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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")
# text2story
def text2story(text):
pipe = pipeline("text-generation", model="distilbert/distilgpt2")
tweet_text = pipe(text)[0]['generated_text']
return tweet_text
# 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("In put your first word...")
if input is not None:
#Stage 1: Input to Tweet
st.text('Generating a Tweet...')
tweet = text2story(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']) |