Initial commit: Email classifier project

.gitignore

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+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Environment variables
+.env
+
+# Virtual environments
+.venv

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+/httpRequests/
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.idea/Smart-Email-Classification-App.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="/opt/anaconda3" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (Smart-Email-Classification-App)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/Smart-Email-Classification-App.iml" filepath="$PROJECT_DIR$/.idea/Smart-Email-Classification-App.iml" />
+    </modules>
+  </component>
+</project>

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+<?xml version="1.0" encoding="UTF-8"?>
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.python-version

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+3.10

app.py

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+import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from dotenv import load_dotenv
+import os
+from huggingface_hub import login
+import numpy as np
+
+# 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="Email Classifier using NLP",
+    page_icon="📧",
+    layout="wide",
+    initial_sidebar_state="collapsed"
+)
+
+st.markdown("""
+    <style>
+        body {
+            background: linear-gradient(to right, #eef2f3, #8e9eab);
+        }
+        .main-title {
+            text-align: center;
+            color: #222;
+            font-size: 2.3rem;
+            font-weight: 700;
+        }
+        .sub-title {
+            text-align: center;
+            color: #444;
+            font-size: 1.1rem;
+        }
+        .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)
+
+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
+    )
+
+# 5. Prediction Logic
+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()
+
+# 6. Predict Button
+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("<div class='prediction-card'>", unsafe_allow_html=True)
+        st.markdown(f"### 🧠 Predicted Category: **{predicted_label}**")
+        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.bar_chart(prob_dict)
+        st.markdown("</div>", unsafe_allow_html=True)
+
+# 7. 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)
+    </p>
+
+""", unsafe_allow_html=True)

pyproject.toml

@@ -0,0 +1,13 @@
+[project]
+name = "smart-email-classification-app"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "huggingface-hub>=0.35.3",
+    "python-dotenv>=1.1.1",
+    "streamlit>=1.50.0",
+    "torch>=2.9.0",
+    "transformers>=4.57.1",
+]

requirements.txt

@@ -0,0 +1,141 @@
+# This file was autogenerated by uv via the following command:
+#    uv pip compile pyproject.toml -o requirements.txt
+altair==5.5.0
+    # via streamlit
+attrs==25.4.0
+    # via
+    #   jsonschema
+    #   referencing
+blinker==1.9.0
+    # via streamlit
+cachetools==6.2.1
+    # via streamlit
+certifi==2025.10.5
+    # via requests
+charset-normalizer==3.4.4
+    # via requests
+click==8.3.0
+    # via streamlit
+filelock==3.20.0
+    # via
+    #   huggingface-hub
+    #   torch
+    #   transformers
+fsspec==2025.9.0
+    # via
+    #   huggingface-hub
+    #   torch
+gitdb==4.0.12
+    # via gitpython
+gitpython==3.1.45
+    # via streamlit
+hf-xet==1.1.10
+    # via huggingface-hub
+huggingface-hub==0.35.3
+    # via
+    #   smart-email-classification-app (pyproject.toml)
+    #   tokenizers
+    #   transformers
+idna==3.11
+    # via requests
+jinja2==3.1.6
+    # via
+    #   altair
+    #   pydeck
+    #   torch
+jsonschema==4.25.1
+    # via altair
+jsonschema-specifications==2025.9.1
+    # via jsonschema
+markupsafe==3.0.3
+    # via jinja2
+mpmath==1.3.0
+    # via sympy
+narwhals==2.8.0
+    # via altair
+networkx==3.4.2
+    # via torch
+numpy==2.2.6
+    # via
+    #   pandas
+    #   pydeck
+    #   streamlit
+    #   transformers
+packaging==25.0
+    # via
+    #   altair
+    #   huggingface-hub
+    #   streamlit
+    #   transformers
+pandas==2.3.3
+    # via streamlit
+pillow==11.3.0
+    # via streamlit
+protobuf==6.33.0
+    # via streamlit
+pyarrow==21.0.0
+    # via streamlit
+pydeck==0.9.1
+    # via streamlit
+python-dateutil==2.9.0.post0
+    # via pandas
+python-dotenv==1.1.1
+    # via smart-email-classification-app (pyproject.toml)
+pytz==2025.2
+    # via pandas
+pyyaml==6.0.3
+    # via
+    #   huggingface-hub
+    #   transformers
+referencing==0.37.0
+    # via
+    #   jsonschema
+    #   jsonschema-specifications
+regex==2025.9.18
+    # via transformers
+requests==2.32.5
+    # via
+    #   huggingface-hub
+    #   streamlit
+    #   transformers
+rpds-py==0.27.1
+    # via
+    #   jsonschema
+    #   referencing
+safetensors==0.6.2
+    # via transformers
+six==1.17.0
+    # via python-dateutil
+smmap==5.0.2
+    # via gitdb
+streamlit==1.50.0
+    # via smart-email-classification-app (pyproject.toml)
+sympy==1.14.0
+    # via torch
+tenacity==9.1.2
+    # via streamlit
+tokenizers==0.22.1
+    # via transformers
+toml==0.10.2
+    # via streamlit
+torch==2.9.0
+    # via smart-email-classification-app (pyproject.toml)
+tornado==6.5.2
+    # via streamlit
+tqdm==4.67.1
+    # via
+    #   huggingface-hub
+    #   transformers
+transformers==4.57.1
+    # via smart-email-classification-app (pyproject.toml)
+typing-extensions==4.15.0
+    # via
+    #   altair
+    #   huggingface-hub
+    #   referencing
+    #   streamlit
+    #   torch
+tzdata==2025.2
+    # via pandas
+urllib3==2.5.0
+    # via requests