Update app.py

app.py

@@ -31,49 +31,90 @@ SUPPORTED_LANGUAGES = {
 
 # ===== ASPECT CONFIGURATION =====
 aspect_map = {
-    # Location
+    # Location related
     "location": ["location", "near", "close", "access", "transport", "distance", "area", "tsim sha tsui", "kowloon"],
     "view": ["view", "scenery", "vista", "panorama", "outlook", "skyline"],
-    
-    # Room
-    "room comfort": ["comfortable", "bed", "pillows", "mattress", "linens", "cozy"],
-    "room cleanliness": ["clean", "dirty", "spotless", "stains", "hygiene"],
-    
-    # Service
-    "staff service": ["staff", "friendly", "helpful", "rude", "welcoming"],
-    "reception": ["reception", "check-in", "check-out", "front desk"],
-    
+    "parking": ["parking", "valet", "garage", "car park", "vehicle"],
+
+    # Room related
+    "room comfort": ["comfortable", "bed", "pillows", "mattress", "linens", "cozy", "hard", "soft"],
+    "room cleanliness": ["clean", "dirty", "spotless", "stains", "hygiene", "sanitation", "dusty"],
+    "room amenities": ["amenities", "minibar", "coffee", "tea", "fridge", "facilities", "tv", "kettle"],
+    "bathroom": ["bathroom", "shower", "toilet", "sink", "towel", "faucet", "toiletries"],
+
+    # Service related
+    "staff service": ["staff", "friendly", "helpful", "rude", "welcoming", "employee", "manager"],
+    "reception": ["reception", "check-in", "check-out", "front desk", "welcome", "registration"],
+    "housekeeping": ["housekeeping", "maid", "cleaning", "towels", "service", "turndown"],
+    "concierge": ["concierge", "recommendation", "advice", "tips", "guidance", "directions"],
+    "room service": ["room service", "food delivery", "order", "meal", "tray"],
+
     # Facilities
-    "dining": ["breakfast", "dinner", "restaurant", "meal", "food"],
-    "spa": ["spa", "massage", "treatment", "relax"],
-    
+    "dining": ["breakfast", "dinner", "restaurant", "meal", "food", "buffet", "lunch"],
+    "bar": ["bar", "drinks", "cocktail", "wine", "lounge", "happy hour"],
+    "pool": ["pool", "swimming", "jacuzzi", "sun lounger", "deck", "towels"],
+    "spa": ["spa", "massage", "treatment", "relax", "wellness", "sauna"],
+    "fitness": ["gym", "fitness", "exercise", "workout", "training", "weights"],
+
     # Technical
-    "Wi-Fi": ["wifi", "internet", "connection"],
-    "AC": ["air conditioning", "AC", "temperature"]
+    "Wi-Fi": ["wifi", "internet", "connection", "online", "network", "speed"],
+    "AC": ["air conditioning", "AC", "temperature", "heating", "cooling", "ventilation"],
+    "elevator": ["elevator", "lift", "escalator", "vertical transport", "wait"],
+
+    # Value
+    "pricing": ["price", "expensive", "cheap", "value", "rate", "cost", "worth"],
+    "extra charges": ["charge", "fee", "bill", "surcharge", "additional", "hidden"]
 }
 
 aspect_responses = {
-    "location": "We're delighted you enjoyed our prime location in Tsim Sha Tsui.",
-    "view": "It's wonderful to hear you appreciated the views from your room.",
-    "room comfort": "Our team takes special care to ensure room comfort.",
-    "room cleanliness": "Your comments about cleanliness have been noted.",
-    "staff service": "Your feedback about our staff has been shared with the team.",
-    "dining": "We appreciate your comments about our dining options."
+    "location": "We're delighted you enjoyed our prime location and convenient access to local attractions.",
+    "view": "It's wonderful to hear you appreciated the beautiful views from our property.",
+    "room comfort": "Our team is thrilled you found your room comfortable and inviting.",
+    "room cleanliness": "Your commendation of our cleanliness standards means a lot to our housekeeping staff.",
+    "staff service": "Your kind words about our team, especially {staff_name}, have been shared with them.",
+    "reception": "We're pleased our front desk team made your arrival/departure seamless.",
+    "spa": "Our spa practitioners will be delighted you enjoyed their treatments.",
+    "pool": "We're glad you had a refreshing time at our pool facilities.",
+    "dining": "Thank you for appreciating our culinary offerings - we've shared your feedback with our chefs.",
+    "concierge": "We're happy our concierge could enhance your stay with local insights.",
+    "fitness": "It's great to hear you made use of our well-equipped fitness center.",
+    "room service": "We're pleased our in-room dining met your expectations for quality and timeliness.",
+    "parking": "We're glad our parking facilities met your needs during your stay.",
+    "bathroom": "We appreciate your feedback about our bathroom amenities and cleanliness.",
+    "bar": "Thank you for your comments about our bar service and beverage selection.",
+    "housekeeping": "Your feedback about our housekeeping service has been shared with the team.",
+    "Wi-Fi": "We're pleased our internet service met your connectivity needs.",
+    "elevator": "We're glad our elevator service provided convenient access during your stay."
 }
 
 improvement_actions = {
-    "AC": "have addressed the air conditioning issues",
-    "housekeeping": "have reviewed our cleaning procedures",
-    "Wi-Fi": "are upgrading our network infrastructure"
+    "AC": "completed a full inspection and maintenance of all AC units",
+    "housekeeping": "retrained our housekeeping team and adjusted schedules",
+    "bathroom": "conducted deep cleaning and maintenance on all bathrooms",
+    "parking": "implemented new key management protocols with our valet service",
+    "dining": "reviewed our menu pricing and quality with the culinary team",
+    "reception": "provided additional customer service training to our front desk",
+    "elevator": "performed full servicing and testing of all elevators",
+    "room amenities": "begun upgrading in-room amenities based on guest feedback",
+    "noise": "initiated soundproofing improvements in affected areas",
+    "pricing": "started a comprehensive review of our pricing structure",
+    "Wi-Fi": "are upgrading our network infrastructure for better connectivity",
+    "bar": "have reviewed our beverage service and inventory procedures",
+    "staff service": "have implemented additional staff training programs",
+    "room service": "have optimized our food delivery processes",
+    "fitness": "are upgrading our gym equipment based on guest feedback"
 }
 
 # ===== MODEL CONFIGURATION =====
 TRANSLATION_MODELS = {
+    # Translations to English
     'zh-en': 'Helsinki-NLP/opus-mt-zh-en',
     'ja-en': 'Helsinki-NLP/opus-mt-ja-en',
     'ko-en': 'Helsinki-NLP/opus-mt-ko-en',
     'fr-en': 'Helsinki-NLP/opus-mt-fr-en',
     'de-en': 'Helsinki-NLP/opus-mt-de-en',
+    
+    # Translations from English
     'en-zh': 'Helsinki-NLP/opus-mt-en-zh',
     'en-ja': 'Helsinki-NLP/opus-mt-en-ja',
     'en-ko': 'Helsinki-NLP/opus-mt-en-ko',
@@ -148,39 +189,50 @@ def detect_aspects(text, aspect_classifier):
     return []
 
 def generate_response(sentiment, aspects, original_text):
+    # Personalization
     guest_name = ""
+    staff_name = ""
     name_match = re.search(r"(Mr\.|Ms\.|Mrs\.)\s(\w+)", original_text, re.IGNORECASE)
+    staff_match = re.search(r"(receptionist|manager|concierge|chef)\s(\w+)", original_text, re.IGNORECASE)
+    
     if name_match:
         guest_name = f" {name_match.group(2)}"
-    
+    if staff_match:
+        staff_name = staff_match.group(2)
+
     if sentiment['label'] == 1:
         response = f"""Dear{guest_name if guest_name else ' Valued Guest'},
 
-Thank you for choosing The Kimberley Hotel Hong Kong."""
+Thank you for choosing The Kimberley Hotel Hong Kong and for sharing your feedback."""
         
+        # Add relevant aspect responses
         added_aspects = set()
         for aspect, _ in sorted(aspects, key=lambda x: float(x[1][:-1]), reverse=True):
             if aspect in aspect_responses:
-                response += "\n\n" + aspect_responses[aspect]
+                response_text = aspect_responses[aspect]
+                if "{staff_name}" in response_text and staff_name:
+                    response_text = response_text.format(staff_name=staff_name)
+                response += "\n\n" + response_text
                 added_aspects.add(aspect)
-                if len(added_aspects) >= 2:
+                if len(added_aspects) >= 3:
                     break
         
         response += "\n\nWe look forward to welcoming you back.\n\nBest regards,"
     else:
         response = f"""Dear{guest_name if guest_name else ' Guest'},
 
-Thank you for your feedback."""
+Thank you for your feedback. We sincerely apologize for any inconvenience you experienced."""
         
+        # Add improvement actions
         added_improvements = set()
         for aspect, _ in sorted(aspects, key=lambda x: float(x[1][:-1]), reverse=True):
             if aspect in improvement_actions:
-                response += f"\n\nRegarding your comments about the {aspect}, we {improvement_actions[aspect]}."
+                response += f"\n\nRegarding the {aspect}, we've {improvement_actions[aspect]}."
                 added_improvements.add(aspect)
                 if len(added_improvements) >= 2:
                     break
         
-        response += "\n\nPlease contact us if we can assist further.\n\nSincerely,"
+        response += "\n\nPlease contact our Guest Relations team if we can assist you further.\n\nSincerely,"
     
     return response + "\nSam Tse\nGuest Relations Manager\nThe Kimberley Hotel Hong Kong"
 
@@ -199,6 +251,7 @@ def main():
         .char-counter { font-size: 12px; color: #666; text-align: right; margin-top: -15px; }
         .char-counter.warning { color: #ff6b6b; }
         .result-box { border-left: 4px solid #003366; padding: 15px; background-color: #f9f9f9; margin: 20px 0; }
+        .aspect-badge { background-color: #e6f2ff; padding: 2px 8px; border-radius: 4px; display: inline-block; margin: 2px; }
     </style>
     """, unsafe_allow_html=True)
     
@@ -252,7 +305,7 @@ def main():
                 
                 sentiment = analyze_sentiment(analysis_text, sentiment_model, tokenizer)
                 aspects = detect_aspects(analysis_text, aspect_classifier)
-                response = generate_response(sentiment, aspects, analysis_text)
+                response = generate_response(sentiment, aspects, review)  # Use original text for name extraction
                 
                 # Translate response back if needed
                 if review_lang != 'en':
@@ -274,7 +327,7 @@ def main():
                     st.markdown("### Key Aspects")
                     if aspects:
                         for aspect, score in sorted(aspects, key=lambda x: float(x[1][:-1]), reverse=True):
-                            st.markdown(f"- {aspect} ({score})")
+                            st.markdown(f'<div class="aspect-badge">{aspect} ({score})</div>', unsafe_allow_html=True)
                     else:
                         st.markdown("_No specific aspects detected_")