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""" | |
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 | |
# Third-party imports | |
import gradio as gr | |
from fastapi import FastAPI | |
from fastapi.staticfiles import StaticFiles | |
from loguru import logger | |
from pydantic import BaseModel | |
from pypdf import PdfReader | |
from pydub import AudioSegment | |
# Local imports | |
from prompts import SYSTEM_PROMPT | |
from utils import generate_script, generate_audio | |
app = FastAPI() | |
app.mount("/static", StaticFiles(directory="static"), name="static") | |
class DialogueItem(BaseModel): | |
"""A single dialogue item.""" | |
speaker: Literal["Host (Jane)", "Guest"] | |
text: str | |
class Dialogue(BaseModel): | |
"""The dialogue between the host and guest.""" | |
scratchpad: str | |
participants: List[str] | |
dialogue: List[DialogueItem] | |
def generate_podcast(file: str) -> Tuple[str, str]: | |
"""Generate the audio and transcript from the PDF.""" | |
# Read the PDF file and extract text | |
with Path(file).open("rb") as f: | |
reader = PdfReader(f) | |
text = "\n\n".join([page.extract_text() for page in reader.pages]) | |
# Call the LLM | |
llm_output = generate_script(SYSTEM_PROMPT, text, Dialogue) | |
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}") | |
transcript_line = f"{line.speaker}: {line.text}" | |
transcript += transcript_line + "\n\n" | |
total_characters += len(line.text) | |
# Get audio file path | |
audio_file_path = generate_audio(line.text, line.speaker) | |
# 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="OpenPodcast", | |
description="Convert your PDFs into podcasts with open-source AI models.", | |
fn=generate_podcast, | |
inputs=[ | |
gr.File( | |
label="PDF", | |
), | |
], | |
outputs=[ | |
gr.Audio(label="Audio", format="mp3"), | |
gr.Textbox(label="Transcript"), | |
], | |
allow_flagging="never", | |
api_name=False, | |
) | |
app = gr.mount_gradio_app(app, demo, path="/") | |
if __name__ == "__main__": | |
demo.launch(show_api=False) | |