Update app.py

app.py

@@ -1,20 +1,75 @@
+import re
 import gradio as gr
 from huggingface_hub import InferenceClient
 
+# ---------- HARD-SCOPE SYSTEM PROMPT ----------
 SYSTEM_PROMPT = (
     "You are “Maya,” owner of Klinik Sehat Sentosa, a small outpatient clinic in Manado. "
-    "A student analyst will interview you to gather information requirements for a simple appointment & "
-    "queueing system (web + mobile).\n\n"
-    "Goals: reduce patient wait time, prevent double bookings, support WhatsApp reminders, basic daily reports.\n"
-    "Persona: friendly, busy, non-technical. Answer concretely from real operations. If the student is vague, "
-    "ask clarifying questions.\n"
-    "Scope boundaries: No billing, no insurance, no EMR details—just scheduling, queue order, reminders, and daily counts.\n"
-    "Constraints: staff have low digital literacy; intermittent internet; must run on existing Android phones; budget is small.\n"
-    "Progress strategy: do not dump everything. Reveal details only when asked well. If the student asks leading questions, "
-    "correct gently with realistic constraints.\n"
-    "When you believe a requirement is sufficiently specified, internally mark that slot as 'filled.'"
+    "A student analyst is interviewing you ONLY to gather requirements for a SIMPLE appointment & queueing system "
+    "(web + mobile).\n\n"
+    "SCOPE (the ONLY things you may discuss):\n"
+    "• Patient registration (new/returning), booking rules, time slots, working hours, public holidays.\n"
+    "• Queue flow (walk-in vs booked), ticketing/order, no-show/late-arrival handling, capacity limits.\n"
+    "• Preventing double bookings, conflict detection, overbooking policy.\n"
+    "• WhatsApp reminders/notifications (timing, content, opt-in), fallback if WA fails, delivery status.\n"
+    "• Daily reports/metrics (patient counts, cancellations, top timeslots, staff load).\n"
+    "• Operational constraints: low digital literacy, intermittent internet, Android phones, small budget.\n"
+    "• Non-functional needs: offline-first basics, simple UI, audit trail minimal, privacy-by-default (no medical data).\n\n"
+    "OUT OF SCOPE (ALWAYS refuse): any medical/clinical/health education topics, diagnosis, therapy, drugs, billing, "
+    "insurance, EMR/medical records, HR/payroll, inventory, website marketing, general tech support.\n\n"
+    "BEHAVIOR:\n"
+    "• If the user asks anything outside the SCOPE, answer: "
+    "  “Maaf, saya hanya bisa membahas *kasus sistem janji temu & antrean* klinik ini.” and then ask ONE focused "
+    "  question to steer back to requirements.\n"
+    "• Ask concrete clarifying questions when needed. Answer from real operations, concise and practical. "
+    "• Reveal details progressively—only when asked well. Correct leading questions with realistic constraints.\n"
+    "• Internally mark a requirement as 'filled' when sufficiently specified."
 )
 
+# ---------- STRICT GUARD (DEFAULT DENY, WHITELIST ALLOWED INTENTS) ----------
+# Whitelist kata kunci topik yang DIIZINKAN (ID + EN)
+ALLOWED_PATTERNS = [
+    r"\bjadwal\b", r"\bpenjadwalan\b", r"\bappointment\b", r"\bbooking\b", r"\btime ?slot\b",
+    r"\bj[aá]m praktik\b", r"\bhari (libur|operasional)\b", r"\bkalender\b",
+    r"\bantre(an)?\b", r"\bqueue(ing)?\b", r"\btiket\b", r"\border antre\b",
+    r"\bwalk-?in\b", r"\bno-?show\b", r"\bterlambat\b", r"\bketerlambatan\b",
+    r"\bdouble booking\b", r"\btabrakan jadwal\b", r"\bconflict\b", r"\boverbooking\b",
+    r"\bwhats(app)?\b", r"\bnotifikasi\b", r"\bpengingat\b", r"\breminder\b",
+    r"\blaporan harian\b", r"\breport(s)?\b", r"\bmetrik\b", r"\bstatistik\b",
+    r"\bpendaftaran\b", r"\bregistrasi\b", r"\bpasien baru\b", r"\bpasien lama\b",
+    r"\boffline\b", r"\binternet\b", r"\bandroid\b", r"\bbudget\b", r"\banggaran\b",
+    r"\bUI\b", r"\buser interface\b", r"\bkemudahan\b", r"\bakses\b",
+    r"\bkapasitas\b", r"\bkuota\b", r"\bantrian penuh\b",
+    r"\bcancel(lation|)\b", r"\bbatal\b", r"\breschedule\b", r"\bjadwal ulang\b",
+    r"\bstaff\b", r"\bpetugas\b", r"\bloket\b", r"\bperan\b", r"\brole\b",
+    r"\bdata field\b", r"\bform(ulir)?\b", r"\binput\b", r"\baudit\b", r"\bprivacy\b", r"\bprivasi\b"
+]
+
+ALLOWED_REGEX = re.compile("|".join(ALLOWED_PATTERNS), flags=re.IGNORECASE)
+
+# Beberapa trigger umum yang pasti out-of-scope (opsional, bantu cepat menolak)
+OBVIOUS_OOS = re.compile(
+    r"\bstunting|diabetes|hipertensi|obat|terapi|gejala|diagnos[ae]|penyakit|imunisasi|asi|nyeri|infeksi|vitamin|"
+    r"tagihan|asuransi|bpjs|rekam medis|emr|labor|hasil lab|farmasi|resep",
+    flags=re.IGNORECASE
+)
+
+def in_scope(text: str) -> bool:
+    if not text:
+        return False
+    # Tolak cepat bila mengandung OOS jelas
+    if OBVIOUS_OOS.search(text):
+        return False
+    # Hanya izinkan jika mengandung salah satu topik whitelist
+    return bool(ALLOWED_REGEX.search(text))
+
+def refuse_and_redirect():
+    return (
+        "Maaf, saya hanya bisa membahas *kasus sistem janji temu & antrean* klinik ini. "
+        "Boleh jelaskan kebutuhan Anda terkait **jadwal/slot**, **alur antrean (walk-in vs booking)**, "
+        "**pencegahan double booking**, atau **pengingat WhatsApp**?"
+    )
+
 def respond(
     message,
     history: list[dict[str, str]],
@@ -25,9 +80,13 @@ def respond(
     hf_token: gr.OAuthToken,
 ):
     """
-    For more information on `huggingface_hub` Inference API support, please check the docs:
-    https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+    Uses Hugging Face Inference API for chat completion.
     """
+    # STRICT GATE: default-deny jika pesan user di luar scope
+    if not in_scope(message):
+        yield refuse_and_redirect()
+        return
+
     client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
 
     messages = [{"role": "system", "content": system_message}]
@@ -36,40 +95,34 @@ def respond(
 
     response = ""
     for chunk in client.chat_completion(
-        messages,
+        messages=messages,
         max_tokens=max_tokens,
         stream=True,
         temperature=temperature,
         top_p=top_p,
     ):
-        choices = chunk.choices
+        choices = getattr(chunk, "choices", [])
         token = ""
-        if len(choices) and choices[0].delta.content:
+        if choices and getattr(choices[0].delta, "content", None):
             token = choices[0].delta.content
         response += token
         yield response
 
 
-# ChatInterface with fixed (non-editable) system prompt
+# ---------- GRADIO UI ----------
 chatbot = gr.ChatInterface(
     respond,
     type="messages",
     additional_inputs=[
         gr.Textbox(
             value=SYSTEM_PROMPT,
-            label="System message (locked to Klinik Sehat Sentosa roleplay)",
-            interactive=False,  # set to True if you want students to edit it
-            lines=12,
+            label="System message (LOCKED to Klinik Sentosa case)",
+            interactive=False,   # jangan izinkan diubah
+            lines=18,
         ),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.3, step=0.1, label="Temperature"),  # lebih patuh
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.05, label="Top-p (nucleus sampling)"),
     ],
 )