print("starting...") import argparse # Argument parser to handle optional parameters parser = argparse.ArgumentParser(description="Launch the Gradio app with optional share parameter.") parser.add_argument("--share", type=bool, default=False, help="Set to True to enable Gradio share link.") args = parser.parse_args() import os import shutil import subprocess import re from pydub import AudioSegment import tempfile from pydub import AudioSegment import os import nltk from nltk.tokenize import sent_tokenize import sys import torch from TTS.api import TTS from TTS.tts.configs.xtts_config import XttsConfig from TTS.tts.models.xtts import Xtts from tqdm import tqdm import gradio as gr from gradio import Progress import urllib.request import zipfile import socket #import MeCab #import unidic #nltk.download('punkt_tab') # Pre-download the xtts TOS agreed file #import download_tos_agreed_file # Import the locally stored Xtts default model #import import_locally_stored_tts_model_files # import all files import import_all_files # Download UniDic if it's not already installed #unidic.download() device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Device selected is: {device}") #nltk.download('punkt') # Make sure to download the necessary models def download_and_extract_zip(url, extract_to='.'): try: # Ensure the directory exists os.makedirs(extract_to, exist_ok=True) zip_path = os.path.join(extract_to, 'model.zip') # Download with progress bar with tqdm(unit='B', unit_scale=True, miniters=1, desc="Downloading Model") as t: def reporthook(blocknum, blocksize, totalsize): t.total = totalsize t.update(blocknum * blocksize - t.n) urllib.request.urlretrieve(url, zip_path, reporthook=reporthook) print(f"Downloaded zip file to {zip_path}") # Unzipping with progress bar with zipfile.ZipFile(zip_path, 'r') as zip_ref: files = zip_ref.namelist() with tqdm(total=len(files), unit="file", desc="Extracting Files") as t: for file in files: if not file.endswith('/'): # Skip directories # Extract the file to the temporary directory extracted_path = zip_ref.extract(file, extract_to) # Move the file to the base directory base_file_path = os.path.join(extract_to, os.path.basename(file)) os.rename(extracted_path, base_file_path) t.update(1) # Cleanup: Remove the ZIP file and any empty folders os.remove(zip_path) for root, dirs, files in os.walk(extract_to, topdown=False): for name in dirs: os.rmdir(os.path.join(root, name)) print(f"Extracted files to {extract_to}") # Check if all required files are present required_files = ['model.pth', 'config.json', 'vocab.json_'] missing_files = [file for file in required_files if not os.path.exists(os.path.join(extract_to, file))] if not missing_files: print("All required files (model.pth, config.json, vocab.json_) found.") else: print(f"Missing files: {', '.join(missing_files)}") except Exception as e: print(f"Failed to download or extract zip file: {e}") def is_folder_empty(folder_path): if os.path.exists(folder_path) and os.path.isdir(folder_path): # List directory contents if not os.listdir(folder_path): return True # The folder is empty else: return False # The folder is not empty else: print(f"The path {folder_path} is not a valid folder.") return None # The path is not a valid folder def remove_folder_with_contents(folder_path): try: shutil.rmtree(folder_path) print(f"Successfully removed {folder_path} and all of its contents.") except Exception as e: print(f"Error removing {folder_path}: {e}") def wipe_folder(folder_path): # Check if the folder exists if not os.path.exists(folder_path): print(f"The folder {folder_path} does not exist.") return # Iterate over all the items in the given folder for item in os.listdir(folder_path): item_path = os.path.join(folder_path, item) # If it's a file, remove it and print a message if os.path.isfile(item_path): os.remove(item_path) print(f"Removed file: {item_path}") # If it's a directory, remove it recursively and print a message elif os.path.isdir(item_path): shutil.rmtree(item_path) print(f"Removed directory and its contents: {item_path}") print(f"All contents wiped from {folder_path}.") # Example usage # folder_to_wipe = 'path_to_your_folder' # wipe_folder(folder_to_wipe) def create_m4b_from_chapters(input_dir, ebook_file, output_dir): # Function to sort chapters based on their numeric order def sort_key(chapter_file): numbers = re.findall(r'\d+', chapter_file) return int(numbers[0]) if numbers else 0 # Extract metadata and cover image from the eBook file def extract_metadata_and_cover(ebook_path): try: cover_path = ebook_path.rsplit('.', 1)[0] + '.jpg' subprocess.run(['ebook-meta', ebook_path, '--get-cover', cover_path], check=True) if os.path.exists(cover_path): return cover_path except Exception as e: print(f"Error extracting eBook metadata or cover: {e}") return None # Combine WAV files into a single file def combine_wav_files(chapter_files, output_path): # Initialize an empty audio segment combined_audio = AudioSegment.empty() # Sequentially append each file to the combined_audio for chapter_file in chapter_files: audio_segment = AudioSegment.from_wav(chapter_file) combined_audio += audio_segment # Export the combined audio to the output file path combined_audio.export(output_path, format='wav') print(f"Combined audio saved to {output_path}") # Function to generate metadata for M4B chapters def generate_ffmpeg_metadata(chapter_files, metadata_file): with open(metadata_file, 'w') as file: file.write(';FFMETADATA1\n') start_time = 0 for index, chapter_file in enumerate(chapter_files): duration_ms = len(AudioSegment.from_wav(chapter_file)) file.write(f'[CHAPTER]\nTIMEBASE=1/1000\nSTART={start_time}\n') file.write(f'END={start_time + duration_ms}\ntitle=Chapter {index + 1}\n') start_time += duration_ms # Generate the final M4B file using ffmpeg def create_m4b(combined_wav, metadata_file, cover_image, output_m4b): # Ensure the output directory exists os.makedirs(os.path.dirname(output_m4b), exist_ok=True) ffmpeg_cmd = ['ffmpeg', '-i', combined_wav, '-i', metadata_file] if cover_image: ffmpeg_cmd += ['-i', cover_image, '-map', '0:a', '-map', '2:v'] else: ffmpeg_cmd += ['-map', '0:a'] ffmpeg_cmd += ['-map_metadata', '1', '-c:a', 'aac', '-b:a', '192k'] if cover_image: ffmpeg_cmd += ['-c:v', 'png', '-disposition:v', 'attached_pic'] ffmpeg_cmd += [output_m4b] subprocess.run(ffmpeg_cmd, check=True) # Main logic chapter_files = sorted([os.path.join(input_dir, f) for f in os.listdir(input_dir) if f.endswith('.wav')], key=sort_key) temp_dir = tempfile.gettempdir() temp_combined_wav = os.path.join(temp_dir, 'combined.wav') metadata_file = os.path.join(temp_dir, 'metadata.txt') cover_image = extract_metadata_and_cover(ebook_file) output_m4b = os.path.join(output_dir, os.path.splitext(os.path.basename(ebook_file))[0] + '.m4b') combine_wav_files(chapter_files, temp_combined_wav) generate_ffmpeg_metadata(chapter_files, metadata_file) create_m4b(temp_combined_wav, metadata_file, cover_image, output_m4b) # Cleanup if os.path.exists(temp_combined_wav): os.remove(temp_combined_wav) if os.path.exists(metadata_file): os.remove(metadata_file) if cover_image and os.path.exists(cover_image): os.remove(cover_image) # Example usage # create_m4b_from_chapters('path_to_chapter_wavs', 'path_to_ebook_file', 'path_to_output_dir') #this code right here isnt the book grabbing thing but its before to refrence in ordero to create the sepecial chapter labeled book thing with calibre idk some systems cant seem to get it so just in case but the next bit of code after this is the book grabbing code with booknlp import os import subprocess import ebooklib from ebooklib import epub from bs4 import BeautifulSoup import re import csv import nltk # Only run the main script if Value is True def create_chapter_labeled_book(ebook_file_path): # Function to ensure the existence of a directory def ensure_directory(directory_path): if not os.path.exists(directory_path): os.makedirs(directory_path) print(f"Created directory: {directory_path}") ensure_directory(os.path.join(".", 'Working_files', 'Book')) def convert_to_epub(input_path, output_path): # Convert the ebook to EPUB format using Calibre's ebook-convert try: subprocess.run(['ebook-convert', input_path, output_path], check=True) except subprocess.CalledProcessError as e: print(f"An error occurred while converting the eBook: {e}") return False return True def save_chapters_as_text(epub_path): # Create the directory if it doesn't exist directory = os.path.join(".", "Working_files", "temp_ebook") ensure_directory(directory) # Open the EPUB file book = epub.read_epub(epub_path) previous_chapter_text = '' previous_filename = '' chapter_counter = 0 # Iterate through the items in the EPUB file for item in book.get_items(): if item.get_type() == ebooklib.ITEM_DOCUMENT: # Use BeautifulSoup to parse HTML content soup = BeautifulSoup(item.get_content(), 'html.parser') text = soup.get_text() # Check if the text is not empty if text.strip(): if len(text) < 2300 and previous_filename: # Append text to the previous chapter if it's short with open(previous_filename, 'a', encoding='utf-8') as file: file.write('\n' + text) else: # Create a new chapter file and increment the counter previous_filename = os.path.join(directory, f"chapter_{chapter_counter}.txt") chapter_counter += 1 with open(previous_filename, 'w', encoding='utf-8') as file: file.write(text) print(f"Saved chapter: {previous_filename}") # Example usage input_ebook = ebook_file_path # Replace with your eBook file path output_epub = os.path.join(".", "Working_files", "temp.epub") if os.path.exists(output_epub): os.remove(output_epub) print(f"File {output_epub} has been removed.") else: print(f"The file {output_epub} does not exist.") if convert_to_epub(input_ebook, output_epub): save_chapters_as_text(output_epub) # Download the necessary NLTK data (if not already present) #nltk.download('punkt') def process_chapter_files(folder_path, output_csv): with open(output_csv, 'w', newline='', encoding='utf-8') as csvfile: writer = csv.writer(csvfile) # Write the header row writer.writerow(['Text', 'Start Location', 'End Location', 'Is Quote', 'Speaker', 'Chapter']) # Process each chapter file chapter_files = sorted(os.listdir(folder_path), key=lambda x: int(x.split('_')[1].split('.')[0])) for filename in chapter_files: if filename.startswith('chapter_') and filename.endswith('.txt'): chapter_number = int(filename.split('_')[1].split('.')[0]) file_path = os.path.join(folder_path, filename) try: with open(file_path, 'r', encoding='utf-8') as file: text = file.read() # Insert "NEWCHAPTERABC" at the beginning of each chapter's text if text: text = "NEWCHAPTERABC" + text sentences = nltk.tokenize.sent_tokenize(text) for sentence in sentences: start_location = text.find(sentence) end_location = start_location + len(sentence) writer.writerow([sentence, start_location, end_location, 'True', 'Narrator', chapter_number]) except Exception as e: print(f"Error processing file {filename}: {e}") # Example usage folder_path = os.path.join(".", "Working_files", "temp_ebook") output_csv = os.path.join(".", "Working_files", "Book", "Other_book.csv") process_chapter_files(folder_path, output_csv) def sort_key(filename): """Extract chapter number for sorting.""" match = re.search(r'chapter_(\d+)\.txt', filename) return int(match.group(1)) if match else 0 def combine_chapters(input_folder, output_file): # Create the output folder if it doesn't exist os.makedirs(os.path.dirname(output_file), exist_ok=True) # List all txt files and sort them by chapter number files = [f for f in os.listdir(input_folder) if f.endswith('.txt')] sorted_files = sorted(files, key=sort_key) with open(output_file, 'w', encoding='utf-8') as outfile: # Specify UTF-8 encoding here for i, filename in enumerate(sorted_files): with open(os.path.join(input_folder, filename), 'r', encoding='utf-8') as infile: # And here outfile.write(infile.read()) # Add the marker unless it's the last file if i < len(sorted_files) - 1: outfile.write("\nNEWCHAPTERABC\n") # Paths input_folder = os.path.join(".", 'Working_files', 'temp_ebook') output_file = os.path.join(".", 'Working_files', 'Book', 'Chapter_Book.txt') # Combine the chapters combine_chapters(input_folder, output_file) ensure_directory(os.path.join(".", "Working_files", "Book")) #create_chapter_labeled_book() import os import subprocess import sys import torchaudio # Check if Calibre's ebook-convert tool is installed def calibre_installed(): try: subprocess.run(['ebook-convert', '--version'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) return True except FileNotFoundError: print("Calibre is not installed. Please install Calibre for this functionality.") return False import os import torch from TTS.api import TTS from nltk.tokenize import sent_tokenize from pydub import AudioSegment # Assuming split_long_sentence and wipe_folder are defined elsewhere in your code default_target_voice_path = "default_voice.wav" # Ensure this is a valid path default_language_code = "en" # Function to check if vocab.json exists and rename it def rename_vocab_file_if_exists(directory): vocab_path = os.path.join(directory, 'vocab.json') new_vocab_path = os.path.join(directory, 'vocab.json_') # Check if vocab.json exists if os.path.exists(vocab_path): # Rename the file os.rename(vocab_path, new_vocab_path) print(f"Renamed {vocab_path} to {new_vocab_path}") return True # Return True if the file was found and renamed def combine_wav_files(input_directory, output_directory, file_name): # Ensure that the output directory exists, create it if necessary os.makedirs(output_directory, exist_ok=True) # Specify the output file path output_file_path = os.path.join(output_directory, file_name) # Initialize an empty audio segment combined_audio = AudioSegment.empty() # Get a list of all .wav files in the specified input directory and sort them input_file_paths = sorted( [os.path.join(input_directory, f) for f in os.listdir(input_directory) if f.endswith(".wav")], key=lambda f: int(''.join(filter(str.isdigit, f))) ) # Sequentially append each file to the combined_audio for input_file_path in input_file_paths: audio_segment = AudioSegment.from_wav(input_file_path) combined_audio += audio_segment # Export the combined audio to the output file path combined_audio.export(output_file_path, format='wav') print(f"Combined audio saved to {output_file_path}") # Function to split long strings into parts def split_long_sentence(sentence, max_length=249, max_pauses=10): """ Splits a sentence into parts based on length or number of pauses without recursion. :param sentence: The sentence to split. :param max_length: Maximum allowed length of a sentence. :param max_pauses: Maximum allowed number of pauses in a sentence. :return: A list of sentence parts that meet the criteria. """ parts = [] while len(sentence) > max_length or sentence.count(',') + sentence.count(';') + sentence.count('.') > max_pauses: possible_splits = [i for i, char in enumerate(sentence) if char in ',;.' and i < max_length] if possible_splits: # Find the best place to split the sentence, preferring the last possible split to keep parts longer split_at = possible_splits[-1] + 1 else: # If no punctuation to split on within max_length, split at max_length split_at = max_length # Split the sentence and add the first part to the list parts.append(sentence[:split_at].strip()) sentence = sentence[split_at:].strip() # Add the remaining part of the sentence parts.append(sentence) return parts """ if 'tts' not in locals(): tts = TTS(selected_tts_model, progress_bar=True).to(device) """ from tqdm import tqdm # Convert chapters to audio using XTTS def convert_chapters_to_audio_custom_model(chapters_dir, output_audio_dir, target_voice_path=None, language=None, custom_model=None): if target_voice_path==None: target_voice_path = default_target_voice_path if custom_model: print("Loading custom model...") config = XttsConfig() config.load_json(custom_model['config']) model = Xtts.init_from_config(config) model.load_checkpoint(config, checkpoint_path=custom_model['model'], vocab_path=custom_model['vocab'], use_deepspeed=False) model.to(device) print("Computing speaker latents...") gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(audio_path=[target_voice_path]) else: selected_tts_model = "tts_models/multilingual/multi-dataset/xtts_v2" tts = TTS(selected_tts_model, progress_bar=False).to(device) if not os.path.exists(output_audio_dir): os.makedirs(output_audio_dir) for chapter_file in sorted(os.listdir(chapters_dir)): if chapter_file.endswith('.txt'): match = re.search(r"chapter_(\d+).txt", chapter_file) if match: chapter_num = int(match.group(1)) else: print(f"Skipping file {chapter_file} as it does not match the expected format.") continue chapter_path = os.path.join(chapters_dir, chapter_file) output_file_name = f"audio_chapter_{chapter_num}.wav" output_file_path = os.path.join(output_audio_dir, output_file_name) temp_audio_directory = os.path.join(".", "Working_files", "temp") os.makedirs(temp_audio_directory, exist_ok=True) temp_count = 0 with open(chapter_path, 'r', encoding='utf-8') as file: chapter_text = file.read() sentences = sent_tokenize(chapter_text, language='italian' if language == 'it' else 'english') for sentence in tqdm(sentences, desc=f"Chapter {chapter_num}"): fragments = split_long_sentence(sentence, max_length=249 if language == "en" else 213, max_pauses=10) for fragment in fragments: if fragment != "": print(f"Generating fragment: {fragment}...") fragment_file_path = os.path.join(temp_audio_directory, f"{temp_count}.wav") if custom_model: out = model.inference(fragment, language, gpt_cond_latent, speaker_embedding, temperature=0.7) torchaudio.save(fragment_file_path, torch.tensor(out["wav"]).unsqueeze(0), 24000) else: speaker_wav_path = target_voice_path if target_voice_path else default_target_voice_path language_code = language if language else default_language_code tts.tts_to_file(text=fragment, file_path=fragment_file_path, speaker_wav=speaker_wav_path, language=language_code) temp_count += 1 combine_wav_files(temp_audio_directory, output_audio_dir, output_file_name) wipe_folder(temp_audio_directory) print(f"Converted chapter {chapter_num} to audio.") def convert_chapters_to_audio_standard_model(chapters_dir, output_audio_dir, target_voice_path=None, language=None): selected_tts_model = "tts_models/multilingual/multi-dataset/xtts_v2" tts = TTS(selected_tts_model, progress_bar=False).to(device) if not os.path.exists(output_audio_dir): os.makedirs(output_audio_dir) for chapter_file in sorted(os.listdir(chapters_dir)): if chapter_file.endswith('.txt'): match = re.search(r"chapter_(\d+).txt", chapter_file) if match: chapter_num = int(match.group(1)) else: print(f"Skipping file {chapter_file} as it does not match the expected format.") continue chapter_path = os.path.join(chapters_dir, chapter_file) output_file_name = f"audio_chapter_{chapter_num}.wav" output_file_path = os.path.join(output_audio_dir, output_file_name) temp_audio_directory = os.path.join(".", "Working_files", "temp") os.makedirs(temp_audio_directory, exist_ok=True) temp_count = 0 with open(chapter_path, 'r', encoding='utf-8') as file: chapter_text = file.read() sentences = sent_tokenize(chapter_text, language='italian' if language == 'it' else 'english') for sentence in tqdm(sentences, desc=f"Chapter {chapter_num}"): fragments = split_long_sentence(sentence, max_length=249 if language == "en" else 213, max_pauses=10) for fragment in fragments: if fragment != "": print(f"Generating fragment: {fragment}...") fragment_file_path = os.path.join(temp_audio_directory, f"{temp_count}.wav") speaker_wav_path = target_voice_path if target_voice_path else default_target_voice_path language_code = language if language else default_language_code tts.tts_to_file(text=fragment, file_path=fragment_file_path, speaker_wav=speaker_wav_path, language=language_code) temp_count += 1 combine_wav_files(temp_audio_directory, output_audio_dir, output_file_name) wipe_folder(temp_audio_directory) print(f"Converted chapter {chapter_num} to audio.") # Define the functions to be used in the Gradio interface def convert_ebook_to_audio(ebook_file, target_voice_file, language, use_custom_model, custom_model_file, custom_config_file, custom_vocab_file, custom_model_url=None, progress=gr.Progress()): ebook_file_path = ebook_file.name target_voice = target_voice_file.name if target_voice_file else None custom_model = None working_files = os.path.join(".", "Working_files", "temp_ebook") full_folder_working_files = os.path.join(".", "Working_files") chapters_directory = os.path.join(".", "Working_files", "temp_ebook") output_audio_directory = os.path.join(".", 'Chapter_wav_files') remove_folder_with_contents(full_folder_working_files) remove_folder_with_contents(output_audio_directory) if use_custom_model and custom_model_file and custom_config_file and custom_vocab_file: custom_model = { 'model': custom_model_file.name, 'config': custom_config_file.name, 'vocab': custom_vocab_file.name } if use_custom_model and custom_model_url: print(f"Received custom model URL: {custom_model_url}") download_dir = os.path.join(".", "Working_files", "custom_model") download_and_extract_zip(custom_model_url, download_dir) # Check if vocab.json exists and rename it if rename_vocab_file_if_exists(download_dir): print("vocab.json file was found and renamed.") custom_model = { 'model': os.path.join(download_dir, 'model.pth'), 'config': os.path.join(download_dir, 'config.json'), 'vocab': os.path.join(download_dir, 'vocab.json_') } try: progress(0, desc="Starting conversion") except Exception as e: print(f"Error updating progress: {e}") if not calibre_installed(): return "Calibre is not installed." try: progress(0.1, desc="Creating chapter-labeled book") except Exception as e: print(f"Error updating progress: {e}") create_chapter_labeled_book(ebook_file_path) audiobook_output_path = os.path.join(".", "Audiobooks") try: progress(0.3, desc="Converting chapters to audio") except Exception as e: print(f"Error updating progress: {e}") if use_custom_model: convert_chapters_to_audio_custom_model(chapters_directory, output_audio_directory, target_voice, language, custom_model) else: convert_chapters_to_audio_standard_model(chapters_directory, output_audio_directory, target_voice, language) try: progress(0.9, desc="Creating M4B from chapters") except Exception as e: print(f"Error updating progress: {e}") create_m4b_from_chapters(output_audio_directory, ebook_file_path, audiobook_output_path) # Get the name of the created M4B file m4b_filename = os.path.splitext(os.path.basename(ebook_file_path))[0] + '.m4b' m4b_filepath = os.path.join(audiobook_output_path, m4b_filename) try: progress(1.0, desc="Conversion complete") except Exception as e: print(f"Error updating progress: {e}") print(f"Audiobook created at {m4b_filepath}") return f"Audiobook created at {m4b_filepath}", m4b_filepath def list_audiobook_files(audiobook_folder): # List all files in the audiobook folder files = [] for filename in os.listdir(audiobook_folder): if filename.endswith('.m4b'): # Adjust the file extension as needed files.append(os.path.join(audiobook_folder, filename)) return files def download_audiobooks(): audiobook_output_path = os.path.join(".", "Audiobooks") return list_audiobook_files(audiobook_output_path) language_options = [ "en", "es", "fr", "de", "it", "pt", "pl", "tr", "ru", "nl", "cs", "ar", "zh-cn", "ja", "hu", "ko" ] theme = gr.themes.Soft( primary_hue="blue", secondary_hue="blue", neutral_hue="blue", text_size=gr.themes.sizes.text_md, ) # Gradio UI setup with gr.Blocks(theme=theme) as demo: gr.Markdown( """ # eBook to Audiobook Converter Transform your eBooks into immersive audiobooks with optional custom TTS models. This interface is based on [Ebook2AudioBookXTTS](https://github.com/DrewThomasson/ebook2audiobookXTTS). """ ) with gr.Row(): with gr.Column(scale=3): ebook_file = gr.File(label="eBook File") target_voice_file = gr.File(label="Target Voice File (Optional)") language = gr.Dropdown(label="Language", choices=language_options, value="en") with gr.Column(scale=3): use_custom_model = gr.Checkbox(label="Use Custom Model") custom_model_file = gr.File(label="Custom Model File (Optional)", visible=False) custom_config_file = gr.File(label="Custom Config File (Optional)", visible=False) custom_vocab_file = gr.File(label="Custom Vocab File (Optional)", visible=False) custom_model_url = gr.Textbox(label="Custom Model Zip URL (Optional)", visible=False) convert_btn = gr.Button("Convert to Audiobook", variant="primary") output = gr.Textbox(label="Conversion Status") audio_player = gr.Audio(label="Audiobook Player", type="filepath") download_btn = gr.Button("Download Audiobook Files") download_files = gr.File(label="Download Files", interactive=False) convert_btn.click( convert_ebook_to_audio, inputs=[ebook_file, target_voice_file, language, use_custom_model, custom_model_file, custom_config_file, custom_vocab_file, custom_model_url], outputs=[output, audio_player] ) use_custom_model.change( lambda x: [gr.update(visible=x)] * 4, inputs=[use_custom_model], outputs=[custom_model_file, custom_config_file, custom_vocab_file, custom_model_url] ) download_btn.click( download_audiobooks, outputs=[download_files] ) #demo.launch(share=True) #demo.launch() # Removing share = True for Gradio Interface # Get the correct local IP or localhost hostname = socket.gethostname() local_ip = socket.gethostbyname(hostname) # Ensure Gradio runs and prints the correct local IP print(f"Running on local URL: http://{local_ip}:7860") print(f"Running on local URL: http://localhost:7860") # Your Gradio launch command demo.launch(server_name="0.0.0.0", server_port=7860, share=args.share)