""" main.py """ # Standard library imports import glob import os import time from pathlib import Path from tempfile import NamedTemporaryFile from typing import List, Literal, Tuple, Optional # Third-party imports import gradio as gr from loguru import logger from pydantic import BaseModel, Field from pypdf import PdfReader from pydub import AudioSegment # Local imports from prompts import SYSTEM_PROMPT from utils import generate_script, generate_podcast_audio, parse_url LANGUAGE_MAPPING = { "English": "en", "Chinese": "zh", "French": "fr", "German": "de", "Hindi": "hi", "Italian": "it", "Japanese": "ja", "Korean": "ko", "Polish": "pl", "Portuguese": "pt", "Russian": "ru", "Spanish": "es", "Turkish": "tr" } class DialogueItem(BaseModel): """A single dialogue item.""" speaker: Literal["Host (Jane)", "Guest"] text: str class ShortDialogue(BaseModel): """The dialogue between the host and guest.""" scratchpad: str name_of_guest: str dialogue: List[DialogueItem] = Field(..., description="A list of dialogue items, typically between 5 to 9 items") class MediumDialogue(BaseModel): """The dialogue between the host and guest.""" scratchpad: str name_of_guest: str dialogue: List[DialogueItem] = Field(..., description="A list of dialogue items, typically between 8 to 13 items") def generate_podcast( files: List[str], url: Optional[str], question: Optional[str], tone: Optional[str], length: Optional[str], language: str, ) -> Tuple[str, str]: """Generate the audio and transcript from the PDFs and/or URL.""" text = "" # Change language to the appropriate code language_mapping = { "English": "EN", "Spanish": "ES", "French": "FR", "Chinese": "ZH", "Japanese": "JP", "Korean": "KR", } # Check if at least one input is provided if not files and not url: raise gr.Error("Please provide at least one PDF file or a URL.") # Process PDFs if any if files: for file in files: if not file.lower().endswith(".pdf"): raise gr.Error( f"File {file} is not a PDF. Please upload only PDF files." ) try: with Path(file).open("rb") as f: reader = PdfReader(f) text += "\n\n".join([page.extract_text() for page in reader.pages]) except Exception as e: raise gr.Error(f"Error reading the PDF file {file}: {str(e)}") # Process URL if provided if url: try: url_text = parse_url(url) text += "\n\n" + url_text except ValueError as e: raise gr.Error(str(e)) # Check total character count if len(text) > 100000: raise gr.Error( "The total content is too long. Please ensure the combined text from PDFs and URL is fewer than ~100,000 characters." ) # Modify the system prompt based on the user input modified_system_prompt = SYSTEM_PROMPT if question: modified_system_prompt += f"\n\PLEASE ANSWER THE FOLLOWING QN: {question}" if tone: modified_system_prompt += f"\n\nTONE: The tone of the podcast should be {tone}." if length: length_instructions = { "Short (1-2 min)": "Keep the podcast brief, around 1-2 minutes long.", "Medium (3-5 min)": "Aim for a moderate length, about 3-5 minutes.", } modified_system_prompt += f"\n\nLENGTH: {length_instructions[length]}" if language: modified_system_prompt += ( f"\n\nOUTPUT LANGUAGE : The the podcast should be {language}." ) # Call the LLM if length == "Short (1-2 min)": llm_output = generate_script(modified_system_prompt, text, ShortDialogue) else: llm_output = generate_script(modified_system_prompt, text, MediumDialogue) logger.info(f"Generated dialogue: {llm_output}") # Process the dialogue audio_segments = [] transcript = "" total_characters = 0 for line in llm_output.dialogue: logger.info(f"Generating audio for {line.speaker}: {line.text}") if line.speaker == "Host (Jane)": speaker = f"**Jane**: {line.text}" else: speaker = f"**{llm_output.name_of_guest}**: {line.text}" transcript += speaker + "\n\n" total_characters += len(line.text) # Get audio file path audio_file_path = generate_podcast_audio( line.text, line.speaker, LANGUAGE_MAPPING[language] ) # Read the audio file into an AudioSegment audio_segment = AudioSegment.from_file(audio_file_path) audio_segments.append(audio_segment) # Concatenate all audio segments combined_audio = sum(audio_segments) # Export the combined audio to a temporary file temporary_directory = "./gradio_cached_examples/tmp/" os.makedirs(temporary_directory, exist_ok=True) temporary_file = NamedTemporaryFile( dir=temporary_directory, delete=False, suffix=".mp3", ) combined_audio.export(temporary_file.name, format="mp3") # Delete any files in the temp directory that end with .mp3 and are over a day old for file in glob.glob(f"{temporary_directory}*.mp3"): if os.path.isfile(file) and time.time() - os.path.getmtime(file) > 24 * 60 * 60: os.remove(file) logger.info(f"Generated {total_characters} characters of audio") return temporary_file.name, transcript demo = gr.Interface( title="Open NotebookLM", description="""
Open NotebookLM

Convert your PDFs into podcasts with open-source AI models (Llama 3.1 405B and MeloTTS).

Note: Only the text content of the PDFs will be processed. Images and tables are not included. The total content should be no more than 100,000 characters due to the context length of Llama 3.1 405B.
""", fn=generate_podcast, inputs=[ gr.File( label="1. 📄 Upload your PDF(s)", file_types=[".pdf"], file_count="multiple" ), gr.Textbox( label="2. 🔗 Paste a URL (optional)", placeholder="Enter a URL to include its content", ), gr.Textbox(label="3. 🤔 Do you have a specific question or topic in mind?"), gr.Dropdown( choices=["Fun", "Formal"], label="4. 🎭 Choose the tone", value="Fun" ), gr.Dropdown( choices=["Short (1-2 min)", "Medium (3-5 min)"], label="5. ⏱️ Choose the length", value="Medium (3-5 min)" ), gr.Dropdown( choices=list(LANGUAGE_MAPPING.keys()), value="English", label="6. 🌐 Choose the language" ), ], outputs=[ gr.Audio(label="Podcast", format="mp3"), gr.Markdown(label="Transcript"), ], allow_flagging="never", api_name="generate_podcast", theme=gr.themes.Soft(), concurrency_limit=3, examples=[ [ [str(Path("examples/1310.4546v1.pdf"))], "", "Explain this paper to me like I'm 5 years old", "Fun", "Short (1-2 min)", "English", ], # [ # [], # "https://en.wikipedia.org/wiki/Hugging_Face", # "How did Hugging Face become so successful?", # "Fun", # "Short (1-2 min)", # "English", # ], # [ # [], # "https://simple.wikipedia.org/wiki/Taylor_Swift", # "Why is Taylor Swift so popular?", # "Fun", # "Short (1-2 min)", # "English", # ], ], cache_examples=True, ) if __name__ == "__main__": demo.launch(show_api=True)