app.py

import streamlit as st
import torch
from transformers import BertForSequenceClassification, BertTokenizerFast
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import time
import pandas as pd
import base64
from PIL import Image
import io

# Set page configuration
st.set_page_config(
    page_title="SMS Spam Guard",
    page_icon="🛡️",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Generate SafeTalk logo as base64 (blue shield with "ST" inside)
def create_logo():
    from PIL import Image, ImageDraw, ImageFont
    import io
    import base64
    
    # Create a new image with a transparent background
    img = Image.new('RGBA', (200, 200), color=(0, 0, 0, 0))
    draw = ImageDraw.Draw(img)
    
    # Draw a shield shape
    shield_color = (30, 58, 138)  # Dark blue
    
    # Shield outline
    points = [(100, 10), (180, 50), (160, 170), (100, 190), (40, 170), (20, 50)]
    draw.polygon(points, fill=shield_color)
    
    # Try to load a font, or use default
    try:
        font = ImageFont.truetype("arial.ttf", 80)
    except IOError:
        font = ImageFont.load_default()
    
    # Add "ST" text in white
    draw.text((70, 60), "ST", fill=(255, 255, 255), font=font)
    
    # Convert to base64 for embedding
    buffered = io.BytesIO()
    img.save(buffered, format="PNG")
    return base64.b64encode(buffered.getvalue()).decode()

# Custom CSS for styling
st.markdown("""

<style>

    .main-header {

        font-size: 2.5rem !important;

        color: #1E3A8A;

        font-weight: 700;

        margin-bottom: 0.5rem;

    }

    .sub-header {

        font-size: 1.1rem;

        color: #6B7280;

        margin-bottom: 2rem;

    }

    .highlight {

        background-color: #F3F4F6;

        padding: 1.5rem;

        border-radius: 0.5rem;

        margin-bottom: 1rem;

    }

    .result-card {

        background-color: #F0F9FF;

        padding: 1.5rem;

        border-radius: 0.5rem;

        border-left: 5px solid #3B82F6;

        margin-bottom: 1rem;

    }

    .spam-alert {

        background-color: #FEF2F2;

        border-left: 5px solid #EF4444;

    }

    .ham-alert {

        background-color: #ECFDF5;

        border-left: 5px solid #10B981;

    }

    .footer {

        text-align: center;

        margin-top: 3rem;

        font-size: 0.8rem;

        color: #9CA3AF;

    }

    .metrics-container {

        display: flex;

        justify-content: space-between;

        margin-top: 1rem;

    }

    .metric-item {

        text-align: center;

        padding: 1rem;

        background-color: #F9FAFB;

        border-radius: 0.5rem;

        box-shadow: 0 1px 3px rgba(0,0,0,0.1);

    }

    .language-tag {

        display: inline-block;

        padding: 0.25rem 0.5rem;

        background-color: #E0E7FF;

        color: #4F46E5;

        border-radius: 9999px;

        font-size: 0.8rem;

        font-weight: 500;

        margin-right: 0.5rem;

    }

</style>

""", unsafe_allow_html=True)

@st.cache_resource
def load_language_model():
    """Load the language detection model"""
    model_name = "papluca/xlm-roberta-base-language-detection"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    return tokenizer, model

@st.cache_resource
def load_spam_model():
    """Load the fine-tuned BERT spam detection model"""
    model_path = "chjivan/final"
    tokenizer = BertTokenizerFast.from_pretrained(model_path)
    model = BertForSequenceClassification.from_pretrained(model_path)
    return tokenizer, model

def detect_language(text, tokenizer, model):
    """Detect the language of the input text"""
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
    
    # Get predictions and convert to probabilities
    logits = outputs.logits
    probabilities = torch.softmax(logits, dim=1)[0]
    
    # Get the predicted language and its probability
    predicted_class_id = torch.argmax(probabilities).item()
    predicted_language = model.config.id2label[predicted_class_id]
    confidence = probabilities[predicted_class_id].item()
    
    # Get top 3 languages with their probabilities
    top_3_indices = torch.topk(probabilities, 3).indices.tolist()
    top_3_probs = torch.topk(probabilities, 3).values.tolist()
    top_3_langs = [(model.config.id2label[idx], prob) for idx, prob in zip(top_3_indices, top_3_probs)]
    
    return predicted_language, confidence, top_3_langs

def classify_spam(text, tokenizer, model):
    """Classify the input text as spam or ham"""
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
    with torch.no_grad():
        outputs = model(**inputs)
    
    # Get predictions and convert to probabilities
    logits = outputs.logits
    probabilities = torch.softmax(logits, dim=1)[0]
    
    # Get the predicted class and its probability (0: ham, 1: spam)
    predicted_class_id = torch.argmax(probabilities).item()
    confidence = probabilities[predicted_class_id].item()
    
    is_spam = predicted_class_id == 1
    return is_spam, confidence

# Generate and cache logo
logo_base64 = create_logo()
logo_html = f'<img src="data:image/png;base64,{logo_base64}" style="height:150px;">'

# Load both models
with st.spinner("Loading models... This may take a moment."):
    lang_tokenizer, lang_model = load_language_model()
    spam_tokenizer, spam_model = load_spam_model()

# App Header with logo
col1, col2 = st.columns([1, 5])
with col1:
    st.markdown(logo_html, unsafe_allow_html=True)
with col2:
    st.markdown('<h1 class="main-header">SMS Spam Guard</h1>', unsafe_allow_html=True)
    st.markdown('<p class="sub-header">智能短信垃圾过滤助手 by SafeTalk Communications Ltd.</p>', unsafe_allow_html=True)

# Sidebar
with st.sidebar:
    st.markdown(logo_html, unsafe_allow_html=True)
    st.markdown("### About SafeTalk")
    st.markdown("SafeTalk Communications Ltd. provides intelligent communication security solutions to protect users from spam and fraudulent messages.")
    st.markdown("#### Our Technology")
    st.markdown("- ✅ Advanced AI-powered spam detection")
    st.markdown("- 🌐 Multi-language support")
    st.markdown("- 🔒 Secure and private processing")
    st.markdown("- ⚡ Real-time analysis")
    
    st.markdown("---")
    st.markdown("### Sample Messages")
    
    if st.button("Sample Spam (English)"):
        st.session_state.sms_input = "URGENT: You have won a $1,000 Walmart gift card. Go to http://bit.ly/claim-prize to claim now before it expires!"
    
    if st.button("Sample Legitimate (English)"):
        st.session_state.sms_input = "Your Amazon package will be delivered today. Thanks for ordering from Amazon!"
    
    if st.button("Sample Message (French)"):
        st.session_state.sms_input = "Bonjour! Votre réservation pour le restaurant est confirmée pour ce soir à 20h. À bientôt!"
    
    if st.button("Sample Message (Spanish)"):
        st.session_state.sms_input = "Hola, tu cita médica está programada para mañana a las 10:00. Por favor llega 15 minutos antes."

# Main Content
st.markdown('<div class="highlight">', unsafe_allow_html=True)
# Input form
sms_input = st.text_area(
    "Enter the SMS message to analyze:",
    value=st.session_state.get("sms_input", ""),
    height=100,
    key="sms_input",
    help="Enter the SMS message you want to analyze for spam"
)

analyze_button = st.button("📱 Analyze Message", use_container_width=True)
st.markdown('</div>', unsafe_allow_html=True)

# Process input and display results
if analyze_button and sms_input:
    with st.spinner("Analyzing message..."):
        # Step 1: Language Detection
        lang_start_time = time.time()
        lang_code, lang_confidence, top_langs = detect_language(sms_input, lang_tokenizer, lang_model)
        lang_time = time.time() - lang_start_time
        
        # Create mapping for full language names
        lang_names = {
            "ar": "Arabic",
            "bg": "Bulgarian",
            "de": "German",
            "el": "Greek",
            "en": "English",
            "es": "Spanish",
            "fr": "French",
            "hi": "Hindi",
            "it": "Italian",
            "ja": "Japanese",
            "nl": "Dutch",
            "pl": "Polish",
            "pt": "Portuguese",
            "ru": "Russian",
            "sw": "Swahili",
            "th": "Thai",
            "tr": "Turkish",
            "ur": "Urdu",
            "vi": "Vietnamese",
            "zh": "Chinese"
        }
        
        lang_name = lang_names.get(lang_code, lang_code)
        
        # Step 2: Spam Classification
        spam_start_time = time.time()
        is_spam, spam_confidence = classify_spam(sms_input, spam_tokenizer, spam_model)
        spam_time = time.time() - spam_start_time
        
        # Display Language Detection Results
        st.markdown("### Analysis Results")
        
        col1, col2 = st.columns(2)
        
        with col1:
            st.markdown("#### 📊 Language Detection")
            st.markdown(f'<div class="result-card">', unsafe_allow_html=True)
            st.markdown(f'<span class="language-tag">{lang_name}</span> Detected with {lang_confidence:.1%} confidence', unsafe_allow_html=True)
            
            # Display top 3 languages
            st.markdown("##### Top language probabilities:")
            for lang_code, prob in top_langs:
                lang_full = lang_names.get(lang_code, lang_code)
                st.markdown(f"- {lang_full}: {prob:.1%}")
                
            st.markdown(f"⏱️ Processing time: {lang_time:.3f} seconds")
            st.markdown('</div>', unsafe_allow_html=True)
        
        with col2:
            st.markdown("#### 🔍 Spam Detection")
            
            if is_spam:
                st.markdown(f'<div class="result-card spam-alert">', unsafe_allow_html=True)
                st.markdown(f"⚠️ **SPAM DETECTED** with {spam_confidence:.1%} confidence")
                st.markdown("This message appears to be spam and potentially harmful.")
            else:
                st.markdown(f'<div class="result-card ham-alert">', unsafe_allow_html=True)
                st.markdown(f"✅ **LEGITIMATE MESSAGE** with {spam_confidence:.1%} confidence")
                st.markdown("This message appears to be legitimate.")
            
            st.markdown(f"⏱️ Processing time: {spam_time:.3f} seconds")
            st.markdown('</div>', unsafe_allow_html=True)
        
        # Summary and Recommendations
        st.markdown("### 📋 Summary & Recommendations")
        if is_spam:
            st.warning("📵 **Recommended Action**: This message should be blocked or moved to spam folder.")
            st.markdown("""

            **Why this is likely spam:**

            - Contains suspicious language patterns

            - May include urgent calls to action

            - Could contain unsolicited offers

            """)
        else:
            st.success("✅ **Recommended Action**: This message can be delivered to the inbox.")
        
        # Chart for visualization
        st.markdown("### 📈 Confidence Visualization")
        chart_data = pd.DataFrame({
            'Task': ['Language Detection', 'Spam Classification'],
            'Confidence': [lang_confidence, spam_confidence if is_spam else 1-spam_confidence]
        })
        st.bar_chart(chart_data.set_index('Task'))

# Footer
st.markdown('<div class="footer">', unsafe_allow_html=True)
st.markdown("© 2023 SafeTalk Communications Ltd. | www.safetalk.com")
st.markdown("SMS Spam Guard is an intelligent message filtering solution to protect users from unwanted communications.")
st.markdown('</div>', unsafe_allow_html=True)