app.py

import streamlit as st

# Use a pipeline as a high-level helper
from transformers import pipeline

toxic_model = pipeline("text-classification", model="Matt09Miao/GP5_tweet_toxic")  
    
def text2audio(text):
    pipe = pipeline("text-to-audio", model="Matthijs/mms-tts-eng")
    audio_data = pipe(text)
    return audio_data

st.set_page_config(page_title="Tweet Toxicity Analysis")

st.header("Please input a Tweet for Toxicity Analysis :performing_arts:")
input = st.text_area("Enter a Tweer for analysis")
result = toxic_model(input)

  # Display the result
st.write("Tweet:", input)
st.write("label:", result[0]['label'])
st.write("score:", result[0]['score'])

text2audio("Tweet:", input)
st.audio(audio_data['audio'],
                     format="audio/wav",
                     start_time=0,
                     sample_rate = audio_data['sampling_rate'])

text2audio("label:", result[0]['label'])
st.audio(audio_data['audio'],
                     format="audio/wav",
                     start_time=0,
                     sample_rate = audio_data['sampling_rate'])

text2audio("score:", result[0]['score'])
st.audio(audio_data['audio'],
                     format="audio/wav",
                     start_time=0,
                     sample_rate = audio_data['sampling_rate'])