app.py

import streamlit as st
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import torch

# Set page configuration for the app
st.set_page_config(
    page_title="Review Assistant",
    page_icon="📝",
    layout="centered"
)

# Custom CSS styling for the page
st.markdown("""
<style>
    .main-header {
        font-size: 2.2rem;
        color: #1E3A8A;
        text-align: center;
        margin-bottom: 0;
        padding-bottom: 0;
    }
    .sub-header {
        font-size: 1rem;
        color: #6B7280;
        text-align: center;
        margin-top: 0.3rem;
        margin-bottom: 2rem;
    }
    .result-card {
        padding: 1.2rem;
        border-radius: 8px;
        margin-bottom: 1rem;
    }
    .topic-card {
        background-color: #ECFDF5;
        border-left: 4px solid #10B981;
    }
    .sentiment-card {
        background-color: #EFF6FF;
        border-left: 4px solid #3B82F6;
    }
    .reply-card {
        background-color: #F9FAFB;
        border-left: 4px solid #6B7280;
        padding: 1.5rem;
    }
    .result-label {
        font-weight: bold;
        margin-bottom: 0.5rem;
    }
    /* Changed button color from blue (#2563EB) to green (#10B981) */
    .stButton>button {
        background-color: #10B981;
        color: white;
        border: none;
        padding: 0.5rem 2rem;
        border-radius: 6px;
        font-weight: 500;
    }
    /* Changed hover color to darker green (#059669) */
    .stButton>button:hover {
        background-color: #059669;
    }
    .footer {
        text-align: center;
        color: #9CA3AF;
        font-size: 0.8rem;
        margin-top: 3rem;
    }
    .stSpinner {
        text-align: center;
    }
</style>
""", unsafe_allow_html=True)

# Page headers
st.markdown("<h1 class='main-header'>Smart Review Analysis Assistant</h1>", unsafe_allow_html=True)
st.markdown("<p class='sub-header'>Topic Recognition, Sentiment Analysis, and Auto Reply in One Click</p>", unsafe_allow_html=True)

# ------- Load ML Pipelines -------
@st.cache_resource
def load_pipelines():
    """
    Load all required machine learning models for review analysis:
    - Topic classifier using zero-shot classification
    - Sentiment analysis model
    - Customer reply generation model
    
    Returns models and topic labels for use in the application
    """
    # Define topic categories for classification
    topic_labels = [
        "billing", "account access", "customer service", "loans",
        "fraud", "technical issue", "credit card", "mobile app",
        "branch service", "transaction delay", "account closure", "information error"
    ]
    
    # Ensure compatibility with CPU-only environments
    dtype = torch.float32  # Use float32 for better CPU compatibility
    
    # Load topic classification model
    topic_classifier = pipeline(
        "zero-shot-classification",
        model="MoritzLaurer/deberta-v3-base-zeroshot-v1",
    )
    
    # Load sentiment analysis model
    sentiment_classifier = pipeline(
        "sentiment-analysis",
        model="cardiffnlp/twitter-roberta-base-sentiment-latest",
    )
    
    # Load reply generation model
    model_name = "Leo66277/finetuned-tinyllama-customer-replies"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)
    
    def generate_reply(text):
        """Generate a polite customer service reply based on the given review text"""
        prompt_text = f"Please write a short, polite English customer service reply to the following customer comment:\n{text}"
        inputs = tokenizer(prompt_text, return_tensors="pt", truncation=True, max_length=512)
        
        # Generate response with beam search for better quality
        with torch.no_grad():
            gen_ids = model.generate(
                inputs.input_ids,
                max_length=inputs.input_ids.shape[1] + 120,
                pad_token_id=tokenizer.eos_token_id if tokenizer.eos_token_id else tokenizer.pad_token_id,
                num_beams=3,
                no_repeat_ngram_size=2,
                early_stopping=True
            )
            
        # Clean up the generated text
        reply = tokenizer.decode(gen_ids[0][inputs.input_ids.shape[1]:], skip_special_tokens=True).strip()
        reply = reply.strip('"').replace('\n', ' ').replace('  ', ' ')
        return reply
    
    return topic_classifier, sentiment_classifier, generate_reply, topic_labels

# ------- Page Layout --------
st.markdown("### Enter a review for instant analysis")

# Updated example review as requested
example_review = "BOA states on their website that holds are 2-7 days. I made a deposit, and the receipt states funds would be available in 2 days. Now, 13 days later, I am still waiting on my funds, and BOA can't give me a straight answer."

# Text input area for user reviews
user_review = st.text_area(
    "Please enter or paste a review below:",
    value=example_review,
    height=120
)

# Button to trigger analysis
if st.button("Analyze"):
    if not user_review.strip():
        # Validate user input
        st.warning("Please enter a valid review!")
    else:
        # Show user-friendly loading message
        with st.spinner("Analyzing your review..."):
            # Load models on first use to avoid loading message on startup
            if "topic_pipe" not in st.session_state:
                st.session_state.topic_pipe, st.session_state.sentiment_pipe, st.session_state.reply_generator, st.session_state.topic_labels = load_pipelines()
            
            # Run topic classification
            topic_result = st.session_state.topic_pipe(user_review, st.session_state.topic_labels, multi_label=False)
            topic = topic_result['labels'][0]
            # Removed confidence percentage as requested
            
            # Run sentiment analysis
            sentiment_result = st.session_state.sentiment_pipe(user_review)
            sentiment = sentiment_result[0]['label']
            # Removed confidence percentage as requested
            
            # Generate auto reply
            reply_text = st.session_state.reply_generator(user_review)

        # Display results in a visually appealing format
        col1, col2 = st.columns(2)
        with col1:
            st.markdown(f"<div class='result-card topic-card'><p class='result-label'>Topic:</p>{topic}</div>", unsafe_allow_html=True)
        with col2:
            st.markdown(f"<div class='result-card sentiment-card'><p class='result-label'>Sentiment:</p>{sentiment}</div>", unsafe_allow_html=True)
        
        # Display suggested reply
        st.markdown(f"<div class='result-card reply-card'><p class='result-label'>Auto-reply Suggestion:</p>{reply_text}</div>", unsafe_allow_html=True)

# Page footer
st.markdown("<div class='footer'>© 2024 Review AI Assistant</div>", unsafe_allow_html=True)