WIP: podcast feature
This commit is contained in:
LUIS NOVO
parent
10ec265a44
commit
177d2c2b93
4 changed files with 686 additions and 28 deletions
179
open_notebook/graphs/website_extractor.py
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179
open_notebook/graphs/website_extractor.py
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"""
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Website Extractor Module
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This module is responsible for extracting clean text content from websites using
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BeautifulSoup for local HTML parsing instead of the Jina AI API.
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"""
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import html
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import logging
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import re
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from typing import List
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from urllib.parse import urlparse
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import requests
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from bs4 import BeautifulSoup
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from podcastfy.utils.config import load_config
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logger = logging.getLogger(__name__)
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class WebsiteExtractor:
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def __init__(self):
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"""
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Initialize the WebsiteExtractor.
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"""
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self.config = load_config()
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self.website_extractor_config = self.config.get("website_extractor", {})
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self.unwanted_tags = self.website_extractor_config.get("unwanted_tags", [])
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self.user_agent = self.website_extractor_config.get("user_agent", "Mozilla/5.0")
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self.timeout = self.website_extractor_config.get("timeout", 10)
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self.remove_patterns = self.website_extractor_config.get(
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"markdown_cleaning", {}
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).get("remove_patterns", [])
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def extract_content(self, url: str) -> str:
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"""
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Extract clean text content from a website using BeautifulSoup.
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Args:
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url (str): Website URL.
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Returns:
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str: Extracted clean text content.
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Raises:
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Exception: If there's an error in extracting the content.
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"""
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try:
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# Normalize the URL
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normalized_url = self.normalize_url(url)
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# Request the webpage
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headers = {"User-Agent": self.user_agent}
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response = requests.get(
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normalized_url, headers=headers, timeout=self.timeout
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)
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response.raise_for_status() # Raise an exception for bad status codes
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# Parse the page content with BeautifulSoup
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soup = BeautifulSoup(response.text, "html.parser")
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# Remove unwanted elements
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self.remove_unwanted_elements(soup)
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# Extract and clean the text content
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raw_text = soup.get_text(separator="\n") # Get all text content
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cleaned_content = self.clean_content(raw_text)
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return cleaned_content
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except requests.RequestException as e:
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logger.error(f"Failed to extract content from {url}: {str(e)}")
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raise Exception(f"Failed to extract content from {url}: {str(e)}")
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except Exception as e:
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logger.error(
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f"An unexpected error occurred while extracting content from {url}: {str(e)}"
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)
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raise Exception(
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f"An unexpected error occurred while extracting content from {url}: {str(e)}"
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)
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def normalize_url(self, url: str) -> str:
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"""
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Normalize the given URL by adding scheme if missing and ensuring it's a valid URL.
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Args:
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url (str): The URL to normalize.
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Returns:
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str: The normalized URL.
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Raises:
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ValueError: If the URL is invalid after normalization attempts.
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"""
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# If the URL doesn't start with a scheme, add 'https://'
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if not url.startswith(("http://", "https://")):
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url = "https://" + url
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# Parse the URL
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parsed = urlparse(url)
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# Ensure the URL has a valid scheme and netloc
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if not all([parsed.scheme, parsed.netloc]):
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raise ValueError(f"Invalid URL: {url}")
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return parsed.geturl()
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def remove_unwanted_elements(self, soup: BeautifulSoup) -> None:
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"""
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Remove unwanted elements from the BeautifulSoup object.
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Args:
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soup (BeautifulSoup): The BeautifulSoup object to clean.
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"""
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for tag in self.unwanted_tags:
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for element in soup.find_all(tag):
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element.decompose()
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def clean_content(self, content: str) -> str:
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"""
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Clean the extracted content by removing unnecessary whitespace and applying
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custom cleaning patterns.
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Args:
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content (str): The content to clean.
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Returns:
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str: Cleaned text content.
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"""
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# Decode HTML entities
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cleaned_content = html.unescape(content)
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# Remove extra whitespace
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cleaned_content = re.sub(r"\s+", " ", cleaned_content)
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# Remove extra newlines
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cleaned_content = re.sub(r"\n{3,}", "\n\n", cleaned_content)
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# Apply custom cleaning patterns from config
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for pattern in self.remove_patterns:
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cleaned_content = re.sub(pattern, "", cleaned_content)
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return cleaned_content.strip()
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def main(seed: int = 42) -> None:
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"""
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Main function to test the WebsiteExtractor class.
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"""
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logging.basicConfig(level=logging.INFO)
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# Create an instance of WebsiteExtractor
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extractor = WebsiteExtractor()
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# Test URLs
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test_urls: List[str] = [
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"www.souzatharsis.com",
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"https://en.wikipedia.org/wiki/Web_scraping",
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]
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for url in test_urls:
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try:
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logger.info(f"Extracting content from: {url}")
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content = extractor.extract_content(url)
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# Print the first 500 characters of the extracted content
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logger.info(
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f"Extracted content (first 500 characters):\n{content[:500]}..."
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)
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# Print the total length of the extracted content
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logger.info(f"Total length of extracted content: {len(content)} characters")
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logger.info("-" * 50)
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except Exception as e:
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logger.error(f"An error occurred while processing {url}: {str(e)}")
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if __name__ == "__main__":
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main()
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307
open_notebook/plugins/podcasts.py
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307
open_notebook/plugins/podcasts.py
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from typing import ClassVar, List, Literal
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from pydantic import Field, field_validator
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from open_notebook.domain import ObjectModel
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class PodcastEpisode(ObjectModel):
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table_name: ClassVar[str] = "podcast_episode"
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name: str
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template: str
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instructions: str
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file_path: str
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class PodcastConfig(ObjectModel):
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table_name: ClassVar[str] = "podcast_config"
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name: str
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podcast_name: str
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podcast_tagline: str
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output_language: str = Field(default="English")
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person1_role: str
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person2_role: str
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conversation_style: List[str]
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engagement_technique: List[str]
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dialogue_structure: List[str]
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wordcount: int = Field(gt=500, lt=10000)
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creativity: float = Field(ge=0, le=1)
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provider: Literal["openai", "elevenlabs", "edge"] = Field(default="openai")
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voice1: str
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voice2: str
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model: str
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@field_validator("wordcount")
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def validate_wordcount(cls, value):
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if not 500 <= value <= 6000:
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raise ValueError("Wordcount must be between 500 and 10000")
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return value
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@field_validator("creativity")
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def validate_creativity(cls, value):
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if not 0 <= value <= 1:
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raise ValueError("Creativity must be between 0 and 1")
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return value
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conversation_styles = [
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"Analytical",
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"Argumentative",
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"Informative",
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"Humorous",
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"Casual",
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"Formal",
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"Inspirational",
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"Debate-style",
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"Interview-style",
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"Storytelling",
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"Reflective",
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"Narrative",
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"Satirical",
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"Educational",
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"Conversational",
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"Critical",
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"Empathetic",
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"Philosophical",
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"Speculative",
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"Motivational",
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"Fun",
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"Technical",
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"Light-hearted",
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"Serious",
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"Investigative",
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"Debunking",
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"Collaborative",
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"Didactic",
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"Thought-provoking",
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"Controversial",
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"Skeptical",
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"Optimistic",
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"Pessimistic",
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"Objective",
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"Subjective",
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"Sarcastic",
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"Emotional",
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"Exploratory",
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"Friendly",
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"Fast-paced",
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"Slow-paced",
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"Introspective",
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"Open-ended",
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"Affirmative",
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"Dissenting",
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]
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# Dialogue Structures
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dialogue_structures = [
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"Topic Introduction",
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"Opening Monologue",
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"Guest Introduction",
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"Icebreakers",
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"Historical Context",
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"Defining Terms",
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"Problem Statement",
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"Overview of the Issue",
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"Deep Dive into Subtopics",
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"Pro Arguments",
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"Con Arguments",
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"Cross-examination",
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"Rebuttal",
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"Expert Interviews",
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"Panel Discussion",
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"Case Studies",
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"Myth Busting",
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"Debunking Misconceptions",
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"Audience Questions",
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"Q&A Session",
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"Listener Feedback",
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"Rapid-fire Questions",
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"Summary of Key Points",
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"Recap",
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"Key Takeaways",
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"Actionable Tips",
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"Call to Action",
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"Future Outlook",
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"Teaser for Next Episode",
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"Closing Remarks",
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"Thank You and Credits",
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"Outtakes or Bloopers",
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"Sponsor Messages",
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"Social Media Shout-outs",
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"Resource Recommendations",
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"Feedback Request",
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"Lightning Round",
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"Behind-the-Scenes Insights",
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"Ethical Considerations",
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"Fact-checking Segment",
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"Trending Topics",
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"Closing Inspirational Quote",
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"Final Reflections",
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"Debrief",
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"Farewell Messages",
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"Next Episode Preview",
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"Live Reactions",
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"Call-in Segment",
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"Acknowledgements",
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"Transition Segments",
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"Break Segments",
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]
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# Podcast Participant Roles
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participant_roles = [
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"Main Summarizer",
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"Questioner/Clarifier",
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"Optimist",
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"Skeptic",
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"Specialist",
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"Thesis Presenter",
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"Counterargument Provider",
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"Professor",
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"Student",
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"Moderator",
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"Host",
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"Co-host",
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"Expert Guest",
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"Novice",
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"Devil's Advocate",
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"Analyst",
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"Storyteller",
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"Fact-checker",
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"Comedian",
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"Interviewer",
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"Interviewee",
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"Historian",
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"Visionary",
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"Strategist",
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"Critic",
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"Enthusiast",
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"Mediator",
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"Commentator",
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"Researcher",
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"Reporter",
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"Advocate",
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"Influencer",
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"Observer",
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"Listener",
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"Facilitator",
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"Innovator",
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"Debater",
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"Educator",
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"Motivator",
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"Narrator",
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"Explorer",
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"Opponent",
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"Proponent",
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"Philosopher",
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"Engineer",
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"Doctor",
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"Psychologist",
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"Economist",
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"Politician",
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"Scientist",
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"Entrepreneur",
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"Artist",
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"Author",
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"Journalist",
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"Activist",
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"Challenger",
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"Supporter",
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"Mentor",
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"Mentee",
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"Panelist",
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"Audience Representative",
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"Case Study Presenter",
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"Data Analyst",
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"Ethicist",
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"Cultural Critic",
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"Technologist",
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"Environmentalist",
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"Legal Expert",
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"Healthcare Professional",
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"Financial Advisor",
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"Policy Maker",
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"Sociologist",
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"Anthropologist",
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"Myth Buster",
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"Trend Analyst",
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"Futurist",
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"Negotiator",
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"Community Leader",
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"Voice of Reason",
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"Conflict Resolver",
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"Emotional Support",
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"Pragmatist",
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"Idealist",
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"Realist",
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"Satirist",
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"Story Analyst",
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"Language Expert",
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"Historical Witness",
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"Survivor",
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"Inspirational Figure",
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"Cultural Ambassador",
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"Digital Nomad",
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"Remote Correspondent",
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"Field Reporter",
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"Data Scientist",
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"Gamer",
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"Musician",
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"Filmmaker",
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]
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# Engagement Techniques
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engagement_techniques = [
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"Rhetorical Questions",
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"Anecdotes",
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"Analogies",
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"Humor",
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"Metaphors",
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"Storytelling",
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"Quizzes",
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"Polls",
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"Contests/Giveaways",
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"Guest Appearances",
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"Sound Effects",
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"Music Interludes",
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"Shout-outs",
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"Interactive Challenges",
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"Personal Testimonials",
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"Quotes",
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"Jokes",
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"Surprise Elements",
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"Emotional Appeals",
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"Provocative Statements",
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"Irony",
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"Sarcasm",
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"Alliteration",
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"Repetition",
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"Foreshadowing",
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"Cliffhangers",
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"Audience Participation",
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"Sensory Descriptions",
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"Visual Aids (if applicable)",
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"Callbacks to Earlier Points",
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"Pop Culture References",
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"Hyperbole",
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"Parables",
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"Thought Experiments",
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"Puzzles and Riddles",
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"Role-playing",
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"Mock Scenarios",
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"Debates",
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"Sound Bites",
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"Catchphrases",
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"Voice Modulation",
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"Interactive Games",
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"Live Demos",
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"Behind-the-Scenes Insights",
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"Vivid Imagery",
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"Statistics and Facts",
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"Open-ended Questions",
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"Challenges to Assumptions",
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"Evoking Curiosity",
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"Memes (if visual components are included)",
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"Surveys",
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"Testimonials",
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"Provocations",
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]
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150
pages/5_🎙️_Podcasts.py
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150
pages/5_🎙️_Podcasts.py
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import streamlit as st
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from streamlit_tags import st_tags
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from open_notebook.plugins.podcasts import (
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PodcastConfig,
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PodcastEpisode,
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conversation_styles,
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dialogue_structures,
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engagement_techniques,
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participant_roles,
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)
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episodes_tab, templates_tab = st.tabs(["Episodes", "Templates"])
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with episodes_tab:
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episodes = PodcastEpisode.get_all()
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for episode in episodes:
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st.json(episode.model_dump())
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else:
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st.write("No episodes yet")
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with templates_tab:
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st.subheader("Podcast Templates")
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st.markdown("")
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with st.expander("**Create new Template**"):
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pd_cfg = {}
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pd_cfg["name"] = st.text_input("Template Name")
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pd_cfg["podcast_name"] = st.text_input("Podcast Name")
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pd_cfg["podcast_tagline"] = st.text_input("Podcast Tagline")
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pd_cfg["output_language"] = st.text_input("Language", value="English")
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pd_cfg["person1_role"] = st.text_input("Person 1 role")
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st.caption(f"Suggestions:{', '.join(participant_roles)}")
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pd_cfg["person2_role"] = st.text_input("Person 2 role")
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pd_cfg["conversation_style"] = st_tags(
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["a"], conversation_styles, "Conversation Style"
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)
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st.caption(f"Suggestions:{', '.join(conversation_styles)}")
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pd_cfg["engagement_technique"] = st_tags(
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[], engagement_techniques, "Engagement Techniques"
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)
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st.caption(f"Suggestions:{', '.join(engagement_techniques)}")
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pd_cfg["dialogue_structure"] = st_tags(
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[], dialogue_structures, "Dialogue Structure"
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)
|
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st.caption(f"Suggestions:{', '.join(dialogue_structures)}")
|
||||
pd_cfg["wordcount"] = st.slider(
|
||||
"Word Count", min_value=400, max_value=6000, step=50
|
||||
)
|
||||
pd_cfg["creativity"] = st.slider(
|
||||
"Creativity", min_value=0.0, max_value=1.0, step=0.05
|
||||
)
|
||||
pd_cfg["provider"] = st.selectbox("Provider", ["openai", "elevenlabs", "edge"])
|
||||
pd_cfg["voice1"] = st.text_input("Voice 1")
|
||||
pd_cfg["voice2"] = st.text_input("Voice 2")
|
||||
pd_cfg["model"] = st.text_input("Model")
|
||||
if st.button("Save"):
|
||||
pd = PodcastConfig(**pd_cfg)
|
||||
pd.save()
|
||||
st.success("Saved")
|
||||
|
||||
for pd_config in PodcastConfig.get_all():
|
||||
with st.expander(pd_config.name):
|
||||
pd_config.name = st.text_input(
|
||||
"Template Name", value=pd_config.name, key=f"name_{pd_config.id}"
|
||||
)
|
||||
pd_config.podcast_name = st.text_input(
|
||||
"Podcast Name",
|
||||
value=pd_config.podcast_name,
|
||||
key=f"podcast_name_{pd_config.id}",
|
||||
)
|
||||
pd_config.podcast_tagline = st.text_input(
|
||||
"Podcast Tagline",
|
||||
value=pd_config.podcast_tagline,
|
||||
key=f"podcast_tagline_{pd_config.id}",
|
||||
)
|
||||
pd_config.output_language = st.text_input(
|
||||
"Language",
|
||||
value=pd_config.output_language,
|
||||
key=f"output_language_{pd_config.id}",
|
||||
)
|
||||
pd_config.person1_role = st.text_input(
|
||||
"Person 1 role",
|
||||
value=pd_config.person1_role,
|
||||
key=f"person1_role_{pd_config.id}",
|
||||
)
|
||||
st.caption(f"Suggestions:{', '.join(participant_roles)}")
|
||||
pd_config.person2_role = st.text_input(
|
||||
"Person 2 role",
|
||||
value=pd_config.person2_role,
|
||||
key=f"person2_role_{pd_config.id}",
|
||||
)
|
||||
pd_config.conversation_style = st_tags(
|
||||
pd_config.conversation_style,
|
||||
conversation_styles,
|
||||
"Conversation Style",
|
||||
key=f"conversation_style_{pd_config.id}",
|
||||
)
|
||||
st.caption(f"Suggestions:{', '.join(conversation_styles)}")
|
||||
pd_config.engagement_technique = st_tags(
|
||||
pd_config.engagement_technique,
|
||||
engagement_techniques,
|
||||
"Engagement Techniques",
|
||||
key=f"engagement_technique_{pd_config.id}",
|
||||
)
|
||||
st.caption(f"Suggestions:{', '.join(engagement_techniques)}")
|
||||
pd_config.dialogue_structure = st_tags(
|
||||
pd_config.dialogue_structure,
|
||||
dialogue_structures,
|
||||
"Dialogue Structure",
|
||||
key=f"dialogue_structure_{pd_config.id}",
|
||||
)
|
||||
st.caption(f"Suggestions:{', '.join(dialogue_structures)}")
|
||||
pd_config.wordcount = st.slider(
|
||||
"Word Count",
|
||||
min_value=400,
|
||||
max_value=6000,
|
||||
step=50,
|
||||
value=pd_config.wordcount,
|
||||
key=f"wordcount_{pd_config.id}",
|
||||
)
|
||||
pd_config.creativity = st.slider(
|
||||
"Creativity",
|
||||
min_value=0.0,
|
||||
max_value=1.0,
|
||||
step=0.05,
|
||||
value=pd_config.creativity,
|
||||
key=f"creativity_{pd_config.id}",
|
||||
)
|
||||
pd_config.provider = st.selectbox(
|
||||
"Provider",
|
||||
["openai", "elevenlabs", "edge"],
|
||||
index=["openai", "elevenlabs", "edge"].index(pd_config.provider),
|
||||
key=f"provider_{pd_config.id}",
|
||||
)
|
||||
pd_config.voice1 = st.text_input(
|
||||
"Voice 1", value=pd_config.voice1, key=f"voice1_{pd_config.id}"
|
||||
)
|
||||
pd_config.voice2 = st.text_input(
|
||||
"Voice 2", value=pd_config.voice2, key=f"voice2_{pd_config.id}"
|
||||
)
|
||||
pd_config.model = st.text_input(
|
||||
"Model", value=pd_config.model, key=f"model_{pd_config.id}"
|
||||
)
|
||||
|
||||
if st.button("Save Config", key=f"btn_save{pd_config.id}"):
|
||||
pd_config.save()
|
||||
st.rerun()
|
||||
|
||||
if st.button("Delete Config", key=f"btn_delete{pd_config.id}"):
|
||||
pd_config.delete()
|
||||
st.rerun()
|
||||
|
|
@ -3,6 +3,7 @@ from langchain_core.runnables import RunnableConfig
|
|||
|
||||
from open_notebook.domain import Note, Source
|
||||
from open_notebook.graphs.chat import graph as chat_graph
|
||||
from open_notebook.plugins.podcasts import PodcastConfig, PodcastEpisode
|
||||
from open_notebook.utils import token_count
|
||||
|
||||
|
||||
|
|
@ -52,38 +53,59 @@ def execute_chat(txt_input, session_id):
|
|||
return result
|
||||
|
||||
|
||||
podcast_configs = PodcastConfig.get_all()
|
||||
podcast_config_names = [pd.name for pd in podcast_configs]
|
||||
|
||||
|
||||
# todo: se eu for usar o token count, preciso deixar configuravel
|
||||
# seria bom ter um total de tokens no admin em algum lugar
|
||||
def chat_sidebar(session_id):
|
||||
context = build_context(session_id=session_id)
|
||||
tokens = token_count(str(context) + str(st.session_state[session_id]["messages"]))
|
||||
with st.container(border=True):
|
||||
request = st.chat_input("Enter your question")
|
||||
# removing for now since it's not multi-model capable right now
|
||||
st.caption(f"Total tokens: {tokens}")
|
||||
if request:
|
||||
response = execute_chat(txt_input=request, session_id=session_id)
|
||||
st.session_state[session_id]["messages"] = response["messages"]
|
||||
chat_tab, podcast_tab = st.tabs(["Chat", "Podcast"])
|
||||
with podcast_tab:
|
||||
with st.container(border=True):
|
||||
template = st.selectbox("Pick a template", podcast_config_names)
|
||||
episode_name = st.text_input("Episode Name")
|
||||
instructions = st.text_area("Instructions")
|
||||
if st.button("Generate"):
|
||||
epi = PodcastEpisode(
|
||||
name=episode_name,
|
||||
instructions=instructions,
|
||||
template=template,
|
||||
file_path="lallaa",
|
||||
)
|
||||
epi.save()
|
||||
st.page_link("pages/5_🎙️_Podcasts.py", label="Go to Config")
|
||||
st.divider()
|
||||
with chat_tab:
|
||||
with st.container(border=True):
|
||||
request = st.chat_input("Enter your question")
|
||||
# removing for now since it's not multi-model capable right now
|
||||
st.caption(f"Total tokens: {tokens}")
|
||||
if request:
|
||||
response = execute_chat(txt_input=request, session_id=session_id)
|
||||
st.session_state[session_id]["messages"] = response["messages"]
|
||||
|
||||
for msg in st.session_state[session_id]["messages"][::-1]:
|
||||
if msg.type not in ["human", "ai"]:
|
||||
continue
|
||||
if not msg.content:
|
||||
continue
|
||||
for msg in st.session_state[session_id]["messages"][::-1]:
|
||||
if msg.type not in ["human", "ai"]:
|
||||
continue
|
||||
if not msg.content:
|
||||
continue
|
||||
|
||||
with st.chat_message(name=msg.type):
|
||||
st.write(msg.content)
|
||||
if msg.type == "ai":
|
||||
if st.button("💾 New Note", key=f"render_save_{msg.id}"):
|
||||
title = "New Note"
|
||||
content = msg.content
|
||||
note = Note(
|
||||
title=title,
|
||||
content=content,
|
||||
note_type="ai",
|
||||
)
|
||||
note.save()
|
||||
note.add_to_notebook(
|
||||
st.session_state[session_id]["notebook"].id
|
||||
)
|
||||
st.rerun()
|
||||
with st.chat_message(name=msg.type):
|
||||
st.write(msg.content)
|
||||
if msg.type == "ai":
|
||||
if st.button("💾 New Note", key=f"render_save_{msg.id}"):
|
||||
title = "New Note"
|
||||
content = msg.content
|
||||
note = Note(
|
||||
title=title,
|
||||
content=content,
|
||||
note_type="ai",
|
||||
)
|
||||
note.save()
|
||||
note.add_to_notebook(
|
||||
st.session_state[session_id]["notebook"].id
|
||||
)
|
||||
st.rerun()
|
||||
|
|
|
|||
Loading…
Reference in a new issue