app.py

"""
Hotel Review Analysis System for The Kimberley Hotel Hong Kong
ISOM5240 Group Project

Automatically analyzes guest reviews in multiple languages, performs sentiment
analysis and aspect detection, then generates professional responses.
"""

import streamlit as st
from transformers import (
    pipeline,
    AutoModelForSequenceClassification,
    AutoTokenizer
)
import torch
import re
from langdetect import detect

# ===== CONSTANTS =====
MAX_CHARS = 500  # Character limit for reviews

# Supported languages with their display names
SUPPORTED_LANGUAGES = {
    'en': 'English',
    'zh': 'Chinese',
    'ja': 'Japanese',
    'ko': 'Korean',
    'fr': 'French',
    'de': 'German'
}

# ===== 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 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": "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',
    'en-fr': 'Helsinki-NLP/opus-mt-en-fr',
    'en-de': 'Helsinki-NLP/opus-mt-en-de'
}

# ===== MODEL LOADING =====
@st.cache_resource
def load_sentiment_model():
    model = AutoModelForSequenceClassification.from_pretrained("smtsead/fine_tuned_bertweet_hotel")
    tokenizer = AutoTokenizer.from_pretrained('finiteautomata/bertweet-base-sentiment-analysis')
    return model, tokenizer

@st.cache_resource
def load_aspect_classifier():
    return pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-base-zeroshot-v1.1-all-33")

@st.cache_resource
def load_translation_model(src_lang, target_lang='en'):
    model_key = f"{src_lang}-{target_lang}"
    if model_key not in TRANSLATION_MODELS:
        raise ValueError(f"Unsupported translation: {src_lang}→{target_lang}")
    return pipeline("translation", model=TRANSLATION_MODELS[model_key])

# ===== CORE FUNCTIONS =====
def detect_language(text):
    try:
        lang = detect(text)
        return 'zh' if lang in ['zh', 'yue'] else lang if lang in SUPPORTED_LANGUAGES else 'en'
    except:
        return 'en'

def translate_text(text, src_lang, target_lang='en'):
    try:
        if src_lang == target_lang:
            return {'translation': text, 'source_lang': src_lang}
        translator = load_translation_model(src_lang, target_lang)
        result = translator(text)[0]['translation_text']
        return {'translation': result, 'source_lang': src_lang}
    except Exception as e:
        return {'error': str(e)}

def analyze_sentiment(text, model, tokenizer):
    inputs = tokenizer(text, padding=True, truncation=True, max_length=512, return_tensors='pt')
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
        predicted_label = torch.argmax(probs).item()
        confidence = torch.max(probs).item()
    return {
        'label': predicted_label,
        'confidence': f"{confidence:.0%}",
        'sentiment': 'POSITIVE' if predicted_label else 'NEGATIVE'
    }

def detect_aspects(text, aspect_classifier):
    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(
            text,
            candidate_labels=relevant_aspects,
            multi_label=True,
            hypothesis_template="This review discusses the hotel's {}."
        )
        return [(aspect, f"{score:.0%}") for aspect, score in zip(result['labels'], result['scores']) if score > 0.6]
    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 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:
                    break
        
        response += "\n\nWe look forward to welcoming you back for another memorable stay."
    else:
        response = f"""Dear{guest_name if guest_name else ' Guest'},

Thank you for taking the time to share your feedback with us. We sincerely regret that your experience did not meet your expectations."""
        
        # Add improvement actions
        added_improvements = set()
        improvement_text = ""
        for aspect, _ in sorted(aspects, key=lambda x: float(x[1][:-1]), reverse=True):
            if aspect in improvement_actions:
                improvement_text += f"\n- Regarding the {aspect}, we have {improvement_actions[aspect]}"
                added_improvements.add(aspect)
                if len(added_improvements) >= 2:
                    break
        
        if improvement_text:
            response += "\n\nTo address your concerns:" + improvement_text
        
        response += "\n\nYour feedback is invaluable to us as we strive to improve our services."
    
    # Common closing
    response += """
    
Should you require any further assistance, please don't hesitate to contact our Guest Relations team.

Sincerely,
Sam Tse
Guest Relations Manager
The Kimberley Hotel Hong Kong
+852 1234 5678 | [email protected]"""
    
    return response

# ===== STREAMLIT UI =====
def main():
    st.set_page_config(
        page_title="Kimberley Review Assistant",
        page_icon="🏨",
        layout="centered"
    )
    
    st.markdown("""
    <style>
        .header { color: #003366; font-size: 28px; font-weight: bold; margin-bottom: 10px; }
        .subheader { color: #666666; font-size: 16px; margin-bottom: 30px; }
        .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)
    
    st.markdown('<div class="header">The Kimberley Hotel Hong Kong</div>', unsafe_allow_html=True)
    st.markdown('<div class="subheader">Guest Review Analysis System</div>', unsafe_allow_html=True)
    
    review = st.text_area("**Paste Guest Review:**", 
                         height=200,
                         max_chars=MAX_CHARS,
                         placeholder=f"Enter review (max {MAX_CHARS} characters)...",
                         key="review_input")
    
    char_count = len(st.session_state.review_input) if 'review_input' in st.session_state else 0
    st.markdown(f'<div class="char-counter{" warning" if char_count > MAX_CHARS else ""}">{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
        
        if char_count > MAX_CHARS:
            st.warning(f"Review truncated to {MAX_CHARS} characters")
            review = review[:MAX_CHARS]
        
        with st.spinner("Analyzing feedback..."):
            try:
                # Auto-detect language
                review_lang = detect_language(review)
                st.info(f"Detected language: {SUPPORTED_LANGUAGES.get(review_lang, 'English')}")
                
                # Translate if not English
                if review_lang != 'en':
                    translation = translate_text(review, review_lang, 'en')
                    if 'error' in translation:
                        st.error(f"Translation error: {translation['error']}")
                        return
                    analysis_text = translation['translation']
                    
                    with st.expander("View Translation"):
                        st.write("**Original Review:**")
                        st.write(review)
                        st.write("**English Translation:**")
                        st.write(translation['translation'])
                else:
                    analysis_text = review
                
                # Analyze text
                sentiment_model, tokenizer = load_sentiment_model()
                aspect_classifier = load_aspect_classifier()
                
                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
                
                # Translate response back if needed
                if review_lang != 'en':
                    translation_back = translate_text(response, 'en', review_lang)
                    final_response = translation_back['translation'] if 'error' not in translation_back else response
                else:
                    final_response = response
                
                # Display results
                st.divider()
                
                col1, col2 = st.columns(2)
                with col1:
                    st.markdown("### Sentiment Analysis")
                    st.markdown(f"{'✅' if sentiment['label'] == 1 else '⚠️'} **{sentiment['sentiment']}**")
                    st.caption(f"Confidence: {sentiment['confidence']}")
                
                with col2:
                    st.markdown("### Key Aspects")
                    if aspects:
                        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("_No specific aspects detected_")
                
                st.divider()
                st.markdown("### Draft Response")
                st.markdown(f'<div class="result-box">{final_response}</div>', unsafe_allow_html=True)
                
            except Exception as e:
                st.error(f"An error occurred: {str(e)}")

if __name__ == "__main__":
    main()