app.py

import streamlit as st
from transformers import pipeline
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("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("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
    spam = "LABEL_1"
    ham = "LABEL_0"
    for result in results:
    if result['label'] == spam:
        #st.write("Text:", text)
        st.write("Label: Spam")