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import pandas as pd |
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import numpy as np |
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import torch |
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from transformers import pipeline |
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import gradio as gr |
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import os |
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from youtube_transcript_api import YouTubeTranscriptApi |
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summarizer_bart = pipeline("summarization", model="facebook/bart-large-cnn") |
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def summarize(full_txt, min_summ_len=30): |
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l = full_txt.split(" ") |
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l_summ = [] |
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chunk_len = 750 |
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overlap = 50 |
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pointer = 0 |
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flag = True |
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while(flag): |
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if pointer < len(l): |
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if pointer + chunk_len < len(l): |
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txt = " ".join(l[pointer:pointer+chunk_len]) |
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pointer = pointer + chunk_len - overlap |
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l_summ.append(summarizer_ft(txt, max_length=130, min_length=40, do_sample=False)[0]['summary_text']) |
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else: |
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txt = " ".join(l[pointer:]) |
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l_summ.append(summarizer_ft(txt, max_length=len(l) - pointer, min_length=40, do_sample=False)[0]['summary_text']) |
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pointer = len(l) |
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flag = False |
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large_summ = " ".join(l_summ) |
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print(l_summ) |
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l_large_summ = large_summ.split(" ") |
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if len(large_summ.split(" ")) < chunk_len: |
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summ = summarizer_bart(large_summ, max_length=150, min_length=int(min_summ_len), do_sample=False)[0]['summary_text'] |
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else: |
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flag = True |
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pointer = 0 |
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final_summ = [] |
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while(flag): |
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if pointer < len(l_large_summ): |
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if pointer + chunk_len < len(l_large_summ): |
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txt = " ".join(l_large_summ[pointer:pointer+chunk_len]) |
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pointer = pointer + chunk_len - overlap |
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t = summarizer_bart(txt, max_length=130, min_length=40, do_sample=False)[0]['summary_text'] |
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print(t) |
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final_summ.append(t) |
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else: |
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txt = " ".join(l_large_summ[pointer:]) |
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t = summarizer_bart(txt, max_length=len(l_large_summ)-pointer, min_length=40, do_sample=False)[0]['summary_text'] |
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final_summ.append(t) |
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print(t) |
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pointer = len(l_large_summ) |
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flag = False |
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large_summ = " ".join(final_summ) |
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summ = summarizer_bart(large_summ, max_length=100, min_length=int(min_summ_len), do_sample=False)[0]['summary_text'] |
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return summ |
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def extract_text(youtube_video_url,min_summ_len): |
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try: |
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video_id = youtube_video_url.split("=")[1] |
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transcript_text = YouTubeTranscriptApi.get_transcript(video_id) |
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transcript = "" |
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for i in transcript_text: |
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transcript += " " + i["text"] |
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print(transcript) |
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res = summarize(transcript,min_summ_len) |
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return res |
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except Exception as e: |
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raise e |
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demo = gr.Interface( |
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fn=extract_text, |
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inputs=["text","number"], |
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outputs="text", |
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title="YouTube Video Text Summarization for Efficient Information Capture", |
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description="Generate concise summaries of your YouTube Video Text tailored to your specific needs.", |
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) |
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demo.launch(debug=True) |
