Update app.py

app.py

@@ -1,23 +1,25 @@
 import streamlit as st
 from transformers import pipeline
-# Load the text classification model pipeline
-classifier = pipeline("text-classification", model='isom5240sp24/bert-base-uncased-emotion', return_all_scores=True)
+import soundfile as sf
+$ pip install txtai[pipeline]
+from txtai.pipeline import TextToSpeech
+
+# Load the text classification model pipeline, filter out the spam and leave the ham
+classifier = pipeline("text-classification", model='JustHuggingFaces/OptimalSpamDetect', return_all_scores=True)
+to_speech = TextToSpeech("NeuML/ljspeech-jets-onnx")
 # Streamlit application title
-st.title("Text Classification")
-st.write("Classification for 6 emotions: sadness, joy, love, anger, fear, surprise")
+st.title("Reading Ham")
+st.write("Classification for Spam Email: spam or ham?")
 # Text input for user to enter the text to classify
-text = st.text_area("Enter the text to classify", "")
+text = st.text_area("Paste the email to classify", "")
 # Perform text classification when the user clicks the "Classify" button
 if st.button("Classify"):
-# Perform text classification on the input text
-results = classifier(text)[0]
-# Display the classification result
-max_score = float('-inf')
-max_label = ''
-for result in results:
-if result['score'] > max_score:
-max_score = result['score']
-max_label = result['label']
-st.write("Text:", text)
-st.write("Label:", max_label)
-st.write("Score:", max_score
+    # Perform text classification on the input text
+    results = classifier(text)[0]
+    # Display the classification result
+    spam = "LABEL_1"
+    ham = "LABEL_0"
+    for result in results:
+    if result['label'] == spam:
+        #st.write("Text:", text)
+        st.write("Label: Spam")