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")  
    

text2tweet = pipeline("text2text-generation", model="google/t5-v1_1-base")    
 
# text2audio
def text2audio(toxic_result):
    pipe = pipeline("text-to-audio", model="Matthijs/mms-tts-eng")
    audio_data = pipe(toxic_result)
    return audio_data



st.set_page_config(page_title="Generate Your Tweet and Toxicity Analysis")

st.header("Please input your first word of a Tweet :performing_arts:")
input = st.text_input("Please input your first word...")

if input is not None:
    #Stage 1: Input to Tweet
    st.text('Generating a Tweet...')
    tweet = text2tweet(input)
    st.write(tweet)
    
    #Stage 2: Tweet Toxicity Analysis
    


    #Stage 3: Story to Audio data
    st.text('Generating audio data...')
    audio_data =text2audio(tweet)

    # Play button
    if st.button("Play Audio"):
        st.audio(audio_data['audio'],
                    format="audio/wav",
                    start_time=0,
                    sample_rate = audio_data['sampling_rate'])