app.py

import yt_dlp
import os
import streamlit as st
import transformers
from transformers import pipeline
from transformers import AutoTokenizer
import nltk

#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 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():

    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()

'''
#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])

main()
'''