diff --git a/.gitignore b/.gitignore index a8d9885..c9a82e2 100644 --- a/.gitignore +++ b/.gitignore @@ -118,3 +118,4 @@ desktop.ini *.db *.sqlite3 +.quarentena \ No newline at end of file diff --git a/open_notebook/graphs/content_processing/__init__.py b/open_notebook/graphs/content_processing/__init__.py deleted file mode 100644 index 5e22311..0000000 --- a/open_notebook/graphs/content_processing/__init__.py +++ /dev/null @@ -1,145 +0,0 @@ -import os -from typing import Any, Dict - -import magic -from langgraph.graph import END, START, StateGraph -from loguru import logger - -from open_notebook.exceptions import UnsupportedTypeException -from open_notebook.graphs.content_processing.audio import extract_audio -from open_notebook.graphs.content_processing.office import ( - SUPPORTED_OFFICE_TYPES, - extract_office_content, -) -from open_notebook.graphs.content_processing.pdf import ( - SUPPORTED_FITZ_TYPES, - extract_pdf, -) -from open_notebook.graphs.content_processing.state import ContentState -from open_notebook.graphs.content_processing.text import extract_txt -from open_notebook.graphs.content_processing.url import extract_url, url_provider -from open_notebook.graphs.content_processing.video import extract_best_audio_from_video -from open_notebook.graphs.content_processing.youtube import extract_youtube_transcript - - -async def source_identification(state: ContentState) -> Dict[str, str]: - """ - Identify the content source based on parameters - """ - if state.get("content"): - doc_type = "text" - elif state.get("file_path"): - doc_type = "file" - elif state.get("url"): - doc_type = "url" - else: - raise ValueError("No source provided.") - - return {"source_type": doc_type} - - -async def file_type(state: ContentState) -> Dict[str, Any]: - """ - Identify the file using python-magic - """ - return_dict = {} - file_path = state.get("file_path") - if file_path is not None: - return_dict["identified_type"] = magic.from_file(file_path, mime=True) - return_dict["title"] = os.path.basename(file_path) - return return_dict - - -async def file_type_edge(data: ContentState) -> str: - assert data.get("identified_type"), "Type not identified" - identified_type = data["identified_type"] - - if identified_type == "text/plain": - return "extract_txt" - elif identified_type in SUPPORTED_FITZ_TYPES: - return "extract_pdf" - elif identified_type in SUPPORTED_OFFICE_TYPES: - return "extract_office_content" - elif identified_type.startswith("video"): - return "extract_best_audio_from_video" - elif identified_type.startswith("audio"): - return "extract_audio" - else: - raise UnsupportedTypeException( - f"Unsupported file type: {data.get('identified_type')}" - ) - - -async def delete_file(data: ContentState) -> Dict[str, Any]: - if data.get("delete_source"): - logger.debug(f"Deleting file: {data.get('file_path')}") - file_path = data.get("file_path") - if file_path is not None: - try: - os.remove(file_path) - return {"file_path": None} - except FileNotFoundError: - logger.warning(f"File not found while trying to delete: {file_path}") - else: - logger.debug("Not deleting file") - return {} - - -async def url_type_router(x: ContentState) -> str: - return x.get("identified_type", "") - - -async def source_type_router(x: ContentState) -> str: - return x.get("source_type", "") - - -# Create workflow -workflow = StateGraph(ContentState) - -# Add nodes -workflow.add_node("source", source_identification) -workflow.add_node("url_provider", url_provider) -workflow.add_node("file_type", file_type) -workflow.add_node("extract_txt", extract_txt) -workflow.add_node("extract_pdf", extract_pdf) -workflow.add_node("extract_url", extract_url) -workflow.add_node("extract_office_content", extract_office_content) -workflow.add_node("extract_best_audio_from_video", extract_best_audio_from_video) -workflow.add_node("extract_audio", extract_audio) -workflow.add_node("extract_youtube_transcript", extract_youtube_transcript) -workflow.add_node("delete_file", delete_file) - -# Add edges -workflow.add_edge(START, "source") -workflow.add_conditional_edges( - "source", - source_type_router, - { - "url": "url_provider", - "file": "file_type", - "text": END, - }, -) -workflow.add_conditional_edges( - "file_type", - file_type_edge, -) -workflow.add_conditional_edges( - "url_provider", - url_type_router, - {"article": "extract_url", "youtube": "extract_youtube_transcript"}, -) -workflow.add_edge("url_provider", END) -workflow.add_edge("file_type", END) -workflow.add_edge("extract_url", END) -workflow.add_edge("extract_txt", END) -workflow.add_edge("extract_youtube_transcript", END) - -workflow.add_edge("extract_pdf", "delete_file") -workflow.add_edge("extract_office_content", "delete_file") -workflow.add_edge("extract_best_audio_from_video", "extract_audio") -workflow.add_edge("extract_audio", "delete_file") -workflow.add_edge("delete_file", END) - -# Compile graph -graph = workflow.compile() diff --git a/open_notebook/graphs/content_processing/audio.py b/open_notebook/graphs/content_processing/audio.py deleted file mode 100644 index 6201788..0000000 --- a/open_notebook/graphs/content_processing/audio.py +++ /dev/null @@ -1,114 +0,0 @@ -import asyncio -import os -from functools import partial -from math import ceil - -from loguru import logger -from pydub import AudioSegment - -from open_notebook.domain.models import model_manager -from open_notebook.graphs.content_processing.state import ContentState - -# todo: remove reference to model_manager -# future: parallelize the transcription process - - -async def split_audio(input_file, segment_length_minutes=15, output_prefix=None): - """ - Split an audio file into segments asynchronously. - """ - - def _split(input_file, segment_length_minutes, output_prefix): - # Convert input file to absolute path - input_file_abs = os.path.abspath(input_file) - output_dir = os.path.dirname(input_file_abs) - os.makedirs(output_dir, exist_ok=True) - - # Set up output prefix - if output_prefix is None: - output_prefix = os.path.splitext(os.path.basename(input_file_abs))[0] - - # Load the audio file - audio = AudioSegment.from_file(input_file_abs) - - # Calculate segment length in milliseconds - segment_length_ms = segment_length_minutes * 60 * 1000 - - # Calculate number of segments - total_segments = ceil(len(audio) / segment_length_ms) - logger.debug(f"Splitting file: {input_file_abs} into {total_segments} segments") - - output_files = [] - - # Split the audio into segments - for i in range(total_segments): - start_time = i * segment_length_ms - end_time = min((i + 1) * segment_length_ms, len(audio)) - - # Extract segment - segment = audio[start_time:end_time] - - # Generate output filename - output_filename = f"{output_prefix}_{str(i+1).zfill(3)}.mp3" - output_path = os.path.join(output_dir, output_filename) - - # Export segment - segment.export(output_path, format="mp3") - output_files.append(output_path) - - logger.debug(f"Exported segment {i+1}/{total_segments}: {output_filename}") - - return output_files - - # Run CPU-bound audio processing in thread pool - return await asyncio.get_event_loop().run_in_executor( - None, partial(_split, input_file, segment_length_minutes, output_prefix) - ) - - -async def transcribe_audio_segment(audio_file, model): - """Transcribe a single audio segment asynchronously""" - - def _transcribe(audio_file, model): - return model.transcribe(audio_file) - - return await asyncio.get_event_loop().run_in_executor( - None, partial(_transcribe, audio_file, model) - ) - - -async def extract_audio(data: ContentState): - SPEECH_TO_TEXT_MODEL = model_manager.speech_to_text - input_audio_path = data.get("file_path") - audio_files = [] - - try: - # Split audio into segments - audio_files = await split_audio(input_audio_path) - - # Transcribe all segments concurrently - transcribe_tasks = [ - transcribe_audio_segment(audio_file, SPEECH_TO_TEXT_MODEL) - for audio_file in audio_files - ] - transcriptions = await asyncio.gather(*transcribe_tasks) - - return {"content": " ".join(transcriptions)} - - except Exception as e: - logger.error(f"Error transcribing audio: {str(e)}") - logger.exception(e) - raise - - finally: - # Clean up temporary files - def _cleanup(files): - for file in files: - try: - os.remove(file) - except OSError as e: - logger.error(f"Error removing temporary file {file}: {str(e)}") - - await asyncio.get_event_loop().run_in_executor( - None, partial(_cleanup, audio_files) - ) diff --git a/open_notebook/graphs/content_processing/office.py b/open_notebook/graphs/content_processing/office.py deleted file mode 100644 index 98f8ea5..0000000 --- a/open_notebook/graphs/content_processing/office.py +++ /dev/null @@ -1,323 +0,0 @@ -import asyncio -from functools import partial - -from docx import Document -from loguru import logger -from openpyxl import load_workbook -from pptx import Presentation - -from open_notebook.graphs.content_processing.state import ContentState - -SUPPORTED_OFFICE_TYPES = [ - "application/vnd.openxmlformats-officedocument.wordprocessingml.document", - "application/vnd.openxmlformats-officedocument.presentationml.presentation", - "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", -] - - -async def extract_docx_content_detailed(file_path): - """Extract content from DOCX file""" - - def _extract(): - try: - doc = Document(file_path) - content = [] - - for paragraph in doc.paragraphs: - if not paragraph.text.strip(): - continue - - style = paragraph.style.name if paragraph.style else "Normal" - text = paragraph.text.strip() - - # Get paragraph formatting - p_format = paragraph.paragraph_format - indent = p_format.left_indent or 0 - - # Convert indent to spaces (1 level = 4 spaces) - indent_level = 0 - if hasattr(indent, "pt"): - indent_level = int(indent.pt / 72) # 72 points = 1 inch - indent_spaces = " " * (indent_level * 4) - - # Handle different types of formatting - if "Heading" in style: - level = style[-1] if style[-1].isdigit() else "1" - heading_marks = "#" * int(level) - content.append(f"\n{heading_marks} {text}\n") - - # Handle bullet points - elif ( - paragraph.style - and hasattr(paragraph.style, "name") - and paragraph.style.name.startswith("List") - ): - # Numbered list - if ( - hasattr(paragraph._p, "pPr") - and paragraph._p.pPr is not None - and hasattr(paragraph._p.pPr, "numPr") - and paragraph._p.pPr.numPr is not None - ): - # Try to get the actual number - try: - if ( - hasattr(paragraph._p.pPr.numPr, "numId") - and paragraph._p.pPr.numPr.numId is not None - and hasattr(paragraph._p.pPr.numPr.numId, "val") - ): - number = paragraph._p.pPr.numPr.numId.val - content.append(f"{indent_spaces}{number}. {text}") - else: - content.append(f"{indent_spaces}1. {text}") - except Exception: - content.append(f"{indent_spaces}1. {text}") - # Bullet list - else: - content.append(f"{indent_spaces}* {text}") - - else: - # Handle text formatting - formatted_text = [] - for run in paragraph.runs: - if run.bold: - formatted_text.append(f"**{run.text}**") - elif run.italic: - formatted_text.append(f"*{run.text}*") - else: - formatted_text.append(run.text) - - content.append(f"{indent_spaces}{''.join(formatted_text)}") - - return "\n\n".join(content) - - except Exception as e: - logger.error(f"Failed to extract DOCX content: {e}") - return None - - return await asyncio.get_event_loop().run_in_executor(None, _extract) - - -async def get_docx_info(file_path): - """Get DOCX metadata and content""" - - def _get_info(): - try: - doc = Document(file_path) - - # Extract core properties if available - core_props = { - "author": doc.core_properties.author, - "created": doc.core_properties.created, - "modified": doc.core_properties.modified, - "title": doc.core_properties.title, - "subject": doc.core_properties.subject, - "keywords": doc.core_properties.keywords, - "category": doc.core_properties.category, - "comments": doc.core_properties.comments, - } - - # Get document content - content = extract_docx_content_detailed(file_path) - - # Get document statistics - stats = { - "paragraph_count": len(doc.paragraphs), - "word_count": sum( - len(p.text.split()) for p in doc.paragraphs if p.text.strip() - ), - "character_count": sum( - len(p.text) for p in doc.paragraphs if p.text.strip() - ), - } - - return {"metadata": core_props, "content": content, "statistics": stats} - - except Exception as e: - logger.error(f"Failed to get DOCX info: {e}") - return None - - return await asyncio.get_event_loop().run_in_executor(None, _get_info) - - -async def extract_pptx_content(file_path): - """Extract content from PPTX file""" - - def _extract(): - try: - prs = Presentation(file_path) - content = [] - - for slide_number, slide in enumerate(prs.slides, 1): - content.append(f"\n# Slide {slide_number}\n") - - # Extract title - if slide.shapes.title: - content.append(f"## {slide.shapes.title.text}\n") - - # Extract text from all shapes - for shape in slide.shapes: - if hasattr(shape, "text") and shape.text.strip(): - if ( - shape != slide.shapes.title - ): # Skip title as it's already added - content.append(shape.text.strip()) - - return "\n\n".join(content) - - except Exception as e: - logger.error(f"Failed to extract PPTX content: {e}") - return None - - return await asyncio.get_event_loop().run_in_executor(None, _extract) - - -async def extract_xlsx_content(file_path, max_rows=10000, max_cols=100): - """Extract content from XLSX file""" - - def _extract(): - try: - wb = load_workbook(file_path, data_only=True) - content = [] - - for sheet in wb.sheetnames: - ws = wb[sheet] - content.append(f"\n# Sheet: {sheet}\n") - - # Get the maximum row and column with data - max_row = min(ws.max_row, max_rows) - max_col = min(ws.max_column, max_cols) - - # Create markdown table header - headers = [] - for col in range(1, max_col + 1): - cell_value = ws.cell(row=1, column=col).value - headers.append(str(cell_value) if cell_value is not None else "") - - content.append("| " + " | ".join(headers) + " |") - content.append("| " + " | ".join(["---"] * len(headers)) + " |") - - # Add table content - for row in range(2, max_row + 1): - row_data = [] - for col in range(1, max_col + 1): - cell_value = ws.cell(row=row, column=col).value - row_data.append( - str(cell_value) if cell_value is not None else "" - ) - content.append("| " + " | ".join(row_data) + " |") - - return "\n".join(content) - - except Exception as e: - logger.error(f"Failed to extract XLSX content: {e}") - return None - - return await asyncio.get_event_loop().run_in_executor(None, partial(_extract)) - - -async def get_pptx_info(file_path): - """Get PPTX metadata and content""" - - def _get_info(): - try: - prs = Presentation(file_path) - - # Extract basic properties - props = { - "slide_count": len(prs.slides), - "title": "", # PowerPoint doesn't have built-in metadata like Word - } - - # Get document content - content = extract_pptx_content(file_path) - - # Get presentation statistics - stats = { - "slide_count": len(prs.slides), - "shape_count": sum(len(slide.shapes) for slide in prs.slides), - "text_frame_count": sum( - sum(1 for shape in slide.shapes if hasattr(shape, "text")) - for slide in prs.slides - ), - } - - return {"metadata": props, "content": content, "statistics": stats} - - except Exception as e: - logger.error(f"Failed to get PPTX info: {e}") - return None - - return await asyncio.get_event_loop().run_in_executor(None, _get_info) - - -async def get_xlsx_info(file_path): - """Get XLSX metadata and content""" - - def _get_info(): - try: - wb = load_workbook(file_path, data_only=True) - - # Extract basic properties - props = { - "sheet_count": len(wb.sheetnames), - "sheets": wb.sheetnames, - "title": wb.properties.title, - "creator": wb.properties.creator, - "created": wb.properties.created, - "modified": wb.properties.modified, - } - - # Get document content - content = extract_xlsx_content(file_path) - - # Get workbook statistics - stats = { - "sheet_count": len(wb.sheetnames), - "total_rows": sum(sheet.max_row for sheet in wb.worksheets), - "total_columns": sum(sheet.max_column for sheet in wb.worksheets), - } - - return {"metadata": props, "content": content, "statistics": stats} - - except Exception as e: - logger.error(f"Failed to get XLSX info: {e}") - return None - - return await asyncio.get_event_loop().run_in_executor(None, _get_info) - - -async def extract_office_content(state: ContentState): - """Universal function to extract content from Office files""" - assert state.get("file_path"), "No file path provided" - assert ( - state.get("identified_type") in SUPPORTED_OFFICE_TYPES - ), "Unsupported File Type" - file_path = state["file_path"] - doc_type = state["identified_type"] - - if ( - doc_type - == "application/vnd.openxmlformats-officedocument.wordprocessingml.document" - ): - logger.debug("Extracting content from DOCX file") - content = await extract_docx_content_detailed(file_path) - info = await get_docx_info(file_path) - elif ( - doc_type - == "application/vnd.openxmlformats-officedocument.presentationml.presentation" - ): - logger.debug("Extracting content from PPTX file") - content = await extract_pptx_content(file_path) - info = await get_pptx_info(file_path) - elif ( - doc_type == "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" - ): - logger.debug("Extracting content from XLSX file") - content = await extract_xlsx_content(file_path) - info = await get_xlsx_info(file_path) - else: - raise Exception(f"Unsupported file format: {doc_type}") - - del info["content"] - return {"content": content, "metadata": info} diff --git a/open_notebook/graphs/content_processing/pdf.py b/open_notebook/graphs/content_processing/pdf.py deleted file mode 100644 index a4d4209..0000000 --- a/open_notebook/graphs/content_processing/pdf.py +++ /dev/null @@ -1,170 +0,0 @@ -import asyncio -import re -import unicodedata - -import fitz # type: ignore -from loguru import logger - -from open_notebook.graphs.content_processing.state import ContentState - -# todo: find tables - https://pymupdf.readthedocs.io/en/latest/the-basics.html#extracting-tables-from-a-page -# todo: what else can we do to make the text more readable? -# todo: try to fix encoding for some PDF that is still breaking -# def _extract_text_from_pdf(pdf_path): -# doc = fitz.open(pdf_path) -# text = "" -# logger.debug(f"Found {len(doc)} pages in PDF") -# for page in doc: -# # Use encode/decode if you need to clean up any encoding issues -# text += page.get_text().encode('utf-8').decode('utf-8') -# doc.close() -# return text - -SUPPORTED_FITZ_TYPES = [ - "application/pdf", - "application/epub+zip", -] - - -def clean_pdf_text(text): - """ - Clean text extracted from PDFs with enhanced space handling. - Preserves special characters like (, ), %, = that are valid in code/math. - - Args: - text (str): The raw text extracted from a PDF - Returns: - str: Cleaned text with minimal necessary spacing - """ - if not text: - return text - - # Step 1: Normalize Unicode characters - text = unicodedata.normalize("NFKC", text) - - # Step 2: Replace common PDF artifacts - replacements = { - # Common ligatures - "fi": "fi", - "fl": "fl", - "ff": "ff", - "ffi": "ffi", - "ffl": "ffl", - # Quotation marks and apostrophes - """: "'", """: "'", - '"': '"', - "′": "'", - "‚": ",", - "„": '"', - # Dashes and hyphens - "‒": "-", - "–": "-", - "—": "-", - "―": "-", - # Other common replacements - "…": "...", - "•": "*", - "°": " degrees ", - "¹": "1", - "²": "2", - "³": "3", - "©": "(c)", - "®": "(R)", - "™": "(TM)", - } - for old, new in replacements.items(): - text = text.replace(old, new) - - # Step 3: Clean control characters while preserving essential whitespace and special chars - text = "".join( - char - for char in text - if unicodedata.category(char)[0] != "C" - or char in "\n\t " - or char in "()%=[]{}#$@!?.,;:+-*/^<>&|~" - ) - - # Step 4: Enhanced space cleaning - text = re.sub(r"[ \t]+", " ", text) # Consolidate horizontal whitespace - text = re.sub(r" +\n", "\n", text) # Remove spaces before newlines - text = re.sub(r"\n +", "\n", text) # Remove spaces after newlines - text = re.sub(r"\n\t+", "\n", text) # Remove tabs at start of lines - text = re.sub(r"\t+\n", "\n", text) # Remove tabs at end of lines - text = re.sub(r"\t+", " ", text) # Replace tabs with single space - - # Step 5: Remove empty lines while preserving paragraph structure - text = re.sub(r"\n{3,}", "\n\n", text) # Max two consecutive newlines - text = re.sub(r"^\s+", "", text) # Remove leading whitespace - text = re.sub(r"\s+$", "", text) # Remove trailing whitespace - - # Step 6: Clean up around punctuation - text = re.sub(r"\s+([.,;:!?)])", r"\1", text) # Remove spaces before punctuation - text = re.sub(r"(\()\s+", r"\1", text) # Remove spaces after opening parenthesis - text = re.sub( - r"\s+([.,])\s+", r"\1 ", text - ) # Ensure single space after periods and commas - - # Step 7: Remove zero-width and invisible characters - text = re.sub(r"[\u200b\u200c\u200d\ufeff\u200e\u200f]", "", text) - - # Step 8: Fix hyphenation and line breaks - text = re.sub( - r"(?<=\w)-\s*\n\s*(?=\w)", "", text - ) # Remove hyphenation at line breaks - - return text.strip() - - -async def _extract_text_from_pdf(pdf_path): - doc = fitz.open(pdf_path) - try: - text = "" - logger.debug(f"Found {len(doc)} pages in PDF") - for page in doc: - text += page.get_text() - normalized_text = clean_pdf_text(text) - return normalized_text - finally: - doc.close() - - -async def _extract_text_from_pdf(pdf_path): - """Extract text from PDF asynchronously""" - - def _extract(): - doc = fitz.open(pdf_path) - try: - text = "" - logger.debug(f"Found {len(doc)} pages in PDF") - for page in doc: - text += page.get_text() - return clean_pdf_text(text) - finally: - doc.close() - - # Run CPU-bound PDF processing in a thread pool - return await asyncio.get_event_loop().run_in_executor(None, _extract) - - -async def extract_pdf(state: ContentState): - """ - Parse the PDF file and extract its content asynchronously. - """ - return_dict = {} - assert state.get("file_path"), "No file path provided" - assert state.get("identified_type") in SUPPORTED_FITZ_TYPES, "Unsupported File Type" - - if ( - state.get("file_path") is not None - and state.get("identified_type") in SUPPORTED_FITZ_TYPES - ): - file_path = state.get("file_path") - try: - text = await _extract_text_from_pdf(file_path) - return_dict["content"] = text - except FileNotFoundError: - raise FileNotFoundError(f"File not found at {file_path}") - except Exception as e: - raise Exception(f"An error occurred: {e}") - - return return_dict diff --git a/open_notebook/graphs/content_processing/state.py b/open_notebook/graphs/content_processing/state.py deleted file mode 100644 index 586ee45..0000000 --- a/open_notebook/graphs/content_processing/state.py +++ /dev/null @@ -1,13 +0,0 @@ -from typing_extensions import TypedDict - - -class ContentState(TypedDict): - content: str - file_path: str - url: str - title: str - source_type: str - identified_type: str - identified_provider: str - metadata: dict - delete_source: bool = False diff --git a/open_notebook/graphs/content_processing/text.py b/open_notebook/graphs/content_processing/text.py deleted file mode 100644 index 85c11aa..0000000 --- a/open_notebook/graphs/content_processing/text.py +++ /dev/null @@ -1,40 +0,0 @@ -import asyncio - -from loguru import logger - -from open_notebook.graphs.content_processing.state import ContentState - - -async def extract_txt(state: ContentState): - """ - Parse the text file and extract its content asynchronously. - """ - return_dict = {} - if ( - state.get("file_path") is not None - and state.get("identified_type") == "text/plain" - ): - logger.debug(f"Extracting text from {state.get('file_path')}") - file_path = state.get("file_path") - - if file_path is not None: - try: - - def _read_file(): - with open(file_path, "r", encoding="utf-8") as file: - return file.read() - - # Run file I/O in thread pool - content = await asyncio.get_event_loop().run_in_executor( - None, _read_file - ) - - logger.debug(f"Extracted: {content[:100]}") - return_dict["content"] = content - - except FileNotFoundError: - raise FileNotFoundError(f"File not found at {file_path}") - except Exception as e: - raise Exception(f"An error occurred: {e}") - - return return_dict diff --git a/open_notebook/graphs/content_processing/url.py b/open_notebook/graphs/content_processing/url.py deleted file mode 100644 index 66bb62e..0000000 --- a/open_notebook/graphs/content_processing/url.py +++ /dev/null @@ -1,191 +0,0 @@ -import re -from urllib.parse import urlparse - -import aiohttp -from bs4 import BeautifulSoup, Comment -from loguru import logger - -from open_notebook.graphs.content_processing.state import ContentState - -# future: better extraction methods -# https://github.com/buriy/python-readability -# also try readability: from readability import Document - - -def url_provider(state: ContentState): - """ - Identify the provider - """ - return_dict = {} - url = state.get("url") - if url: - if "youtube.com" in url or "youtu.be" in url: - return_dict["identified_type"] = ( - "youtube" # future: playlists, channels in the future - ) - else: - return_dict["identified_type"] = "article" - # future: article providers in the future - return return_dict - - -async def extract_url_bs4(url: str): - """ - Get the title and content of a URL using bs4 - """ - try: - headers = { - "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36" - } - - # If URL is actually HTML content - if url.startswith("") or url.startswith("") - else None, - "url": url if not url.startswith("") else None, - } - - except aiohttp.ClientError as e: - logger.error(f"Failed to fetch URL {url}: {e}") - return None - except Exception as e: - logger.error(f"Failed to process content: {e}") - return None - - -async def extract_url_jina(url: str): - """ - Get the content of a URL using Jina - """ - async with aiohttp.ClientSession() as session: - async with session.get(f"https://r.jina.ai/{url}") as response: - text = await response.text() - if text.startswith("Title:") and "\n" in text: - title_end = text.index("\n") - title = text[6:title_end].strip() - content = text[title_end + 1 :].strip() - logger.debug( - f"Processed url: {url}, found title: {title}, content: {content[:100]}..." - ) - return {"title": title, "content": content} - else: - logger.debug( - f"Processed url: {url}, does not have Title prefix, returning full content: {text[:100]}..." - ) - return {"content": text} - - -async def extract_url(state: ContentState): - assert state.get("url"), "No URL provided" - url = state["url"] - try: - result = await extract_url_bs4(url) - if not result or not result.get("content"): - logger.debug( - f"BS4 extraction failed for url {url}, falling back to Jina extractor" - ) - result = await extract_url_jina(url) - return result - except Exception as e: - logger.error(f"URL extraction failed for URL: {url}") - logger.exception(e) - return None diff --git a/open_notebook/graphs/content_processing/video.py b/open_notebook/graphs/content_processing/video.py deleted file mode 100644 index 9fc5018..0000000 --- a/open_notebook/graphs/content_processing/video.py +++ /dev/null @@ -1,167 +0,0 @@ -import asyncio -import json -import os -import subprocess -from functools import partial - -from loguru import logger - -from open_notebook.graphs.content_processing.state import ContentState - - -async def extract_audio_from_video(input_file, output_file, stream_index): - """ - Extract the specified audio stream to MP3 format asynchronously - """ - - def _extract(input_file, output_file, stream_index): - try: - cmd = [ - "ffmpeg", - "-i", - input_file, - "-map", - f"0:a:{stream_index}", # Select specific audio stream - "-codec:a", - "libmp3lame", # Use MP3 codec - "-q:a", - "2", # High quality setting - "-y", # Overwrite output file if exists - output_file, - ] - - result = subprocess.run(cmd, capture_output=True, text=True) - if result.returncode != 0: - raise Exception(f"FFmpeg failed: {result.stderr}") - - return True - - except Exception as e: - logger.error(f"Error extracting audio: {str(e)}") - return False - - return await asyncio.get_event_loop().run_in_executor( - None, partial(_extract, input_file, output_file, stream_index) - ) - - -async def get_audio_streams(input_file): - """ - Analyze video file and return information about all audio streams asynchronously - """ - - def _analyze(input_file): - logger.debug(f"Analyzing video file {input_file} for audio streams") - try: - cmd = [ - "ffprobe", - "-v", - "quiet", - "-print_format", - "json", - "-show_streams", - "-select_streams", - "a", - input_file, - ] - - result = subprocess.run(cmd, capture_output=True, text=True) - if result.returncode != 0: - raise Exception(f"FFprobe failed: {result.stderr}") - - data = json.loads(result.stdout) - return data.get("streams", []) - - except Exception as e: - logger.error(f"Error analyzing file: {str(e)}") - return [] - - return await asyncio.get_event_loop().run_in_executor( - None, partial(_analyze, input_file) - ) - - -async def select_best_audio_stream(streams): - """ - Select the best audio stream based on various quality metrics - """ - - def _select(streams): - if not streams: - logger.debug("No audio streams found") - return None - else: - logger.debug(f"Found {len(streams)} audio streams") - - # Score each stream based on various factors - scored_streams = [] - for stream in streams: - score = 0 - - # Prefer higher bit rates - bit_rate = stream.get("bit_rate") - if bit_rate: - score += int(int(bit_rate) / 1000000) # Convert to Mbps and ensure int - - # Prefer more channels (stereo over mono) - channels = stream.get("channels", 0) - score += channels * 10 - - # Prefer higher sample rates - sample_rate = stream.get("sample_rate", "0") - score += int(int(sample_rate) / 48000) - - scored_streams.append((score, stream)) - - # Return the stream with highest score - return max(scored_streams, key=lambda x: x[0])[1] - - return await asyncio.get_event_loop().run_in_executor( - None, partial(_select, streams) - ) - - -async def extract_best_audio_from_video(data: ContentState): - """ - Main function to extract the best audio stream from a video file asynchronously - """ - input_file = data.get("file_path") - assert input_file is not None, "Input file path must be provided" - - def _check_file(path): - return os.path.exists(path) - - file_exists = await asyncio.get_event_loop().run_in_executor( - None, partial(_check_file, input_file) - ) - - if not file_exists: - logger.critical(f"Input file not found: {input_file}") - return False - - base_name = os.path.splitext(input_file)[0] - output_file = f"{base_name}_audio.mp3" - - # Get all audio streams - streams = await get_audio_streams(input_file) - if not streams: - logger.debug("No audio streams found in the file") - return False - - # Select best stream - best_stream = await select_best_audio_stream(streams) - if not best_stream: - logger.error("Could not determine best audio stream") - return False - - # Extract the selected stream - stream_index = streams.index(best_stream) - success = await extract_audio_from_video(input_file, output_file, stream_index) - - if success: - logger.debug(f"Successfully extracted audio to: {output_file}") - logger.debug(f"- Channels: {best_stream.get('channels', 'unknown')}") - logger.debug(f"- Sample rate: {best_stream.get('sample_rate', 'unknown')} Hz") - logger.debug(f"- Bit rate: {best_stream.get('bit_rate', 'unknown')} bits/s") - - return {"file_path": output_file, "identified_type": "audio/mp3"} diff --git a/open_notebook/graphs/content_processing/youtube.py b/open_notebook/graphs/content_processing/youtube.py deleted file mode 100644 index e39f4ba..0000000 --- a/open_notebook/graphs/content_processing/youtube.py +++ /dev/null @@ -1,159 +0,0 @@ -import re -import ssl - -import aiohttp -from bs4 import BeautifulSoup -from loguru import logger -from youtube_transcript_api import YouTubeTranscriptApi # type: ignore -from youtube_transcript_api.formatters import TextFormatter # type: ignore - -from open_notebook.config import CONFIG -from open_notebook.exceptions import NoTranscriptFound -from open_notebook.graphs.content_processing.state import ContentState - -ssl._create_default_https_context = ssl._create_unverified_context - - -async def get_video_title(video_id): - try: - url = f"https://www.youtube.com/watch?v={video_id}" - async with aiohttp.ClientSession() as session: - async with session.get(url) as response: - html = await response.text() - - # BeautifulSoup doesn't support async operations - soup = BeautifulSoup(html, "html.parser") - - # YouTube stores title in a meta tag - title = soup.find("meta", property="og:title")["content"] - return title - - except Exception as e: - logger.error(f"Failed to get video title: {e}") - return None - - -def _extract_youtube_id(url): - """ - Extract the YouTube video ID from a given URL using regular expressions. - - Args: - url (str): The YouTube URL from which to extract the video ID. - - Returns: - str: The extracted YouTube video ID or None if no valid ID is found. - """ - # Define a regular expression pattern to capture the YouTube video ID - youtube_regex = ( - r"(?:https?://)?" # Optional scheme - r"(?:www\.)?" # Optional www. - r"(?:" - r"youtu\.be/" # Shortened URL - r"|youtube\.com" # Main URL - r"(?:" # Group start - r"/embed/" # Embed URL - r"|/v/" # Older video URL - r"|/watch\?v=" # Standard watch URL - r"|/watch\?.+&v=" # Other watch URL - r")" # Group end - r")" # End main group - r"([\w-]{11})" # 11 characters (YouTube video ID) - ) - - # Search the URL for the pattern - match = re.search(youtube_regex, url) - - # Return the video ID if a match is found - return match.group(1) if match else None - - -async def get_best_transcript(video_id, preferred_langs=["en", "es", "pt"]): - try: - transcript_list = YouTubeTranscriptApi.list_transcripts(video_id) - - # First try: Manual transcripts in preferred languages - manual_transcripts = [] - try: - for transcript in transcript_list: - if not transcript.is_generated and not transcript.is_translatable: - manual_transcripts.append(transcript) - - if manual_transcripts: - # Sort based on preferred language order - for lang in preferred_langs: - for transcript in manual_transcripts: - if transcript.language_code == lang: - return transcript.fetch() - # If no preferred language found, return first manual transcript - return manual_transcripts[0].fetch() - except NoTranscriptFound: - pass - - # Second try: Auto-generated transcripts in preferred languages - generated_transcripts = [] - try: - for transcript in transcript_list: - if transcript.is_generated and not transcript.is_translatable: - generated_transcripts.append(transcript) - - if generated_transcripts: - # Sort based on preferred language order - for lang in preferred_langs: - for transcript in generated_transcripts: - if transcript.language_code == lang: - return transcript.fetch() - # If no preferred language found, return first generated transcript - return generated_transcripts[0].fetch() - except NoTranscriptFound: - pass - - # Last try: Translated transcripts in preferred languages - translated_transcripts = [] - try: - for transcript in transcript_list: - if transcript.is_translatable: - translated_transcripts.append(transcript) - - if translated_transcripts: - # Sort based on preferred language order - for lang in preferred_langs: - for transcript in translated_transcripts: - if transcript.language_code == lang: - return transcript.fetch() - # If no preferred language found, return translation to first preferred language - translation = translated_transcripts[0].translate(preferred_langs[0]) - return translation.fetch() - except NoTranscriptFound: - pass - - raise Exception("No suitable transcript found") - - except Exception as e: - logger.error(f"Failed to get transcript for video {video_id}: {e}") - return None - - -async def extract_youtube_transcript(state: ContentState): - """ - Parse the text file and print its content. - """ - - languages = CONFIG.get("youtube_transcripts", {}).get( - "preferred_languages", ["en", "es", "pt"] - ) - - video_id = _extract_youtube_id(state.get("url")) - transcript = await get_best_transcript(video_id, languages) - - logger.debug(f"Found transcript: {transcript}") - formatter = TextFormatter() - try: - title = await get_video_title(video_id) - except Exception as e: - logger.critical(f"Failed to get video title for video_id: {video_id}") - logger.exception(e) - title = None - return { - "content": formatter.format_transcript(transcript), - "title": title, - } diff --git a/pyproject.toml b/pyproject.toml index f9223c1..ce90a2f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,6 @@ classifiers = [ requires-python = ">=3.11,<3.13" dependencies = [ "streamlit>=1.39.0", - "watchdog>=5.0.3", "pydantic>=2.9.2", "loguru>=0.7.2", "langchain>=0.3.3", @@ -25,31 +24,23 @@ dependencies = [ "tiktoken>=0.8.0", "streamlit-monaco>=0.1.3", "langgraph-checkpoint-sqlite>=2.0.0", - "pymupdf==1.24.11", - "python-magic>=0.4.27", - "langdetect>=1.0.9", - "youtube-transcript-api>=0.6.2", "openai>=1.52.0", - "pre-commit>=4.0.1", "langchain-community>=0.3.3", - "litellm>=1.50.1", "langchain-openai>=0.2.3", "langchain-anthropic>=0.2.3", "langchain-ollama>=0.2.0", "langchain-google-vertexai>=2.0.5", "langchain-google-genai>=2.0.1", - "podcastfy>=0.4", "tomli>=2.0.2", - "bs4>=0.0.2", - "python-docx>=1.1.2", - "python-pptx>=1.0.2", - "openpyxl>=3.1.5", "google-generativeai>=0.8.3", "langchain-groq>=0.2.1", "groq>=0.12.0", 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