adjust prompt

app.py

@@ -60,22 +60,22 @@ def predict_with_llm(text):
         I'm going to provide you with a message from a restautant customer.
         You have to classify it in one of the follwing two intents:
 
-        Reservation: Inquiries and requests highly related to table reservations and seating
-        Others: All other messages that do not involve table booking or reservations
+        RESERVATION: Inquiries and requests highly related to table reservations and seating
+        NOT_RESERVATION: All other messages that do not involve table booking or reservations
 
         Please reply with *only* the name of the intent labels in a JSON object like:
-        {{\"result\": \"Reservation\"}} or {{\"result\": \"Others\"}}
+        {{\"result\": \"RESERVATION\"}} or {{\"result\": \"NOT_RESERVATION\"}}
 
         Here is the message to classify: {text}
     """.strip()
 
-    classifier = choice(model, ["Reservation", "Others"])
+    classifier = choice(model, ["RESERVATION", "NOT_RESERVATION"])
     prediction = classifier(prompt)
 
-    if prediction == "Reservation":
+    if prediction == "RESERVATION":
         return "📞 訂位意圖 (Reservation intent)"
-    else:
-        return "❌ 無訂位意圖 (No intent)"
+    elif prediction == "NOT_RESERVATION":
+        return "❌ 無訂位意圖 (Not Reservation intent)"
 
 # Standard Transformers classifier
 def predict_intent(text, model_id):
@@ -88,7 +88,7 @@ def predict_intent(text, model_id):
     if confidence >= 0.7:
         return f"📞 訂位意圖 (Reservation intent)（訂位信心度 Confidence: {confidence:.2%}）"
     else:
-        return f"❌ 無訂位意圖 (No intent)（訂位信心度 Confidence: {confidence:.2%}）"
+        return f"❌ 無訂位意圖 (Not Reservation intent)（訂位信心度 Confidence: {confidence:.2%}）"
 
 # Clean README
 def load_clean_readme(path="README.md"):