app.py

def transcribe_youtube_video(url, force_transcribe=False):

    text = ''
    try:
        from youtube_transcript_api import YouTubeTranscriptApi
        import pytube
        from pytube import YouTube
        
        vid_id = pytube.extract.video_id(url)
        temp = YouTubeTranscriptApi.get_transcript(vid_id)
        for t in temp:
            text+=t['text']+' '
        yt = YouTube(str(url))
        
    except:
        pass

    if text == '' or force_transcribe:
        from pytube import YouTube
        import torch
        import os
        
        save_dir="./docs/youtube/"
        os.mkdir(save_dir)
        yt = YouTube(str(url))
        audio = yt.streams.filter(only_audio = True).first()
        out_file = audio.download(filename="audio.mp3",output_path = save_dir)
        
        import transformers

        whisper_asr = transformers.pipeline(
            "automatic-speech-recognition", model="openai/whisper-large", device_map= 'auto',
        )
        
        whisper_asr.model.config.forced_decoder_ids = (
            whisper_asr.tokenizer.get_decoder_prompt_ids(
                language="en", 
                task="transcribe"
            )
        )
        temp = whisper_asr(out_file,chunk_length_s=20)
        text = temp['text']

        del(whisper_asr)
        torch.cuda.empty_cache()
    
    return yt.title, text

def summarize_text(title,text):
    
    from langchain.chains.llm import LLMChain
    from langchain.prompts import PromptTemplate
    from langchain.chains import ReduceDocumentsChain, MapReduceDocumentsChain
    from langchain.chains.combine_documents.stuff import StuffDocumentsChain
    import torch
    import transformers
    from transformers import BitsAndBytesConfig
    from transformers import AutoTokenizer, AutoModelForCausalLM

    quantization_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.float16,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_use_double_quant=True,
    )

    # model = "nomic-ai/gpt4all-falcon"
    model = "tiiuae/falcon-7b-instruct"

    tokenizer = AutoTokenizer.from_pretrained(model,trust_remote_code=True,)
    model = AutoModelForCausalLM.from_pretrained(model,
                                                # trust_remote_code=True,
                                                quantization_config=quantization_config,
                                                )

    from langchain import HuggingFacePipeline
    import torch

    pipeline = transformers.pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
        torch_dtype=torch.bfloat16,
        device_map="auto",
        max_new_tokens = 150,
        pad_token_id=tokenizer.eos_token_id,
        # device=-1,
    )

    llm = HuggingFacePipeline(pipeline=pipeline)

    pipeline2 = transformers.pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
        torch_dtype=torch.bfloat16,
        device_map="auto",
        max_new_tokens = 250,
        pad_token_id=tokenizer.eos_token_id,
        repetition_penalty= 2.0,
        # device=-1,
    )

    llm2 = HuggingFacePipeline(pipeline=pipeline2)

    # Map
    map_template = """
    Summarize the following text in a clear and concise way:
    TITLE: `{title}`
    TEXT:`{docs}`
    Brief Summary:
    """
    map_prompt = PromptTemplate(template = map_template, 
                                input_variables = ['title','docs'])
    map_chain = LLMChain(llm=llm, prompt=map_prompt)

    # Reduce - Collapse
    reduce_template = """
    The following is set of partial summaries of a video titled {title}:
    partial summaries: {doc_summaries}
    Take these and distill them into a consolidated summary. 
    Summary:
    """

    reduce_prompt = PromptTemplate(template = reduce_template, 
                                    input_variables = ['title','doc_summaries'])
    reduce_chain = LLMChain(llm=llm, prompt=reduce_prompt)

    # Takes a list of documents, combines them into a single string, and passes this to an LLMChain
    collapse_documents_chain = StuffDocumentsChain(
        llm_chain=reduce_chain, document_variable_name="doc_summaries"
    )

    # Final Reduce - Combine
    final_reduce_template = """
    The following is set of partial summaries of a video titled '{title}':
    partial summaries: 

    {doc_summaries}

    Generate a summary of the whole text that includes `Video Subject`, and the `Key Highlights` as maximum 10 pullet points listing the main facts, arguments, or points:
    """
    final_reduce_prompt = PromptTemplate(template = final_reduce_template, 
                                        input_variables = ['title','doc_summaries'])
    final_reduce_chain = LLMChain(llm=llm2, prompt=final_reduce_prompt)

    # Takes a list of documents, combines them into a single string, and passes this to an LLMChain
    combine_documents_chain = StuffDocumentsChain(
        llm_chain=final_reduce_chain, document_variable_name="doc_summaries"
    )

    # Combines and iteravely reduces the mapped documents
    reduce_documents_chain = ReduceDocumentsChain(
        # This is final chain that is called.
        combine_documents_chain=combine_documents_chain,
        # If documents exceed context for `StuffDocumentsChain`
        collapse_documents_chain=collapse_documents_chain,
        # The maximum number of tokens to group documents into.
        token_max=500,
    )

    # Combining documents by mapping a chain over them, then combining results
    map_reduce_chain = MapReduceDocumentsChain(
        # Map chain
        llm_chain=map_chain,
        # Reduce chain
        reduce_documents_chain=reduce_documents_chain,
        # The variable name in the llm_chain to put the documents in
        document_variable_name="docs",
        # Return the results of the map steps in the output
        return_intermediate_steps=False,
    )

    from langchain.document_loaders import TextLoader
    from langchain.text_splitter import TokenTextSplitter
    
    with open('./docs/transcript.txt','w') as f:
        f.write(text)
    loader = TextLoader("./docs/transcript.txt")
    doc = loader.load()
    text_splitter = TokenTextSplitter(chunk_size=500, chunk_overlap=0)
    docs = text_splitter.split_documents(doc)

    summary = map_reduce_chain.run({'input_documents':docs, 'title':title})

    del(llm)
    del(llm2)
    del(model)
    del(tokenizer)
    torch.cuda.empty_cache()

    return summary

import gradio as gr 
import pytube
from pytube import YouTube

def get_youtube_title(url):
    yt = YouTube(str(url))
    return yt.title

def get_video(url):
    vid_id = pytube.extract.video_id(url)
    embed_html = '<iframe width="100%" height="315" src="https://www.youtube.com/embed/{}" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>'.format(vid_id)
    return embed_html

def summarize_youtube_video(url,force_transcribe):
    title,text = transcribe_youtube_video(url,force_transcribe)
    Summary = summarize_text(title,text)
    return Summary

html = '<iframe width="100%" height="315" src="https://www.youtube.com/embed/" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>'

with gr.Blocks() as demo:
    # gr.Markdown("Transribe a YouTube video using this demo.")
    with gr.Row():
        with gr.Column(scale=3):
            url = gr.Textbox(label="Enter YouTube video URL here:",placeholder="https://www.youtube.com/watch?v=")
            force_transcribe = gr.Checkbox(label="Transcribe even if transcription is available.")
        with gr.Column(scale=1):
            gr.Markdown("# Summarize a YouTube video using this demo!",scale=2)
            sum_btn = gr.Button("Summarize!",scale=1)
    title = gr.Textbox(label="Video Title",placeholder="title...")
    with gr.Row():
        video = gr.HTML(html)
        output = gr.Textbox(label="Summary",placeholder="summary...")
    sum_btn.click(fn=get_youtube_title, inputs=url, outputs=title, api_name="get_youtube_title")
    sum_btn.click(fn=summarize_youtube_video, inputs=[url,force_transcribe], outputs=output, api_name="summarize_youtube_video", queue=True)
    sum_btn.click(fn=get_video, inputs=url, outputs=video, api_name="get_youtube_video",queue=False)

demo.queue()    
demo.launch()