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| import streamlit as st | |
| from transformers import pipeline | |
| def main(): | |
| # Load the models | |
| spam_pipeline = pipeline("text-classification", model="cybersectony/phishing-email-detection-distilbert_v2.4.1") | |
| sentiment_pipeline = pipeline("text-classification", model="ISOM5240GP4/email_sentiment") | |
| # Title and description | |
| st.title("Email Analysis Tool") | |
| st.write("Enter an email body below to check if it's spam and analyze its sentiment.") | |
| # Text area for email input | |
| email_body = st.text_area("Email Body", height=200) | |
| # Button to trigger analysis | |
| if st.button("Analyze Email"): | |
| if email_body: | |
| # Step 1: Check if the email is spam | |
| spam_result = spam_pipeline(email_body) | |
| spam_label = spam_result[0]["label"] | |
| spam_confidence = spam_result[0]["score"] | |
| # If it's spam, display result and stop | |
| if spam_label == "POSITIVE": # Assuming "POSITIVE" means spam/phishing (check model docs) | |
| st.write(f"This is a spam email (Confidence: {spam_confidence:.2f}). No follow-up needed.") | |
| else: | |
| # Step 2: If not spam, analyze sentiment | |
| sentiment_result = sentiment_pipeline(email_body) | |
| sentiment_label = sentiment_result[0]["label"] | |
| sentiment_confidence = sentiment_result[0]["score"] | |
| if sentiment_label == "POSITIVE": | |
| st.write(f"This email is not spam (Confidence: {spam_confidence:.2f}).") | |
| st.write(f"Sentiment: Positive (Confidence: {sentiment_confidence:.2f}). No follow-up needed.") | |
| else: # Assuming "NEGATIVE" for negative sentiment | |
| st.write(f"This email is not spam (Confidence: {spam_confidence:.2f}).") | |
| st.write(f"Sentiment: Negative (Confidence: {sentiment_confidence:.2f}).") | |
| st.write("**This email needs follow-up as it is not spam and has negative sentiment.**") | |
| else: | |
| st.write("Please enter an email body to analyze.") | |
| if __name__ == "__main__": | |
| main() |