157 lines
5.1 KiB
Python
157 lines
5.1 KiB
Python
import aiohttp
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import asyncio
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import os
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import sys
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import requests
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import io
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from bs4 import BeautifulSoup
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from PyPDF2 import PdfReader
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import tiktoken
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from pipecat.frames.frames import LLMMessagesFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.llm_response import (
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LLMAssistantResponseAggregator, LLMUserResponseAggregator)
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from pipecat.services.cartesia import CartesiaTTSService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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from pipecat.vad.silero import SileroVADAnalyzer
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from runner import configure
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from loguru import logger
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from dotenv import load_dotenv
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load_dotenv(override=True)
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from openai import OpenAI
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client = OpenAI()
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# Run this script directly from your command line.
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# This project was adapted from https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/07d-interruptible-cartesia.py
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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# Count number of tokens used in model and truncate the content
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def truncate_content(content, model_name):
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encoding = tiktoken.encoding_for_model(model_name)
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tokens = encoding.encode(content)
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max_tokens = 10000
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if len(tokens) > max_tokens:
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truncated_tokens = tokens[:max_tokens]
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return encoding.decode(truncated_tokens)
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return content
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# Main function to extract content from url
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def get_article_content(url):
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if 'arxiv.org' in url:
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return get_arxiv_content(url)
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else:
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return get_wikipedia_content(url)
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# Helper function to extract content from Wikipedia url (this is technically agnostic to URL type but will work best with Wikipedia articles)
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def get_wikipedia_content(url):
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response = requests.get(url)
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soup = BeautifulSoup(response.content, 'html.parser')
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content = soup.find('div', {'class': 'mw-parser-output'})
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if content:
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return content.get_text()
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else:
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return "Failed to extract Wikipedia article content."
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# Helper function to extract content from arXiv url
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def get_arxiv_content(url):
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if '/abs/' in url:
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url = url.replace('/abs/', '/pdf/')
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if not url.endswith('.pdf'):
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url += '.pdf'
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response = requests.get(url)
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if response.status_code == 200:
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pdf_file = io.BytesIO(response.content)
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pdf_reader = PdfReader(pdf_file)
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text = ""
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for page in pdf_reader.pages:
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text += page.extract_text()
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return text
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else:
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return "Failed to download arXiv PDF."
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# This is the main function that handles STT -> LLM -> TTS
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async def main():
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url = input("Enter the URL of the article you would like to talk about: ")
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article_content = get_article_content(url)
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article_content = truncate_content(article_content, model_name="gpt-4o-mini")
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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transport = DailyTransport(
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room_url,
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token,
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"studypal",
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DailyParams(
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audio_out_sample_rate=44100,
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audio_out_enabled=True,
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transcription_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer()
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)
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)
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="4d2fd738-3b3d-4368-957a-bb4805275bd9", # British Narration Lady: 4d2fd738-3b3d-4368-957a-bb4805275bd9
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sample_rate=44100,
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)
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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model="gpt-4o-mini")
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messages = [
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{
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"role": "system",
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"content": f"""You are an AI study partner. You have been given the following article content:
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{article_content}
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Your task is to help the user understand and learn from this article in 2 sentences. THESE RESPONSES SHOULD BE ONLY MAX 2 SENTENCES. THIS INSTRUCTION IS VERY IMPORTANT. RESPONSES SHOULDN'T BE LONG.
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""",
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},
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]
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tma_in = LLMUserResponseAggregator(messages)
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tma_out = LLMAssistantResponseAggregator(messages)
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pipeline = Pipeline([
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transport.input(),
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tma_in,
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llm,
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tts,
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tma_out,
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transport.output(),
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])
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task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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transport.capture_participant_transcription(participant["id"])
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messages.append(
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{"role": "system", "content": "Hello! I'm ready to discuss the article with you. What would you like to learn about?"})
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await task.queue_frames([LLMMessagesFrame(messages)])
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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asyncio.run(main()) |