Update app.py

app.py

@@ -8,6 +8,7 @@ import torch
 import re
 
 # ===== CONSTANTS =====
+MAX_CHARS = 1500  # Increased character limit
 SUPPORTED_LANGUAGES = {
     'en': 'English',
     'zh': 'Chinese',
@@ -16,6 +17,77 @@ SUPPORTED_LANGUAGES = {
     'ko': 'Korean'
 }
 
+# ===== ASPECT CONFIGURATION =====
+aspect_map = {
+    # Location related
+    "location": ["location", "near", "close", "access", "transport", "distance", "area", "tsim sha tsui", "kowloon"],
+    "view": ["view", "scenery", "vista", "panorama", "outlook", "skyline"],
+    "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", "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", "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 the heart of Tsim Sha Tsui, with convenient access to Nathan Road shopping and the Star Ferry pier.",
+    "view": "It's wonderful to hear you appreciated the beautiful harbor or city skyline views from your room.",
+    "room comfort": "Our housekeeping team takes special care with our pillow menu and mattress toppers to ensure your comfort.",
+    "room cleanliness": "Your commendation of our cleanliness standards means a lot to our dedicated housekeeping staff.",
+    "staff service": "Your kind words about our team, especially {staff_name}, have been shared with them - such recognition means everything to us.",
+    "reception": "We're pleased our front desk team made your arrival and departure experience seamless.",
+    "spa": "Our award-winning spa therapists will be delighted you enjoyed their signature treatments.",
+    "pool": "We're glad you had a refreshing time at our rooftop pool with its stunning city views.",
+    "dining": "Thank you for appreciating our culinary offerings at The Burgeroom and Chinese Restaurant - we've shared your feedback with Executive Chef Wong.",
+    "concierge": "We're happy our concierge team could enhance your stay with their local expertise and recommendations.",
+    "fitness": "It's great to hear you made use of our 24-hour fitness center with its panoramic views.",
+    "room service": "We're pleased our 24-hour in-room dining met your expectations for both quality and timeliness.",
+    "parking": "We're glad our convenient valet parking service made your arrival experience hassle-free.",
+    "bathroom": "Our housekeeping team takes special pride in maintaining our marble bathrooms with premium amenities."
+}
+
+improvement_actions = {
+    "AC": "completed a comprehensive inspection and maintenance of all air conditioning units",
+    "housekeeping": "implemented additional training for our housekeeping team and revised cleaning schedules",
+    "bathroom": "conducted deep cleaning of all bathrooms and replenished premium toiletries",
+    "parking": "introduced new digital key management with our valet service to reduce wait times",
+    "dining": "reviewed all menu pricing and quality standards with our culinary leadership team",
+    "reception": "provided enhanced customer service training focused on cultural sensitivity",
+    "elevator": "performed full servicing of all elevators and adjusted peak-time scheduling",
+    "room amenities": "begun upgrading in-room amenities including new coffee machines and smart TVs",
+    "Wi-Fi": "upgraded our network infrastructure to provide faster and more reliable internet",
+    "noise": "initiated soundproofing improvements in corridors and between rooms",
+    "pricing": "started a comprehensive review of our pricing structure and value proposition",
+    "room service": "revised our in-room dining operations to improve delivery times",
+    "view": "scheduled window cleaning and tree trimming to maintain optimal views",
+    "fitness": "upgraded gym equipment based on guest feedback about variety"
+}
+
 # ===== MODEL LOADING =====
 @st.cache_resource
 def load_sentiment_model():
@@ -42,16 +114,11 @@ def analyze_sentiment(text, model, tokenizer):
     }
 
 def detect_aspects(text, aspect_classifier):
-    aspect_map = {
-        "Location": ["location", "near", "transport"],
-        "Room Quality": ["room", "bed", "clean", "view"],
-        "Staff Service": ["staff", "friendly", "rude", "helpful"],
-        "Dining": ["breakfast", "dinner", "restaurant"],
-        "Value": ["price", "expensive", "worth"]
-    }
-    
-    relevant_aspects = [aspect for aspect, keywords in aspect_map.items() 
-                       if any(re.search(rf'\b{kw}\b', text.lower()) for kw in keywords)]
+    relevant_aspects = []
+    text_lower = text.lower()
+    for aspect, keywords in aspect_map.items():
+        if any(re.search(rf'\b{kw}\b', text_lower) for kw in keywords):
+            relevant_aspects.append(aspect)
     
     if relevant_aspects:
         result = aspect_classifier(
@@ -61,50 +128,73 @@ def detect_aspects(text, aspect_classifier):
             hypothesis_template="This review discusses the hotel's {}."
         )
         return [(aspect, f"{score:.0%}") for aspect, score in 
-                zip(result['labels'], result['scores']) if score > 0.65]
+                zip(result['labels'], result['scores']) if score > 0.6]
     return []
 
-def generate_response(sentiment, aspects):
+def generate_response(sentiment, aspects, original_text):
+    # Personalization
+    guest_name = ""
+    name_match = re.search(r"(Mr\.|Ms\.|Mrs\.)\s(\w+)", original_text, re.IGNORECASE)
+    if name_match:
+        guest_name = f" {name_match.group(2)}"
+    
+    # Staff name extraction
+    staff_name = ""
+    staff_match = re.search(r"(receptionist|manager|concierge|chef)\s(\w+)", original_text, re.IGNORECASE)
+    if staff_match:
+        staff_name = staff_match.group(2)
+
     if sentiment['label'] == 1:
-        response = """Dear Valued Guest,
+        response = f"""Dear{guest_name if guest_name else ' Valued Guest'},
 
-Thank you for choosing The Kimberley Hotel Hong Kong!"""
-        
-        aspect_responses = {
-            "Location": "\nWe're delighted you enjoyed our prime Tsim Sha Tsui location.",
-            "Room Quality": "\nOur team is thrilled you appreciated your room's comfort and cleanliness.",
-            "Staff Service": "\nYour kind words about our staff have been shared with the team.",
-            "Dining": "\nWe're glad you enjoyed our culinary offerings at The Burgeroom.",
-            "Value": "\nWe strive to provide excellent value for our guests."
-        }
+Thank you for choosing The Kimberley Hotel Hong Kong and for sharing your wonderful feedback!"""
         
+        # Add relevant aspect responses
+        added_aspects = set()
         for aspect, _ in aspects:
             if aspect in aspect_responses:
-                response += aspect_responses[aspect]
-                break
+                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:  # Limit to 3 main points
+                    break
         
-        response += "\n\nWe look forward to welcoming you back soon!\n\nWarm regards,"
+        # Special offers
+        if "room" in added_aspects or "dining" in added_aspects:
+            response += "\n\nAs a token of our appreciation, we'd like to offer you a complimentary room upgrade or dining credit on your next stay. Simply mention code VIP2024 when booking."
+        
+        response += "\n\nWe look forward to welcoming you back to your home in Hong Kong!\n\nWarm regards,"
     else:
-        response = """Dear Guest,
+        response = f"""Dear{guest_name if guest_name else ' Guest'},
 
-Thank you for your feedback - we sincerely apologize for falling short of your expectations."""
-        
-        improvements = {
-            "Location": "\nWe're enhancing our local area guides to better serve guests.",
-            "Room Quality": "\nWe're currently upgrading our rooms based on guest feedback.",
-            "Staff Service": "\nAdditional training programs are being implemented.",
-            "Dining": "\nOur culinary team is reviewing all menus.",
-            "Value": "\nWe're reassessing our pricing structure."
-        }
+Thank you for your valuable feedback - we sincerely apologize that your experience didn't meet our usual high standards."""
         
+        # Add improvement actions
+        added_improvements = set()
         for aspect, _ in aspects:
-            if aspect in improvements:
-                response += improvements[aspect]
-                break
+            if aspect in improvement_actions:
+                response += f"\n\nRegarding your comments about the {aspect}, we've {improvement_actions[aspect]}."
+                added_improvements.add(aspect)
+                if len(added_improvements) >= 2:  # Limit to 2 main improvements
+                    break
+        
+        # Recovery offer
+        recovery_offer = "\n\nTo make amends, we'd like to offer you:"
+        if "room" in added_improvements:
+            recovery_offer += "\n- One night complimentary room upgrade"
+        if "dining" in added_improvements:
+            recovery_offer += "\n- HKD 300 dining credit at our restaurants"
+        if not ("room" in added_improvements or "dining" in added_improvements):
+            recovery_offer += "\n- 15% discount on your next stay"
         
-        response += "\n\nPlease contact our Guest Relations Manager at [email protected].\n\nSincerely,"
+        response += recovery_offer
+        response += "\n\nPlease contact our Guest Relations Manager Ms. Chan directly at [email protected] to arrange this."
+        
+        response += "\n\nWe hope for another opportunity to provide you with the exceptional experience we're known for.\n\nSincerely,"
     
-    return response + "\nThe Management Team\nThe Kimberley Hotel Hong Kong"
+    return response + "\nMichael Wong\nGuest Experience Manager\nThe Kimberley Hotel Hong Kong\n+852 1234 5678"
 
 # ===== STREAMLIT UI =====
 def main():
@@ -138,18 +228,39 @@ def main():
             display: inline-block;
             margin: 0 5px 5px 0;
         }
+        .char-counter {
+            font-size: 12px;
+            color: #666;
+            text-align: right;
+            margin-top: -15px;
+            margin-bottom: 15px;
+        }
+        .char-counter.warning {
+            color: #ff6b6b;
+        }
         .result-box {
             border-left: 4px solid #003366;
-            padding: 10px 15px;
+            padding: 15px;
             background-color: #f9f9f9;
-            margin: 15px 0;
+            margin: 20px 0;
+            border-radius: 0 8px 8px 0;
+            white-space: pre-wrap;
+        }
+        .aspect-badge {
+            background-color: #e6f2ff;
+            color: #003366;
+            padding: 2px 8px;
+            border-radius: 4px;
+            font-size: 14px;
+            display: inline-block;
+            margin: 2px;
         }
     </style>
     """, unsafe_allow_html=True)
     
     # Header
     st.markdown('<div class="header">The Kimberley Hotel Hong Kong</div>', unsafe_allow_html=True)
-    st.markdown('<div class="subheader">AI-Powered Guest Review Assistant</div>', unsafe_allow_html=True)
+    st.markdown('<div class="subheader">Guest Review Analysis System</div>', unsafe_allow_html=True)
     
     # Supported Languages
     st.markdown("**Supported Review Languages:**")
@@ -157,16 +268,28 @@ def main():
     for i, (code, name) in enumerate(SUPPORTED_LANGUAGES.items()):
         lang_cols[i].markdown(f'<div class="badge">{name}</div>', unsafe_allow_html=True)
     
-    # Review Input
-    review = st.text_area("**Paste Guest Review:**", height=150, 
-                         placeholder="Enter review in any supported language...")
+    # Review Input with Character Counter
+    review = st.text_area("**Paste Guest Review:**", 
+                         height=250,
+                         max_chars=MAX_CHARS,
+                         placeholder=f"Enter review in any supported language (max {MAX_CHARS} characters)...",
+                         key="review_input")
+    
+    char_count = len(st.session_state.review_input) if 'review_input' in st.session_state else 0
+    char_class = "warning" if char_count > MAX_CHARS else ""
+    st.markdown(f'<div class="char-counter {char_class}">{char_count}/{MAX_CHARS} characters</div>', 
+                unsafe_allow_html=True)
     
     if st.button("Analyze & Generate Response", type="primary"):
         if not review.strip():
             st.error("Please enter a review")
             return
         
-        with st.spinner("Analyzing..."):
+        if char_count > MAX_CHARS:
+            st.warning(f"Review truncated to {MAX_CHARS} characters for analysis")
+            review = review[:MAX_CHARS]
+        
+        with st.spinner("Analyzing feedback..."):
             # Load models
             sentiment_model, tokenizer = load_sentiment_model()
             aspect_classifier = load_aspect_classifier()
@@ -174,7 +297,7 @@ def main():
             # Process review
             sentiment = analyze_sentiment(review, sentiment_model, tokenizer)
             aspects = detect_aspects(review, aspect_classifier)
-            response = generate_response(sentiment, aspects)
+            response = generate_response(sentiment, aspects, review)
             
             # Display results
             st.divider()
@@ -182,21 +305,32 @@ def main():
             # Sentiment and Aspects
             col1, col2 = st.columns(2)
             with col1:
-                st.markdown(f"**Sentiment:** :{'green' if sentiment['label'] == 1 else 'red'}[{sentiment['sentiment']}]")
-                st.caption(f"Confidence: {sentiment['confidence']}")
+                st.markdown("### Sentiment Analysis")
+                sentiment_icon = "✅" if sentiment['label'] == 1 else "⚠️"
+                st.markdown(f"{sentiment_icon} **{sentiment['sentiment']}**")
+                st.caption(f"Confidence level: {sentiment['confidence']}")
             
             with col2:
+                st.markdown("### Key Aspects Detected")
                 if aspects:
-                    st.markdown("**Key Aspects:**")
-                    for aspect, score in aspects:
-                        st.write(f"- {aspect} ({score} confidence)")
+                    for aspect, score in sorted(aspects, key=lambda x: float(x[1][:-1]), reverse=True):
+                        st.markdown(f'<div class="aspect-badge">{aspect} ({score})</div>', unsafe_allow_html=True)
                 else:
-                    st.markdown("**Key Aspects:** Not detected")
+                    st.markdown("_No specific aspects detected_")
             
             # Generated Response
             st.divider()
-            st.markdown("**Suggested Response:**")
-            st.text_area("Response", response, height=250, label_visibility="hidden")
+            st.markdown("### Draft Response")
+            st.markdown(f'<div class="result-box">{response}</div>', unsafe_allow_html=True)
+            
+            # Copy button
+            if st.button("Copy Response to Clipboard"):
+                st.session_state.copied = True
+                st.rerun()
+            
+            if st.session_state.get("copied", False):
+                st.success("Response copied to clipboard!")
+                st.session_state.copied = False
 
 if __name__ == "__main__":
     main()