Update app.py

app.py

@@ -1,7 +1,7 @@
 """
-Hotel Review Analysis System for The Mira Hong Kong
+Hotel Review Analysis and Response System
 ISOM5240 Group Project
-Automatically analyzes guest reviews in multiple languages, performs sentiment
+Automatically analyzes hotel guest reviews in multiple languages, performs sentiment
 analysis and aspect detection, then generates professional responses.
 """
 
@@ -70,7 +70,7 @@ aspect_responses = {
     "view": "It's wonderful to hear you appreciated the stunning views of Victoria Harbour from your room.",
     "room comfort": "Our team is thrilled you found your room comfortable and well-appointed for your needs.",
     "room cleanliness": "Your commendation of our cleanliness standards means a great deal to our housekeeping team who work diligently to maintain our high standards.",
-    "staff service": "Your kind words about our team, especially {staff_name}, have been shared with them and are greatly appreciated.",
+    "staff service": "Your kind words about our team have been shared with them and are greatly appreciated.",
     "reception": "We're pleased our front desk team made your arrival and departure experience seamless and welcoming.",
     "spa": "Our spa practitioners will be delighted you enjoyed their treatments and the relaxing ambiance of our wellness center.",
     "pool": "We're glad you had a refreshing time at our rooftop pool with its panoramic city views.",
@@ -188,28 +188,22 @@ def detect_aspects(text, aspect_classifier):
     return []
 
 def generate_response(sentiment, aspects, original_text):
-    # Personalization
+    # Personalization - only extract guest name
     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 Mira Hong Kong and for sharing your kind feedback with us."""
+Thank you for choosing our hotel and for sharing your kind feedback with us."""
         
         # 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_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) >= 3:
@@ -240,8 +234,7 @@ Thank you for taking the time to share your feedback with us. We sincerely regre
     
 Should you require any further assistance, please don't hesitate to contact our Guest Relations team.
 Sincerely yours,
-Sam Tse
-Guest Relations Manager
+Guest Relations Team
 The Mira Hong Kong
 +852 1234 5678 | [email protected]"""
     
@@ -250,7 +243,7 @@ The Mira Hong Kong
 # ===== STREAMLIT UI =====
 def main():
     st.set_page_config(
-        page_title="Mira Review Assistant",
+        page_title="Hotel Review Analysis and Response System",
         page_icon="🏨",
         layout="centered"
     )
@@ -267,8 +260,8 @@ def main():
     </style>
     """, unsafe_allow_html=True)
     
-    st.markdown('<div class="header">The Mira Hong Kong</div>', unsafe_allow_html=True)
-    st.markdown('<div class="subheader">Guest Review Analysis System</div>', unsafe_allow_html=True)
+    st.markdown('<div class="header">Hotel Review Analysis and Response System</div>', unsafe_allow_html=True)
+    st.markdown('<div class="subheader">The Mira Hong Kong</div>', unsafe_allow_html=True)
     
     review = st.text_area("**Paste Guest Review:**", 
                          height=200,
@@ -317,7 +310,7 @@ def main():
                 
                 sentiment = analyze_sentiment(analysis_text, sentiment_model, tokenizer)
                 aspects = detect_aspects(analysis_text, aspect_classifier)
-                response = generate_response(sentiment, aspects, review)  # Use original text for name extraction
+                response = generate_response(sentiment, aspects, review)
                 
                 # Translate response back if needed
                 if review_lang != 'en':