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Update app.py
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app.py
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@@ -2,19 +2,43 @@ import streamlit as st
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from transformers import pipeline
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def main():
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st.
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result = sentiment_pipeline(user_input)
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sentiment = result[0]["label"]
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confidence = result[0]["score"]
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if __name__ == "__main__":
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main()
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from transformers import pipeline
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def main():
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# Load the models
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spam_pipeline = pipeline("text-classification", model="cybersectony/phishing-email-detection-distilbert_v2.4.1")
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sentiment_pipeline = pipeline("text-classification", model="ISOM5240GP4/email_sentiment")
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# Title and description
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st.title("Email Analysis Tool")
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st.write("Enter an email body below to check if it's spam and analyze its sentiment.")
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# Text area for email input
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email_body = st.text_area("Email Body", height=200)
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# Button to trigger analysis
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if st.button("Analyze Email"):
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if email_body:
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# Step 1: Check if the email is spam
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spam_result = spam_pipeline(email_body)
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spam_label = spam_result[0]["label"]
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spam_confidence = spam_result[0]["score"]
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# If it's spam, display result and stop
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if spam_label == "POSITIVE": # Assuming "POSITIVE" means spam/phishing (check model docs)
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st.write(f"This is a spam email (Confidence: {spam_confidence:.2f}). No follow-up needed.")
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else:
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# Step 2: If not spam, analyze sentiment
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sentiment_result = sentiment_pipeline(email_body)
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sentiment_label = sentiment_result[0]["label"]
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sentiment_confidence = sentiment_result[0]["score"]
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if sentiment_label == "POSITIVE":
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st.write(f"This email is not spam (Confidence: {spam_confidence:.2f}).")
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st.write(f"Sentiment: Positive (Confidence: {sentiment_confidence:.2f}). No follow-up needed.")
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else: # Assuming "NEGATIVE" for negative sentiment
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st.write(f"This email is not spam (Confidence: {spam_confidence:.2f}).")
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st.write(f"Sentiment: Negative (Confidence: {sentiment_confidence:.2f}).")
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st.write("**This email needs follow-up as it is not spam and has negative sentiment.**")
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else:
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st.write("Please enter an email body to analyze.")
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if __name__ == "__main__":
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main()
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