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import yt_dlp
import os
import streamlit as st
import transformers
from transformers import pipeline
from transformers import AutoTokenizer
import nltk
from PIL import Image
#Download youtube video
@st.cache_data
def download_audio(link):
with yt_dlp.YoutubeDL({'extract_audio': True, 'format': 'bestaudio', 'outtmpl': 'video.mp3'}) as video:
video.download(link)
#Load Whisper pipeline via HuggingFace
@st.cache_resource
def load_whisper(seconds):
return pipeline("automatic-speech-recognition",
model="openai/whisper-tiny",
chunk_length_s=seconds,
)
#Load Extractive Summarizer pipeline via HuggingFace
@st.cache_resource
def load_extractive():
return pipeline("summarization",
model = "NotXia/longformer-bio-ext-summ",
tokenizer = AutoTokenizer.from_pretrained("NotXia/longformer-bio-ext-summ"),
trust_remote_code = True,
)
def old_funcs():
'''
def get_transcription_en(mp3_file):
model = whisper.load_model("tiny.en")
directory = os.getcwd()
result = model.transcribe(os.path.join(directory, mp3_file))
return result["text"]
#def portuguese_sum_pipeline(link):
# video_title = download_audio(link)
def english_sum_pipeline(link):
download_audio(link)
transcript_text = get_transcription_en("video.mp3")
#extractive summarization
extractive_model = Summarizer()
extractive = extractive_model(transcript_text, num_sentences=15)
#abstractive summarization
device_num = 0 if torch.cuda.is_available() else -1
abstractive_summarizer = pipeline("summarization", model="facebook/bart-large-cnn", tokenizer="facebook/bart-large-cnn", device=device_num)
output_text = abstractive_summarizer(extractive)[0]["summary_text"]
return transcript_text, output_text
def english_qa_pipeline(questions, context):
nlp = pipeline("question-answering", model='distilbert-base-uncased-distilled-squad')
answers = []
for question in questions:
result = nlp(question=question, context=context)
answers.append(result["answer"])
return answers
'''
def main():
icon = Image.open("Traçado laranja #f1863d.png")
st.set_page_config(
page_title = "Turing Videos",
page_icon = icon,
layout = "wide",
initial_sidebar_state = "auto",
)
with st.sidebar():
st.title(":blue[Turing]Videos")
with st.form("data_collection"):
compression_rate = st.slider("Selecione a taxa de compressão:",
min_value=0.1, max_value=0.9,
value=0.2, step=0.05
)
# Every form must have a submit button.
submitted = st.form_submit_button("Submit")
if submitted:
st.success('Dados coletados!', icon="✅")
else:
st.error('Dados ainda não coletados!', icon="🚨")
main()
def old_main():
'''
#Collect inputs and create the interface
def main():
header = st.container()
model = st.container()
model_1, model_2 = st.columns(2)
qa = st.container()
qa_1, qa_2 = st.columns(2)
with header:
st.title("TuringVideos")
st.write("Este trabalho visa a criação de uma interface capaz de sumarizar e responder perguntas sobre um determinado vídeo em português ou inglês!")
with model:
st.header("Modelo para sumarização")
with model_1:
language = st.selectbox('Qual a linguagem do seu modelo?', ('Português (pt)', 'Inglês (en)', 'Outra'))
link = st.text_area(label="Coloque o link do seu vídeo do YouTube!", height=25, placeholder="Digite seu link...")
questions = st.text_area(label="Coloque suas perguntas separadas por vírgula!", height=50, placeholder="Digite suas perguntas...").split(",")
submit_1 = st.button('Gerar soluções!')
with model_2:
if submit_1:
with st.spinner('Wait for it...'):
if language == 'Português (pt)':
#outputs = portuguese_sum_pipeline(link)
st.write("Modelo ainda não implementado.")
elif language == 'Inglês (en)':
outputs = english_sum_pipeline(link)
answers = english_qa_pipeline(questions, outputs[0])
else:
st.write("Erro na seleção de linguagem.")
st.write("Sumário.....................................................................: \n {} \n \n".format(outputs[1]))
st.write("Resposta....................................................................: \n")
for i in range(len(answers)):
st.write(questions[i] + ": " + answers[i])
''' |