Smart Email Classification App is an advanced Natural Language Processing (NLP) and Deep Learning project designed to automate email intent classification. The application is capable of categorizing emails into six widely-used categories: Promotions, Spam, Social Media Updates, Forum Updates, Code Verification, and Work Updates.
", unsafe_allow_html=True) # ---------------------------- # 4. Sidebar: Model Selection # ---------------------------- st.sidebar.header("⚙️ Model Configuration") # Define model options model_options = { "DistilBERT (Fine-tuned) 1": "kaisarhossain/email-classifier-distilbert-finetuned-kaisar", "DistilBERT (Fine-tuned) 2": "kaisarhossain/email_classifier_model" } model_choice = st.sidebar.selectbox("Select Model", list(model_options.keys())) MODEL_REPO = model_options[model_choice] st.sidebar.info(f"Using model: {MODEL_REPO}") # ---------------------------- # 5. Environment Variables & Authentication # ---------------------------- load_dotenv() HF_TOKEN = os.getenv("HF_TOKEN") if HF_TOKEN: try: login(token=HF_TOKEN) except Exception as e: st.sidebar.warning("⚠️ Unable to authenticate with Hugging Face token.") st.sidebar.write(e) # ---------------------------- # 6. Load Model Dynamically # ---------------------------- @st.cache_resource(show_spinner=True) def load_model(model_repo): tokenizer = AutoTokenizer.from_pretrained(model_repo) model = AutoModelForSequenceClassification.from_pretrained(model_repo) return tokenizer, model try: tokenizer, model = load_model(MODEL_REPO) except Exception as e: st.error(f"❌ Failed to load model from {MODEL_REPO}") st.exception(e) st.stop() # ---------------------------- # 7. Labels and Dummy Inbox # ---------------------------- LABELS = [ "Promotions", "Spam", "Social Media Updates", "Forum Updates", "Code Verification", "Work Updates" ] dummy_subjects = { "Promotions": ["50% OFF Today Only!", "Your Exclusive Coupon Awaits", "Flash Sale on Electronics"], "Spam": ["Claim your free reward!", "Win an iPhone 15 now!", "You’ve been selected!"], "Social Media Updates": ["New friend request on Facebook", "Someone mentioned you on Twitter", "New followers on Instagram"], "Forum Updates": ["Your Stack Overflow answer received upvotes", "New discussion thread in Data Science Forum", "Python 3.12 update discussion"], "Code Verification": ["Your verification code is 482915", "Confirm login attempt", "Verify your new device"], "Work Updates": ["Meeting rescheduled for 3 PM", "Project deadline extended", "Client feedback received"] } dummy_bodies = { "Promotions": "Get up to 70% off on your favorite brands. Offer valid for a limited time only!", "Spam": "Click this link to win cash prizes. Limited slots available!", "Social Media Updates": "You have new notifications and updates from your social media network.", "Forum Updates": "A new reply has been posted to a thread you are following.", "Code Verification": "Enter this code in the app to verify your login session.", "Work Updates": "Please find attached the meeting notes and next steps for the team." } # Generate dummy Gmail inbox random.seed(42) inbox_data = [] for _ in range(100): label = random.choice(LABELS) inbox_data.append({ "Category": label, "Subject": random.choice(dummy_subjects[label]), "Body": dummy_bodies[label] }) inbox_df = pd.DataFrame(inbox_data) # ---------------------------- # 8. Classification Function # ---------------------------- def classify_email(text): inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=256) with torch.no_grad(): outputs = model(**inputs) probs = torch.nn.functional.softmax(outputs.logits, dim=1) predicted_idx = torch.argmax(probs, dim=1).item() confidence = probs[0][predicted_idx].item() return LABELS[predicted_idx], confidence, probs[0].numpy() # ---------------------------- # 9. Gmail-like Layout # ---------------------------- st.markdown("## 📥 Inbox") st.markdown("---") col1, col2, col3 = st.columns([2, 3, 2]) with col1: st.markdown("#### 📩 Inbox") if not inbox_df.empty: # Ensure first email is selected by default selected_email = st.radio( "Choose an email to view:", range(len(inbox_df)), format_func=lambda i: inbox_df.iloc[i]["Subject"], label_visibility="collapsed", index=0 # default to first email ) else: st.info("No emails available in the inbox.") selected_email = None with col2: st.markdown("#### ✉️ Email Details") if selected_email is not None: selected_row = inbox_df.iloc[selected_email] st.markdown(f"**Subject:** {selected_row['Subject']}") st.markdown(f"**Body:** {selected_row['Body']}") else: st.info("📩 Select an email from the inbox to view details.") with col3: st.markdown("#### 📊 Classification Result") if selected_email is not None: text = inbox_df.iloc[selected_email]["Subject"] + " " + inbox_df.iloc[selected_email]["Body"] predicted_label, confidence, all_probs = classify_email(text) st.markdown(f"**Predicted Category:** {predicted_label}") st.markdown(f"**Confidence:** {confidence * 100:.2f}%") prob_dict = {LABELS[i]: float(all_probs[i]) for i in range(len(LABELS))} st.bar_chart(prob_dict) else: st.warning("Select an email to see classification results.") # ---------------------------- # 10. Manual Custom Email Input # ---------------------------- st.markdown("---") st.subheader("✉️ Enter email text (subject/body) for classification:") email_text = st.text_area( "Enter Email Text Below:", placeholder="Example: Your code for verification is 123456 or Meeting scheduled for 3 PM today.", height=150 ) if st.button("🔍 Classify Email"): if not email_text.strip(): st.warning("⚠️ Please enter email text before classifying.") else: with st.spinner("Classifying..."): predicted_label, confidence, all_probs = classify_email(email_text) st.markdown("Built for CSC-546: Natural Language Processing (Smart Email Classification Project) | Developed by: Mohammed Golam Kaisar Hossain Bhuyan (hossainbhuyan@cua.edu)
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