import streamlit as st from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer import torch import numpy as np def analyze_email(email_body): spam_pipeline = pipeline("text-classification", model="cybersectony/phishing-email-detection-distilbert_v2.4.1") sentiment_model = AutoModelForSequenceClassification.from_pretrained("ISOM5240GP4/email_sentiment", num_labels=2) tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") spam_result = spam_pipeline(email_body) spam_label = spam_result[0]["label"] spam_confidence = spam_result[0]["score"] if spam_label == "LABEL_1": return "spam", f"This is a spam email (Confidence: {spam_confidence:.2f}). No follow-up needed." else: inputs = tokenizer(email_body, padding=True, truncation=True, return_tensors='pt') outputs = sentiment_model(**inputs) predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) predictions = predictions.cpu().detach().numpy() sentiment_index = np.argmax(predictions) sentiment_confidence = predictions[0][sentiment_index] sentiment = "Positive" if sentiment_index == 1 else "Negative" if sentiment == "Positive": return "positive", (f"This email is not spam (Confidence: {spam_confidence:.2f}).\n" f"Sentiment: {sentiment} (Confidence: {sentiment_confidence:.2f}). No follow-up needed.") else: return "negative", (f"This email is not spam (Confidence: {spam_confidence:.2f}).\n" f"Sentiment: {sentiment} (Confidence: {sentiment_confidence:.2f}).\n" "**Need to Follow-Up**: This email is not spam and has negative sentiment.") def main(): st.title("EmailSentry") st.write("Aims to perform analysis on incoming emails and to determine whether there is urgency or higher priority for the company to follow-up.") # Initialize session state if "email_body" not in st.session_state: st.session_state.email_body = "" if "result" not in st.session_state: st.session_state.result = "" if "result_type" not in st.session_state: st.session_state.result_type = "" # Collapsible instructions with st.expander("How to Use", expanded=False): st.write(""" - Type or paste an email into the text box. - Alternatively, click one of the sample buttons to load a predefined email. - Press 'Analyze Email' to check if it’s spam and analyze its sentiment. - Use 'Clear' to reset the input and result. """) # Text area for email input email_body = st.text_area("Email Body", value=st.session_state.email_body, height=200, key="email_input") # Sample emails (shortened snippets for button labels) sample_spam = """ Subject: Urgent: Verify Your Account Now! Dear Customer, We have detected unusual activity on your account. To prevent suspension, please verify your login details immediately by clicking the link below: [Click Here to Verify](http://totally-legit-site.com/verify) Failure to verify within 24 hours will result in your account being locked. This is for your security. Best regards, The Security Team """ spam_snippet = "Subject: Urgent: Verify Your Account Now! Dear Customer, We have detected unusual activity..." sample_not_spam_positive = """ Subject: Great Experience with HKTV Mall! Dear HKTV Mall Team, I just received my order #HKTV-123456, and I’m really impressed with the fast delivery and quality of the products! Thanks for making my shopping experience so smooth. Keep up the great work! Best regards, Emily """ positive_snippet = "Subject: Great Experience with HKTV Mall! Dear HKTV Mall Team, I just received my order..." sample_not_spam_negative = """ Subject: Issue with Recent Delivery Dear Support, I received my package today, but it was damaged, and two items were missing. This is really frustrating—please let me know how we can resolve this as soon as possible. Thanks, Sarah """ negative_snippet = "Subject: Issue with Recent Delivery Dear Support, I received my package today, but..." # Custom CSS for buttons and result boxes st.markdown(""" """, unsafe_allow_html=True) # Sample buttons (in columns) col1, col2, col3 = st.columns(3) with col1: if st.button(spam_snippet, key="spam_sample"): st.session_state.email_body = sample_spam st.session_state.result = "" st.session_state.result_type = "" st.rerun() with col2: if st.button(positive_snippet, key="positive_sample"): st.session_state.email_body = sample_not_spam_positive st.session_state.result = "" st.session_state.result_type = "" st.rerun() with col3: if st.button(negative_snippet, key="negative_sample"): st.session_state.email_body = sample_not_spam_negative st.session_state.result = "" st.session_state.result_type = "" st.rerun() # Analyze and Clear buttons (in a row) col_analyze, col_clear = st.columns([1, 1]) with col_analyze: if st.button("Analyze Email", key="analyze", type="primary"): if email_body: with st.spinner("Analyzing email..."): result_type, result = analyze_email(email_body) st.session_state.result = result st.session_state.result_type = result_type else: st.session_state.result = "Please enter an email body or select a sample to analyze." st