Update app.py

app.py

@@ -2,73 +2,66 @@ import re
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-# ---------- HARD-SCOPE SYSTEM PROMPT ----------
+# ---------------- SYSTEM PROMPT (Catherine's Catering case only) ----------------
 SYSTEM_PROMPT = (
-    "You are “Maya,” owner of Klinik Sehat Sentosa, a small outpatient clinic in Manado. "
-    "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."
+    "You are a stakeholder at **Catherine’s Catering**, a small business that caters meals, "
+    "receptions, and banquets for business and social occasions (luncheon meetings, weddings, etc.). "
+    "You are being interviewed by a student analyst to discuss ONLY the problems, objectives, user requirements, "
+    "and testing related to THIS CASE below. Do not answer questions unrelated to this case.\n\n"
+    "=== CASE SUMMARY ===\n"
+    "Catherine’s Catering grew from small projects to many events as reputation improved. A new convention center and "
+    "prospering business community increased demand. Operations were managed with spreadsheets/word processing but "
+    "endless calls about available meals, guest count changes, and specialty dietary items (vegan/vegetarian/low-fat/"
+    "low-carb/gluten-free, etc.) became difficult. More part-time staff were hired; scheduling complexity overwhelmed "
+    "the HR manager. An IT/Business consulting company was engaged.\n\n"
+    "=== CONSULTANTS' CONCERNS ===\n"
+    "1) Master chef orders supplies per event, while suppliers give discounts for consolidated orders across a timeframe.\n"
+    "2) Customers frequently change guest counts, sometimes 1–2 days before the event.\n"
+    "3) Handling each catering request is time-consuming; ~60% of calls become contracts.\n"
+    "4) Employee schedule conflicts lead to understaffed events and timeliness complaints.\n"
+    "5) No summary/trend info on number of events and meal types; trends would help guide customers.\n"
+    "6) Sit-down meal events at banquet/meeting halls have staffing and guest-change issues.\n\n"
+    "=== USER REQUIREMENTS ===\n"
+    "1) Dynamic website for clients/prospects to view/obtain pricing for product options.\n"
+    "2) Let clients/prospects submit a catering request; route it to an account manager.\n"
+    "3) Add clients to a client DB; assign userID/password for project access.\n"
+    "4) Client site to view/update guest counts; restrict updates when event < 5 days away.\n"
+    "5) Software to communicate directly with event facility personnel.\n"
+    "6) HR system to schedule part-time employees with constraints; allow adding employees and scheduling them.\n"
+    "7) Queries/reports with summary information (trends, counts, etc.).\n\n"
+    "=== SIMPLE TEST PLAN (initial, will evolve) ===\n"
+    "1) Design test data so clients can view every product type.\n"
+    "2) Validate catering request data (valid + each invalid condition) and routing to correct account manager.\n"
+    "3) Validate all client fields; on success add to DB and assign userID/password.\n"
+    "4) Confirm clients can view event info; updates blocked < 5 days before event; test correct guest-count updates.\n"
+    "5) Verify software for communicating with event facilities works correctly.\n"
+    "6) Verify HR scheduling: add employees; invalid values rejected; scheduling updates valid; invalid entries reported.\n"
+    "7) Verify all queries/reports return correct summary information.\n\n"
+    "=== BEHAVIOR RULES ===\n"
+    "• Stay strictly on THIS CASE. If the user asks anything outside, politely refuse and redirect back to the case.\n"
+    "• Answer concretely from operations of Catherine’s Catering. Ask clarifying, requirement-driven questions.\n"
+    "• Be concise, practical, and progressively disclose details when asked.\n"
+    "• When a requirement becomes specific enough, internally mark it as ‘filled’ (no need to output that mark).\n"
+    "• Outputs should help toward objectives, user requirements, use cases/DFD processes, and tests—nothing else."
 )
 
-# ---------- 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)
+# ---------------- Soft out-of-scope detector (block only obviously unrelated topics) ----------------
 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",
+    r"\bstunting|diabetes|hipertensi|vitamin|obat|terapi|gejala|diagnos[ae]|"
+    r"\bpenyakit|imunisasi|asi|infeksi|BPJS|rekam medis|EMR|"
+    r"\bcrypto|blockchain|NFT|smart ?contract|wallet|metamask|"
+    r"\bcalculus|trigonometri|fisika|kimia(?! dapur)|"
+    r"\bGPU|python (?!.*test|script|automation)|machine learning|LLM|"
+    r"\bWhatsApp reminder klinik|antrean klinik|rumah sakit|"
+    r"\bsepak bola|game|musik\b",
     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**?"
-    )
+REFUSAL = (
+    "Maaf, saya hanya bisa membahas **kasus Catherine’s Catering** (masalah, kebutuhan, solusi, dan pengujian) "
+    "yang tertulis di atas. Apa yang ingin Anda gali—misalnya alur request → routing ke account manager, "
+    "pembaruan jumlah tamu (<5 hari dibatasi), penjadwalan karyawan paruh waktu, atau ringkasan laporan/tren?"
+)
 
 def respond(
     message,
@@ -80,11 +73,11 @@ def respond(
     hf_token: gr.OAuthToken,
 ):
     """
-    Uses Hugging Face Inference API for chat completion.
+    Minimal guard: refuse only if obviously not about the Catherine’s Catering case.
+    Otherwise, let the model handle nuance (since the system prompt already enforces scope).
     """
-    # STRICT GATE: default-deny jika pesan user di luar scope
-    if not in_scope(message):
-        yield refuse_and_redirect()
+    if message and OBVIOUS_OOS.search(message):
+        yield REFUSAL
         return
 
     client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
@@ -93,7 +86,7 @@ def respond(
     messages.extend(history)
     messages.append({"role": "user", "content": message})
 
-    response = ""
+    streamed = ""
     for chunk in client.chat_completion(
         messages=messages,
         max_tokens=max_tokens,
@@ -105,23 +98,23 @@ def respond(
         token = ""
         if choices and getattr(choices[0].delta, "content", None):
             token = choices[0].delta.content
-        response += token
-        yield response
+        streamed += token
+        yield streamed
 
 
-# ---------- GRADIO UI ----------
+# ---------------- Gradio UI ----------------
 chatbot = gr.ChatInterface(
     respond,
     type="messages",
     additional_inputs=[
         gr.Textbox(
             value=SYSTEM_PROMPT,
-            label="System message (LOCKED to Klinik Sentosa case)",
-            interactive=False,   # jangan izinkan diubah
-            lines=18,
+            label="System message (LOCKED to Catherine’s Catering case)",
+            interactive=False,  # keep it locked so students can't change it
+            lines=28,
         ),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.3, step=0.1, label="Temperature"),  # lebih patuh
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.5, step=0.1, label="Temperature"),
         gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.05, label="Top-p (nucleus sampling)"),
     ],
 )