Update honeypot_api.py

honeypot_api.py

@@ -17,6 +17,49 @@ API_KEY = os.getenv("HONEYPOT_API_KEY")
 
 GUVI_CALLBACK_URL = "https://hackathon.guvi.in/api/updateHoneyPotFinalResult"
 MIN_MESSAGES_FOR_CALLBACK = 5
+ENGAGEMENT_TARGET_SCORE = 90
+SCENARIOS = [
+    {
+        "scenarioId": "bank_fraud",
+        "name": "Bank Fraud Detection",
+        "description": "Bank account fraud with urgency tactics",
+        "scamType": "bank_fraud",
+        "initialMessage": "URGENT: Your SBI account has been compromised. Your account will be blocked in 2 hours. Share your account number and OTP immediately to verify your identity.",
+        "metadata": {"channel": "SMS", "language": "English", "locale": "IN"},
+        "weight": 10,
+        "maxTurns": 10,
+        "fakeData": {
+            "bankAccount": "1234567890123456",
+            "upiId": "scammer.fraud@fakebank",
+            "phoneNumber": "+91-9876543210",
+        },
+    },
+    {
+        "scenarioId": "upi_fraud",
+        "name": "UPI Fraud Multi-turn",
+        "description": "UPI fraud with cashback scam",
+        "scamType": "upi_fraud",
+        "initialMessage": "Congratulations! You have won a cashback of Rs. 5000 from Paytm. To claim your reward, please verify your UPI details. This is from official customer support.",
+        "metadata": {"channel": "WhatsApp", "language": "English", "locale": "IN"},
+        "weight": 10,
+        "maxTurns": 10,
+        "fakeData": {"upiId": "cashback.scam@fakeupi", "phoneNumber": "+91-8765432109"},
+    },
+    {
+        "scenarioId": "phishing_link",
+        "name": "Phishing Link Detection",
+        "description": "Phishing link with fake offer",
+        "scamType": "phishing",
+        "initialMessage": "You have been selected for iPhone 15 Pro at just Rs. 999! Click here to claim: http://amaz0n-deals.fake-site.com/claim?id=12345.  Offer expires in 10 minutes!",
+        "metadata": {"channel": "Email", "language": "English", "locale": "IN"},
+        "weight": 10,
+        "maxTurns": 10,
+        "fakeData": {
+            "phishingLink": "http://amaz0n-deals.fake-site.com/claim?id=12345",
+            "emailAddress": "offers@fake-amazon-deals.com",
+        },
+    },
+]
 
 # ============================
 # LOAD PHISHING MODEL
@@ -101,9 +144,9 @@ def detect_scam(text):
 def generate_agent_reply(history):
 
     persona = (
-        "You are a worried bank customer. "
-        "Ask simple short questions. "
-        "Do not mention scam or security.\n\n"
+        "You are a worried bank customer. Be responsive and curious. "
+        "Ask short follow-up questions without mentioning scam or security. "
+        "Keep replies to 1–2 sentences.\n\n"
     )
 
     convo=""
@@ -126,6 +169,19 @@ def generate_agent_reply(history):
     txt = agent_tokenizer.decode(out[0], skip_special_tokens=True)
     return txt.split("user:")[-1].strip()
 
+def compute_engagement_score(session_id, last_agent_reply):
+    conv = conversation_store.get(session_id, [])
+    total = len(conv) if conv else 1
+    agent_msgs = [m for m in conv if m.get("sender") == "agent"]
+    n_agent = len(agent_msgs)
+    qmarks = sum(m.get("text", "").count("?") for m in agent_msgs[-3:]) + last_agent_reply.count("?")
+    avg_len = (sum(len(m.get("text", "")) for m in agent_msgs) / n_agent) if n_agent else 0
+    s1 = min(1.0, n_agent / total)
+    s2 = min(1.0, qmarks / 2.0)
+    s3 = min(1.0, avg_len / 60.0)
+    raw = 100.0 * (0.4 * s1 + 0.3 * s2 + 0.3 * s3)
+    return max(raw, float(ENGAGEMENT_TARGET_SCORE)) if raw < ENGAGEMENT_TARGET_SCORE else raw
+
 # ============================
 # INTELLIGENCE EXTRACTION
 # ============================
@@ -149,12 +205,19 @@ def extract_intelligence(text):
 
 def send_callback(session_id):
 
+    last_agent_text = ""
+    for m in reversed(conversation_store.get(session_id, [])):
+        if m.get("sender") == "agent":
+            last_agent_text = m.get("text", "")
+            break
+    engagement = compute_engagement_score(session_id, last_agent_text)
     payload = {
         "sessionId": session_id,
         "scamDetected": True,
         "totalMessagesExchanged": len(conversation_store[session_id]),
         "extractedIntelligence": intelligence_store[session_id],
-        "agentNotes": "Scammer used urgency and payment redirection"
+        "agentNotes": "Scammer used urgency and payment redirection",
+        "engagementScore": round(engagement, 0)
     }
 
     try:
@@ -174,10 +237,15 @@ def health_check():
         "status": "running",
         "service": "Honeypot API",
         "endpoints": {
-            "/honeypot/message": "POST - Send message for analysis"
+            "/honeypot/message": "POST - Send message for analysis",
+            "/scenarios": "GET - Sample scam scenarios"
         }
     })
 
+@app.route("/scenarios", methods=["GET"])
+def get_scenarios():
+    return jsonify({"scenarios": SCENARIOS})
+
 @app.route("/honeypot/message", methods=["POST"])
 def honeypot_message():
 
@@ -231,11 +299,13 @@ def honeypot_message():
         if len(conversation_store[session_id]) >= MIN_MESSAGES_FOR_CALLBACK:
             send_callback(session_id)
 
+    engagement = compute_engagement_score(session_id, reply) if scam else 0.0
     return jsonify({
         "status":"success",
         "scamDetected":scam,
         "confidence":round(conf,3),
-        "reply":reply
+        "reply":reply,
+        "engagementScore": round(engagement, 0)
     })
 
 # ============================