Update app.py

app.py

@@ -3,58 +3,24 @@ import torch
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 from dotenv import load_dotenv
 import os
-from huggingface_hub import login
+import random
 import numpy as np
+import pandas as pd
+from huggingface_hub import login
 
+# ----------------------------
+# 1. Streamlit App Configuration
+# ----------------------------
 st.set_page_config(
-    page_title="Smart Email Classification App",
+    page_title="Email Classifier using NLP",
     page_icon="📧",
     layout="wide",
-    initial_sidebar_state="collapsed"
+    initial_sidebar_state="expanded"
 )
 
-st.title("Smart Email Classification App")
-
-# 1. Load Environment Variables
-load_dotenv()
-HF_TOKEN = os.getenv("HF_TOKEN")
-MODEL_REPO = os.getenv("MODEL_REPO", "kaisarhossain/email_classifier_model")  # default fallback
-
-# Login to Hugging Face (optional if model is public)
-if HF_TOKEN:
-    login(token=HF_TOKEN)
-
-# 2. Load Model & Tokenizer
-@st.cache_resource(show_spinner=True)
-def load_model():
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO)
-    model = AutoModelForSequenceClassification.from_pretrained(MODEL_REPO)
-    return tokenizer, model
-
-tokenizer, model = load_model()
-
-# Define the label schema
-LABELS = [
-    "Promotions",
-    "Spam",
-    "Social Media Updates",
-    "Forum Updates",
-    "Code Verification",
-    "Work Updates"
-]
-
-# 3. Streamlit App Configuration
-
-
-# st.set_page_config(
-#     page_title="Smart Email Classification App",
-#     page_icon="📧",
-#     layout="wide",
-#     initial_sidebar_state="collapsed"
-# )
-
-# st.title("Smart Email Classification App")
-
+# ----------------------------
+# 2. Custom Styling
+# ----------------------------
 st.markdown("""
     <style>
         body {
@@ -70,30 +36,136 @@ st.markdown("""
             text-align: center;
             color: #444;
             font-size: 1.1rem;
+            margin-bottom: 40px;
+        }
+        .email-list {
+            background-color: white;
+            border-radius: 10px;
+            padding: 10px;
+            height: 500px;
+            overflow-y: auto;
+            box-shadow: 0 4px 20px rgba(0,0,0,0.1);
+        }
+        .email-item {
+            padding: 10px;
+            border-bottom: 1px solid #eee;
+            cursor: pointer;
+        }
+        .email-item:hover {
+            background-color: #f5f5f5;
+        }
+        .email-content {
+            background-color: white;
+            border-radius: 10px;
+            padding: 20px;
+            box-shadow: 0 4px 20px rgba(0,0,0,0.1);
+            height: 500px;
+            overflow-y: auto;
         }
         .prediction-card {
             background-color: white;
             border-radius: 12px;
             padding: 20px;
             box-shadow: 0 4px 20px rgba(0,0,0,0.1);
-            margin-top: 20px;
         }
     </style>
 """, unsafe_allow_html=True)
 
+# ----------------------------
+# 3. App Header
+# ----------------------------
 st.markdown("<h1 class='main-title'>📧 Smart Email Classifier</h1>", unsafe_allow_html=True)
-st.markdown("<p class='sub-title'>The Smart Email Classifier application is capable of classifying emails of different types based on email subject or body using advanced Natural Language Processing (NLP) techniques and using a fine-tuned NLP model.</p>", unsafe_allow_html=True)
-
-# 4. User Input Section
-with st.container():
-    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
-    )
+st.markdown("<p class='sub-title'>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.</p>", 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()
 
-# 5. Prediction Logic
+# ----------------------------
+# 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():
@@ -103,7 +175,55 @@ def classify_email(text):
     confidence = probs[0][predicted_idx].item()
     return LABELS[predicted_idx], confidence, probs[0].numpy()
 
-# 6. Predict Button
+# ----------------------------
+# 9. Gmail-like Layout
+# ----------------------------
+st.markdown("## 📥 Inbox")
+st.markdown("---")
+col1, col2, col3 = st.columns([2, 3, 2])
+
+with col1:
+    st.markdown("#### 📩 Inbox")
+    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=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.")
@@ -116,18 +236,18 @@ if st.button("🔍 Classify Email"):
         st.markdown(f"**Confidence:** {confidence * 100:.2f}%")
         st.progress(confidence)
 
-        # Display class probabilities in a bar chart
-        st.markdown("#### 📊 Category Probabilities:")
         prob_dict = {LABELS[i]: float(all_probs[i]) for i in range(len(LABELS))}
+        st.markdown("#### 📊 Category Probabilities:")
         st.bar_chart(prob_dict)
         st.markdown("</div>", unsafe_allow_html=True)
 
-# 7. Footer
+# ----------------------------
+# 11. Footer
+# ----------------------------
 st.markdown("---")
 st.markdown("""
     <p style='text-align: left; color: gray; font-size: 0.9rem'>
     Built for CSC-546: Natural Language Processing (Smart Email Classification Project) | 
-    Built by: Mohammed Golam Kaisar Hossain Bhuyan (hossainbhuyan@cua.edu)
+    Developed by: Mohammed Golam Kaisar Hossain Bhuyan (hossainbhuyan@cua.edu)
     </p>
-
 """, unsafe_allow_html=True)