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import streamlit as st
from transformers import pipeline
def load_summarizer():
whisper = pipeline('automatic-speech-recognition' , model ='openai/whisper-medium') #audio-to-text
summarize = pipeline("summarization", device=0)
senti = pipeline("sentiment-analysis",device=0)
nameentity = pipeline("ner",device=0)
translate = pipeline("translation", device=0)
return whisper, summarize, senti, nameentity, translate
#pipe = pipeline("sentiment-analysis")
text = st.text_area('Enter some Text!')
summarizer = load_summarizer()
st.title("Summarize Text")
sentence = st.text_area('Please paste your article :', height=30)
button = st.button("Click")
st.subheader("Choose a mp3 file that you extracted from the work site")
uploaded_file = st.file_uploader("Select file from your directory")
if uploaded_file is not None:
audio_bytes = uploaded_file.read()
st.audio(audio_bytes, format='audio/mp3')
if text:
out=pipe(text)
st.json(out)
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