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Update app.py
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app.py
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import
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import pytube
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from youtube_transcript_api import YouTubeTranscriptApi as yt
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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from langchain import PromptTemplate
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from langchain import LLMChain
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from langchain_together import Together
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import re
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import json
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import os
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# Set the API key with double quotes
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os.environ['TOGETHER_API_KEY'] = "d88cb7414e4039a84d2ed63f1b47daaaa4230c4c53a422045d8a30a9a3bc87d8"
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def Summary_BART(text):
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checkpoint = "sshleifer/distilbart-cnn-12-6"
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tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
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inputs = tokenizer(text,
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max_length=1024,
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truncation=True,
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return_tensors="pt")
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summary_ids = model.generate(inputs["input_ids"])
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summary = tokenizer.batch_decode(summary_ids,
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skip_special_tokens=True,
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clean_up_tokenization_spaces=False)
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return summary[0]
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def YtToQuizz(link,difficulty_level):
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Give a 10 different multiple-choice question MCQ related to the summary: {summary}
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The difficulty level of the question should be: {difficulty_level}
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Please provide the following in:
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1. Question
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2. Correct answer
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3. Three plausible incorrect answer options
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4. Proper
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"""
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def main():
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st.title("YouTube video Subtitle to MCQ's Quizz")
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url_link=st.text_area("Enter YouTube video link")
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diffculity_level=st.selectbox("Select diffculity level:",["Eassy","Medium","Hard"])
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if st.button("Generate MCQS Quizz"):
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YtToQuizz(url_link,diffculity_level)
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if __name__ == '__main__':
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import gradio as gr
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import pytube
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from youtube_transcript_api import YouTubeTranscriptApi as yt
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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from langchain import PromptTemplate
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from langchain import LLMChain
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from langchain_together import Together
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# Set the API key with double quotes
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os.environ['TOGETHER_API_KEY'] = "d88cb7414e4039a84d2ed63f1b47daaaa4230c4c53a422045d8a30a9a3bc87d8"
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def Summary_BART(text):
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checkpoint = "sshleifer/distilbart-cnn-12-6"
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tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
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inputs = tokenizer(text, max_length=1024, truncation=True, return_tensors="pt")
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summary_ids = model.generate(inputs["input_ids"])
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summary = tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
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return summary[0]
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def YtToQuizz(link, difficulty_level):
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video_id = pytube.extract.video_id(link)
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transcript = yt.get_transcript(video_id)
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data = ""
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for text in transcript:
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data += text.get('text')
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summary = Summary_BART(data)
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mcq_template = """
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Give a 10 different multiple-choice question MCQ related to the summary: {summary}
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The difficulty level of the question should be: {difficulty_level}
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Please provide the following in:
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1. Question
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2. Correct answer
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3. Three plausible incorrect answer options
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4. Proper MCQ format
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"""
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prompt = PromptTemplate(
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input_variables=['summary', 'difficulty_level'],
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template=mcq_template
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)
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llama3 = Together(model="meta-llama/Llama-3-70b-chat-hf", max_tokens=2500)
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Generated_mcqs = LLMChain(llm=llama3, prompt=prompt)
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response = Generated_mcqs.invoke({
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"summary": summary,
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"difficulty_level": difficulty_level
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})
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response_text = response['text']
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# Extract MCQs, correct answers, and options
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questions = re.findall(r'Question: (.*?)\n', response_text)
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correct_answers = re.findall(r'Correct answer: (.*?)\n', response_text)
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options = re.findall(r'Options: (.*?)\n', response_text)
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all_options = [option.split(', ') for option in options]
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return questions, all_options, correct_answers
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def main(link, difficulty_level):
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questions, options, correct_answers = YtToQuizz(link, difficulty_level)
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return {
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"Questions": questions,
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"Options": options,
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"Correct Answers": correct_answers
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}
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iface = gr.Interface(
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fn=main,
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inputs=[
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gr.inputs.Textbox(lines=2, placeholder="Enter YouTube video link"),
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gr.inputs.Dropdown(["Easy", "Medium", "Hard"], label="Select difficulty level:")
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],
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outputs=[
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gr.outputs.JSON(label="MCQs Output")
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],
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title="YouTube Video Subtitle to MCQs Quiz",
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description="Generate MCQs from YouTube video subtitles"
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)
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if __name__ == '__main__':
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iface.launch()
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