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app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Load the text classification model pipeline
+classifier = pipeline("text-classification", model='kithangw/phishing_email_detection')
+
+# Streamlit application title
+st.title("Please enter a suspicious email")
+
+# Text input for user to enter the email to classify
+email = st.text_area("Enter the email to classify", "")
+
+# Perform text classification when the user clicks the "Classify" button
+if st.button("Classify"):
+    if email:  # Check if email is not empty
+        # Perform text classification on the input email
+        results = classifier(email)
+        
+        # The results variable contains a list with one item, which is a dictionary.
+        # The dictionary has 'label' and 'score' as keys.
+        result = results[0]
+        label = result['label']
+        score = round(result['score'] * 100, 2)  # Convert score to percentage
+
+        # Check the label and print out the corresponding message
+        if label == "LABEL_1":  # Assuming LABEL_1 indicates phishing
+            st.write(f"The email you entered is {score}% likely to be a phishing email.")
+        else:  # Assuming LABEL_0 indicates not phishing
+            st.write(f"The email you entered is {score}% likely to be not a phishing email.")
+    else:
+        st.error("Please enter an email to classify.")

requirements.txt

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+spaces
+transformers
+torch
+evaluate
+transformers[torch]