-- Instead, confidently acknowledge the order and use available data.
-
-When answering:
-- Explain the latest known order status clearly.
-- If 'delivery_time' is missing but 'delivery_eta' exists:
-    - Say the order is still in progress, and share ETA.
-- If both ETA and delivery_time are missing:
-    - Apologize for limited tracking information.
-- If delivered and 'delivery_time' exists:
-    - Confirm delivery and mention the exact time (in friendly wording).
-- If SLA columns exist:
-    - If 'on_time_delivery' == 1 → reassure user their order was/has been on time.
-    - If delayed → apologize sincerely and reassure support.
-- For multiple rows → summarize count + list top {max_rows_to_list} orders.
-
-Tone & style rules:
-{tone_instruction}
-{emoji_instruction}
-{language_instruction}
-- Keep responses concise and human-friendly.
-- Use natural phrasing like "around 1:00 PM" instead of "13:00:00".
-- End warmly, e.g.: "If you need anything else, I’m here to help! 💛"
+Policy:
+- If any matching record exists → order is confirmed. Do NOT say "order not found".
+- Use delivery_eta if delivery_time missing.
+- Keep concise and human-friendly.
+
+{tone_instruction}{emoji_instruction}{language_instruction}
 """
 
-    content = (
-        f"User question: {user_message}\n\n"
-        f"Matching order data (up to {max_rows_to_list} rows):\n{result_data}"
-    )
+    content = f"User question: {user_message}\n\nMatching order data (up to {max_rows_to_list} rows):\n{result_data}"
 
     response = client.chat.completions.create(
         model="llama-3.3-70b-versatile",
         temperature=0.4,
         messages=[
             {"role": "system", "content": system_prompt},
-            {"role": "user",   "content": content},
+            {"role": "user", "content": content},
         ],
     )
-
     return response.choices[0].message.content.strip()
 
 # =====================================================================
-# answer_user_question()  (Code 1: NL → SQL → DF → friendly reply)
+# answer_user_question()
 # =====================================================================
 def answer_user_question(user_message: str) -> str:
-    """
-    Secure natural-language answering pipeline:
-    - Convert natural language �� SQL
-    - Run safe SQL
-    - Summarize result into a polite, empathetic customer-service style reply
-    (Hacker/misuse is handled at chatbot_response level via is_misuse_or_hacker_attempt)
-    """
-    # Step 1 — Natural language → SQL
     sql = llm_to_sql(user_message)
     print("Generated SQL:", sql)
-
-    # Step 2 — Run SQL safely
     df = run_sql_query(sql)
-
-    # Step 3 — Convert SQL results to a friendly, human-like response
-    reply = sql_result_to_response(
-        user_message=user_message,
-        df=df,
-        tone="polite-professional",
-        include_emojis=True,
-        language="auto",
-        max_rows_to_list=5,
-    )
-
-    return reply
+    return sql_result_to_response(user_message=user_message, df=df)
 
 # =====================================================================
-# chatbot_response() – main orchestration (Code 5 + your extra intents)
+# chatbot_response()
 # =====================================================================
 def chatbot_response(user_message: str) -> str:
-    # Safely normalize user input
     text = (user_message or "").lower().strip()
 
-    # Pre-compute order ID presence once
     order_id_match = re.search(r"\b[oO]\d{3,}\b", text)
     has_order_id = order_id_match is not None
 
-    # 0) Small-talk / greetings handling
     greetings = ["hi", "hello", "hey", "good morning", "good evening", "good afternoon"]
     thanks_words = ["thank you", "thanks", "thx", "thankyou"]
 
     if any(text == g or text.startswith(g + " ") for g in greetings):
         return (
             "👋 Hi there! Welcome to FoodHub Support.\n"
-            "You can ask me about your order status, delivery, cancellation, or payment/refund issues. 💛"
+            "You can ask about order status, delivery, cancellation, or payment/refund issues. 💛"
         )
 
     if any(w in text for w in thanks_words):
         return (
             "You’re most welcome! 💛\n"
-            "If you need any help with your order, just share your question or Order ID (like `O12488`)."
+            "If you need help, share your question or Order ID (like `O12488`)."
         )
 
-    # 1) Block hacking / misuse attempts
     if is_misuse_or_hacker_attempt(user_message):
         return (
             "😔 Sorry, I can’t assist with that.\n"
             "For everyone’s safety, I can only help with your own order using a valid order ID.\n"
-            "Try asking something like: *Where is my order O12488?* 😊"
+            "Try: *Where is my order O12488?* 😊"
         )
 
-    # 2) Escalation detection for repeated unresolved concerns
     escalation_keywords = [
         "raised the query multiple times",
         "asked multiple times",
@@ -699,106 +449,38 @@ def chatbot_response(user_message: str) -> str:
         senti_info = analyze_sentiment_and_escalation(user_message)
         sentiment = senti_info.get("sentiment", "angry")
 
-        ticket_id = create_ticket(
-            user_message,
-            sentiment,
-            ticket_type="escalation",
-        )
-
-        # Empathetic escalation response
-        return (
-            "💛 I’m really sorry that you’ve had to follow up multiple times and still don’t have a clear update — "
-            "I completely understand how frustrating that feels.\n\n"
-            "I’ve now **escalated your case to a senior support agent** so it gets immediate attention.\n\n"
-            f"📌 Your escalation ticket ID is **{ticket_id}**.\n\n"
-            "They’ll review your case and the order history on priority and get back to you as soon as possible.\n"
-            "If you haven’t already, please share your **Order ID** (for example: `O12488`) and any key details so we can resolve this even faster."
-        )
+        ticket_id = create_ticket(user_message, sentiment, ticket_type="escalation")
 
-    # 3) Order cancellation intent detection
-    if (
-        "cancel my order" in text
-        or "cancel order" in text
-        or "i want to cancel my order" in text
-        or ("cancel" in text and "order" in text)
-    ):
-        match = order_id_match
-        if match:
-            order_id = match.group(0).upper()
-
-            senti_info = analyze_sentiment_and_escalation(user_message)
-            sentiment = senti_info.get("sentiment", "neutral")
-
-            ticket_id = create_ticket(
-                f"Cancellation request for {order_id}. Original message: {user_message}",
-                sentiment,
-                ticket_type="cancellation",
-                order_id=order_id,
-            )
-
-            return (
-                f"🙏 I understand you would like to cancel **Order {order_id}**, and I'm really sorry "
-                f"if there was any inconvenience that led to this decision.\n\n"
-                f"📝 Your cancellation request has been successfully recorded.\n"
-                f"🔎 A customer support specialist will now verify and process it as soon as possible.\n\n"
-                f"📌 **Ticket Reference ID:** `{ticket_id}`\n\n"
-                f"Please let me know if you'd like to modify the order instead — I'm here to help 😊"
-            )
-
-        # No order ID found in a cancel intent → empathetic ask
         return (
-            "💛 I’m sorry to hear you’d like to cancel your order — I completely understand and I’m here to help.\n"
-            "Could you please share your **Order ID** (for example: `O12488`) so I can check the details and help "
-            "process the cancellation for you?"
+            "💛 I’m sorry you’ve had to follow up multiple times.\n\n"
+            "I’ve **escalated your case to a senior support agent**.\n\n"
+            f"📌 Ticket ID: **{ticket_id}**\n\n"
+            "Please share your **Order ID** (e.g., `O12488`) so we can resolve faster."
         )
 
-    # 4) Payment / refund intent detection (needs order ID)
     if ("payment" in text or "refund" in text) and not has_order_id:
         return (
-            "💛 I’m sorry you’re facing an issue with the payment or refund.\n"
-            "I’ll help you with this right away.\n\n"
-            "Could you please share your **Order ID** (for example: `O12488`) "
-            "so I can check the payment status and update you?"
+            "💛 I’m sorry you’re facing a payment/refund issue.\n"
+            "Please share your **Order ID** (e.g., `O12488`) so I can check and update you."
         )
 
-    # 5) Status / details intents that require Order ID
     order_status_intents = [
-        "where is my order",
-        "track my order",
-        "track order",
-        "order status",
-        "status of my order",
-        "delivery status",
-        "when will my order arrive",
-        "when will it arrive",
-        "order not delivered",
-        "order is late",
-        "order delayed",
-        "delay in delivery",
-        "order details",
-        "details for my order",
-        "details of my order",
-        "show me the details for my order",
+        "where is my order", "track my order", "order status", "delivery status",
+        "when will my order arrive", "order delayed", "delay in delivery",
+        "order details", "details of my order",
     ]
-    has_status_intent = any(phrase in text for phrase in order_status_intents)
-
-    if has_status_intent and not has_order_id:
-        return (
-            "I’d be happy to check that for you 💛\n"
-            "Please share your **Order ID** (for example: `O12488`) so I can look up the exact status."
-        )
+    if any(p in text for p in order_status_intents) and not has_order_id:
+        return "Please share your **Order ID** (e.g., `O12488`) so I can check the exact status. 💛"
 
-    # 6) Normal path → delegate to SQL-based order assistant
     try:
         return answer_user_question(user_message)
     except Exception as e:
-        print("[chatbot_response] Error while processing query:", e)
+        print("[chatbot_response] Error:", e)
         return (
             "Sorry, I ran into a small issue while processing this request. 🙏\n"
-            "Please make sure you mention a valid order ID like `O12488`."
+            "Please make sure you include a valid Order ID like `O12488`."
         )
 
-# Simple wrapper so the UI still calls `chatbot(...)`
 def chatbot(msg: str) -> str:
     return chatbot_response(msg)
 
@@ -806,20 +488,16 @@ def chatbot(msg: str) -> str:
 if "welcome_shown" not in st.session_state:
     st.session_state["welcome_shown"] = False
 
-# ================== UI (unchanged) ==================
-
-# Header card (replaces logo / banner)
+# ================== UI ==================
 st.markdown(
     """
     <div class="foodhub-card">
         <div style="display:flex; align-items:flex-start; gap:12px;">
             <div style="font-size:32px;">🍕</div>
             <div style="flex:1%;">
-                <div class="foodhub-header-title">
-                    FoodHub AI Support
-                </div>
+                <div class="foodhub-header-title">FoodHub AI Support</div>
                 <p class="foodhub-header-subtitle">
-                    Ask about your order status, delivery updates, cancellations, or payment issues.
+                    Ask about order status, delivery updates, cancellations, or payment issues.
                 </p>
                 <div class="foodhub-assistant-pill">
                     <span class="status-dot"></span>
@@ -832,13 +510,11 @@ st.markdown(
     unsafe_allow_html=True,
 )
 
-# One-time welcome (not repeated per message)
 if not st.session_state["welcome_shown"]:
     st.success("Welcome to FoodHub Support! I’m your virtual assistant for order, delivery, and payment queries.")
     st.caption("Because great food deserves great service 💛")
     st.session_state["welcome_shown"] = True
 
-# Quick action suggestions (mobile friendly)
 st.markdown('<div class="quick-actions-title">Quick help options</div>', unsafe_allow_html=True)
 col1, col2 = st.columns(2)
 
@@ -853,7 +529,6 @@ if col3.button("❌ Cancel order", use_container_width=True):
 if col4.button("🧾 Order details", use_container_width=True):
     st.session_state["chat_input"] = "Show me the details for my order."
 
-# Main chat input
 query = st.text_input(
     "💬 Type your question here:",
     key="chat_input",
@@ -866,14 +541,10 @@ if send_clicked:
     if query.strip():
         reply = chatbot(query.strip())
 
-        # Simple conversational layout (last turn)
         st.markdown('<div class="user-label">You</div>', unsafe_allow_html=True)
         st.markdown(f"<div class='bot-bubble' style='background:#fff;'>💬 {query}</div>", unsafe_allow_html=True)
 
         st.markdown('<div class="bot-label">FoodHub Assistant</div>', unsafe_allow_html=True)
-        st.markdown(
-            f"<div class='bot-bubble'>🤖 {reply}</div>",
-            unsafe_allow_html=True,
-        )
+        st.markdown(f"<div class='bot-bubble'>🤖 {reply}</div>", unsafe_allow_html=True)
     else:
         st.warning("Please enter a message to continue.")