import gradio as gr from youtube_transcript_api import YouTubeTranscriptApi from transformers import AutoTokenizer from transformers import pipeline from transformers import AutoModelForQuestionAnswering import pandas as pd from sentence_transformers import SentenceTransformer, util import torch model_ckpt = "deepset/minilm-uncased-squad2" tokenizer = AutoTokenizer.from_pretrained(model_ckpt) model = AutoModelForQuestionAnswering.from_pretrained(model_ckpt) modelST = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') #input - video link, output - full transcript def get_transcript(link): print("******** Inside get_transcript ********") print(f"link to be extracted is : {link}") video_id = link.split("=")[1] # Handle additional query parameters such as timestamp, ... video_id = video_id.split("&")[0] print(f"video id extracted is : {video_id}") transcript = YouTubeTranscriptApi.get_transcript(video_id) FinalTranscript = ' '.join([i['text'] for i in transcript]) return FinalTranscript,transcript, video_id #input - question and transcript, output - answer timestamp def get_answers_timestamp(question, final_transcript, transcript): print("******** Inside get_answers_timestamp ********") context = final_transcript print(f"Input Question is : {question}") print(f"Type of trancript is : {type(context)}, Length of transcript is : {len(context)}") inputs = tokenizer(question, context, return_overflowing_tokens=True, max_length=512, stride = 25) #getting a list of contexts available after striding contx=[] for window in inputs["input_ids"]: #print(f"{tokenizer.decode(window)} \n") contx.append(tokenizer.decode(window).split('[SEP]')[1].strip()) #print(ques) #print(contx) lst=[] pipe = pipeline("question-answering", model=model, tokenizer=tokenizer) for contexts in contx: lst.append(pipe(question=question, context=contexts)) print(f"contx list is : {contx}") lst_scores = [dicts['score'] for dicts in lst] print(f"lst_scores is : {lst_scores}") #getting highest and second highest scores idxmax = lst_scores.index(max(lst_scores)) lst_scores.remove(max(lst_scores)) idxmax2 = lst_scores.index(max(lst_scores)) sentence_for_timestamp = lst[idxmax]['answer'] sentence_for_timestamp_secondbest = lst[idxmax2]['answer'] dftranscript = pd.DataFrame(transcript) embedding_1= modelST.encode(dftranscript.text, convert_to_tensor=True) embedding_2 = modelST.encode(sentence_for_timestamp, convert_to_tensor=True) embedding_3 = modelST.encode(sentence_for_timestamp_secondbest, convert_to_tensor=True) similarity_tensor = util.pytorch_cos_sim(embedding_1, embedding_2) idx = torch.argmax(similarity_tensor) start_timestamp = dftranscript.iloc[[int(idx)-3]].start.values[0] start_timestamp = round(start_timestamp) similarity_tensor_secondbest = util.pytorch_cos_sim(embedding_1, embedding_3) idx_secondbest = torch.argmax(similarity_tensor_secondbest) start_timestamp_secondbest = dftranscript.iloc[[int(idx_secondbest)-3]].start.values[0] start_timestamp_secondbest = round(start_timestamp_secondbest) return start_timestamp, start_timestamp_secondbest def display_vid(url, question, sample_question=None, example_video=None): print("******** display_vid ********") if question == '': question = sample_question #get embedding and youtube link for initial video html_in = "" #print(html) if len(example_video) !=0 : #is not None: print(f"example_video is : {example_video}") url = example_video[0] #get transcript final_transcript, transcript, video_id = get_transcript(url) #get answer timestamp #input - question and transcript, output - answer timestamp ans_timestamp, ans_timestamp_secondbest = get_answers_timestamp(question, final_transcript, transcript) #created embedding width='560' height='315' html_out = "" print(f"html output is : {html_out}") html_out_secondbest = "" if question == '': print(f"Inside display_vid(), Sample_Question coming from Radio box is BEFORE : {sample_question}") sample_ques = set_example_question(sample_question) print(f"Inside display_vid(), Sample Question coming from Radio box is AFTER : {sample_ques}") else: sample_ques = question return html_out, html_out_secondbest, sample_ques, url def set_example_question(sample_question): print(f"******* Inside Sample Questions ********") print(f"Sample Question coming from Radio box is : {sample_question}") print("What is the Return value : {gr.Radio.update(value=sample_question)}") return gr.Radio.update(value=sample_question) #input_ques.update(example) demo = gr.Blocks() with demo: gr.Markdown("

Have you ever watched a lengthy video or podcast on YouTube and thought it would have been so much better if there had been 'explanatory' timestamps?

") gr.Markdown( """### How many times have you seen a long video/podcast on Youtube and wondered only if there would have been 'explanatory' timestamps it would have been so much better.. **Best part:** You don't even have to move away from the Space tab in your browser as the YouTube video gets played within the given View. """ ) with gr.Row(): input_url = gr.Textbox(label="Input a Youtube video link") input_ques = gr.Textbox(label="Ask a Question") with gr.Row(): output_vid = gr.HTML(label="Video from timestamp 1", show_label=True) output_vid_secondbest = gr.HTML(label="Video from timestamp 2", show_label=True) with gr.Row(): example_question = gr.Dropdown( ["Choose a sample question", "Does video talk about different modalities", "does the model uses perceiver architecture?", "when does the video talk about locked image tuning or lit?", "comparison between gpt3 and jurassic?", "Has flamingo passed turing test yet?", "Any funny examples in video?", "is it possible to download the stylegan model?", "what was very cool?", "what is the cool library?"], label= "Choose a sample Question", value=None) with gr.Row(): example_video = gr.CheckboxGroup( ["https://www.youtube.com/watch?v=smUHQndcmOY"], label= "Choose a sample YouTube video") b1 = gr.Button("Publish Video") b1.click(display_vid, inputs=[input_url, input_ques, example_question, example_video], outputs=[output_vid, output_vid_secondbest, input_ques, input_url]) with gr.Row(): gr.Markdown(''' #### Model Credits 1. [Question Answering](https://huggingface.co/deepset/minilm-uncased-squad2) 1. [Sentence Transformer](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) ''') with gr.Row(): gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=gradio-blocks_ask_questions_to_youtube_videos)") demo.launch(enable_queue=True, debug=True)