import gradio as gr
from transformers import BartForConditionalGeneration, BartTokenizer
from youtube_transcript_api import YouTubeTranscriptApi

# Load BART model and tokenizer
model_name = 'facebook/bart-large-cnn'
tokenizer = BartTokenizer.from_pretrained(model_name)
model = BartForConditionalGeneration.from_pretrained(model_name)

def get_transcript(url):
    try:
        video_id = url.split('=')[1]
        transcript_list = YouTubeTranscriptApi.get_transcript(video_id)
        transcript_text = ""
        for item in transcript_list:
            transcript_text += item['text'] + "\n"
        return transcript_text
    except Exception as e:
        return "Error fetching transcript: " + str(e)

def summarize_transcript(transcript):
    input_ids = tokenizer.encode("summarize: " + transcript, return_tensors="pt", max_length=1024, truncation=True)
    summary_ids = model.generate(input_ids, num_beams=4, min_length=30, max_length=200, early_stopping=True)
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary

def summarize_video_transcript(url):
    transcript = get_transcript(url)
    if not transcript or transcript.startswith("Error"):
        return transcript
    summary = summarize_transcript(transcript)
    return summary

iface = gr.Interface(fn=summarize_video_transcript,
                     inputs="text",
                     outputs="text",
                     title="YouTube Video Transcription Summarizer",
                     description="Enter YouTube Video URL to get the transcript summary")

if __name__ == "__main__":
    iface.launch()