124 lines
4.1 KiB
Python
124 lines
4.1 KiB
Python
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import uuid
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import instructor
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import openai
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from burr.core import action, State, ApplicationBuilder
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from pydantic import BaseModel, Field
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from pydantic.json_schema import SkipJsonSchema
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from youtube_transcript_api import YouTubeTranscriptApi
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class QuestionAnswer(BaseModel):
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question: str = Field(description="Question about the topic")
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options: list[str] = Field(
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description="Potential answers to the question.", min_items=3, max_items=5
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)
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answer_index: int = Field(
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description="Index of the correct answer options (starting from 0).", ge=0, lt=5
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)
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difficulty: int = Field(
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description="Difficulty of this question from 1 to 5, 5 being the most difficult.",
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gt=0,
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le=5,
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)
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youtube_url: SkipJsonSchema[str | None] = None
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id: uuid.UUID = Field(description="Unique identifier", default_factory=uuid.uuid4)
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@action(reads=[], writes=["youtube_url"])
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def process_user_input(state: State, user_input: str) -> State:
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"""Process user input and update the YouTube URL."""
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youtube_url = (
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user_input # In practice, we would have more complex validation logic.
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)
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return state.update(youtube_url=youtube_url)
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@action(reads=["youtube_url"], writes=["transcript"])
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def get_youtube_transcript(state: State) -> State:
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"""Get the official YouTube transcript for a video given it's URL"""
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youtube_url = state["youtube_url"]
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_, _, video_id = youtube_url.partition("?v=")
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transcript = YouTubeTranscriptApi.get_transcript(video_id)
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full_transcript = " ".join([entry["text"] for entry in transcript])
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# store the transcript in state
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return state.update(transcript=full_transcript, youtube_url=youtube_url)
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@action(reads=["transcript", "youtube_url"], writes=["question_answers"])
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def generate_question_and_answers(state: State) -> State:
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"""Generate `QuestionAnswer` from a YouTube transcript using an LLM."""
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# read the transcript from state
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transcript = state["transcript"]
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youtube_url = state["youtube_url"]
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# create the instructor client
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instructor_client = instructor.from_openai(openai.OpenAI())
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system_prompt = (
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"Analyze the given YouTube transcript and generate question-answer pairs"
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" to help study and understand the topic better. Please rate all questions from 1 to 5"
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" based on their difficulty."
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)
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response = instructor_client.chat.completions.create_iterable(
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model="gpt-4o-mini",
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response_model=QuestionAnswer,
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": transcript},
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],
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)
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# iterate over QuestionAnswer, add the `youtube_url`, and append to state
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for qna in response:
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qna.youtube_url = youtube_url
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# `State` is immutable, so `.append()` returns a new object with the appended value
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state = state.append(question_answers=qna)
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return state
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def build_application():
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return (
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ApplicationBuilder()
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.with_actions(
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process_user_input,
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get_youtube_transcript,
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generate_question_and_answers,
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)
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.with_transitions(
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("process_user_input", "get_youtube_transcript"),
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("get_youtube_transcript", "generate_question_and_answers"),
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("generate_question_and_answers", "process_user_input"),
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)
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.with_entrypoint("process_user_input")
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.with_tracker(project="youtube-qna")
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.build()
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)
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if __name__ == "__main__":
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app = build_application()
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while True:
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user_input = input("Enter a YouTube URL (q to quit): ")
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if user_input.lower() == "q":
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break
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action_name, result, state = app.run(
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halt_before=["process_user_input"],
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inputs={"user_input": user_input},
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)
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print(f"{len(state['question_answers'])} question-answer pairs generated")
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print("Preview:\n")
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count = 0
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for qna in state["question_answers"]:
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if count > 3:
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break
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print(qna.question)
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print(qna.options)
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print()
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count += 1
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