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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'])
audio_data1 = text2audio(input)
st.audio(audio_data1['audio'],
format="audio/wav",
start_time=0,
sample_rate = audio_data['sampling_rate'])
audio_data2 = text2audio(result[0]['label'])
st.audio(audio_data2['audio'],
format="audio/wav",
start_time=0,
sample_rate = audio_data['sampling_rate'])
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