import os import traceback import gradio as gr from gui.asset_components import AssetComponentsUtils from gui.ui_abstract_component import AbstractComponentUI from gui.ui_components_html import GradioComponentsHTML from shortGPT.audio.edge_voice_module import EdgeTTSVoiceModule from shortGPT.audio.eleven_voice_module import ElevenLabsVoiceModule from shortGPT.audio.coqui_voice_module import CoquiVoiceModule from shortGPT.config.api_db import ApiKeyManager from shortGPT.config.languages import (EDGE_TTS_VOICENAME_MAPPING, ELEVEN_SUPPORTED_LANGUAGES, COQUI_SUPPORTED_LANGUAGES, LANGUAGE_ACRONYM_MAPPING, Language) from shortGPT.engine.facts_short_engine import FactsShortEngine from shortGPT.engine.reddit_short_engine import RedditShortEngine class ShortAutomationUI(AbstractComponentUI): def __init__(self, shortGptUI: gr.Blocks): self.shortGptUI = shortGptUI self.embedHTML = '
' self.progress_counter = 0 self.short_automation = None def create_ui(self): with gr.Row(visible=False) as short_automation: with gr.Column(): numShorts = gr.Number(label="Number of shorts", minimum=1, value=1) short_type = gr.Radio(["Reddit Story shorts", "Historical Facts shorts", "Scientific Facts shorts", "Custom Facts shorts"], label="Type of shorts generated", value="Scientific Facts shorts", interactive=True) facts_subject = gr.Textbox(label="Write a subject for your facts (example: Football facts)", interactive=True, visible=False) short_type.change(lambda x: gr.update(visible=x == "Custom Facts shorts"), [short_type], [facts_subject]) tts_engine = gr.Radio([AssetComponentsUtils.ELEVEN_TTS, AssetComponentsUtils.EDGE_TTS, AssetComponentsUtils.COQUI_TTS], label="Text to speech engine", value=AssetComponentsUtils.ELEVEN_TTS, interactive=True) self.tts_engine = tts_engine.value with gr.Column(visible=True) as eleven_tts: language_eleven = gr.Radio([lang.value for lang in ELEVEN_SUPPORTED_LANGUAGES], label="Language", value="English", interactive=True) voice_eleven = AssetComponentsUtils.voiceChoice(provider=AssetComponentsUtils.ELEVEN_TTS) with gr.Column(visible=False) as edge_tts: language_edge = gr.Dropdown([lang.value.upper() for lang in Language], label="Language", value="ENGLISH", interactive=True) with gr.Column(visible=False) as coqui_tts: language_coqui = gr.Radio([lang.value for lang in COQUI_SUPPORTED_LANGUAGES], label="Language", value="English", interactive=True) voice_coqui = AssetComponentsUtils.voiceChoice(provider=AssetComponentsUtils.COQUI_TTS) def tts_engine_change(x): self.tts_engine = x return gr.update(visible=x == AssetComponentsUtils.ELEVEN_TTS), gr.update(visible=x == AssetComponentsUtils.EDGE_TTS), gr.update(visible=x == AssetComponentsUtils.COQUI_TTS) tts_engine.change(tts_engine_change, tts_engine, [eleven_tts, edge_tts, coqui_tts]) useImages = gr.Checkbox(label="Use images", value=True) numImages = gr.Radio([5, 10, 25], value=25, label="Number of images per short", visible=True, interactive=True) useImages.change(lambda x: gr.update(visible=x), useImages, numImages) addWatermark = gr.Checkbox(label="Add watermark") watermark = gr.Textbox(label="Watermark (your channel name)", visible=False) addWatermark.change(lambda x: gr.update(visible=x), [addWatermark], [watermark]) AssetComponentsUtils.background_video_checkbox() AssetComponentsUtils.background_music_checkbox() createButton = gr.Button("Create Shorts") generation_error = gr.HTML(visible=True) video_folder = gr.Button("📁", visible=True) output = gr.HTML() video_folder.click(lambda _: AssetComponentsUtils.start_file(os.path.abspath("videos/"))) createButton.click(self.inspect_create_inputs, inputs=[AssetComponentsUtils.background_video_checkbox(), AssetComponentsUtils.background_music_checkbox(), watermark, short_type, facts_subject], outputs=[generation_error]).success(self.create_short, inputs=[ numShorts, short_type, tts_engine, language_eleven, language_edge, language_coqui, numImages, watermark, AssetComponentsUtils.background_video_checkbox(), AssetComponentsUtils.background_music_checkbox(), facts_subject, voice_eleven, voice_coqui ], outputs=[output, video_folder, generation_error]) self.short_automation = short_automation return self.short_automation def create_short(self, numShorts, short_type, tts_engine, language_eleven, language_edge, language_coqui, numImages, watermark, background_video_list, background_music_list, facts_subject, voice_eleven, voice_coqui, progress=gr.Progress()): '''Creates a short''' try: numShorts = int(numShorts) numImages = int(numImages) if numImages else None background_videos = (background_video_list * ((numShorts // len(background_video_list)) + 1))[:numShorts] background_musics = (background_music_list * ((numShorts // len(background_music_list)) + 1))[:numShorts] if tts_engine == AssetComponentsUtils.ELEVEN_TTS: language = Language(language_eleven.lower().capitalize()) voice_module = ElevenLabsVoiceModule(ApiKeyManager.get_api_key('ELEVEN LABS'), voice_eleven, checkElevenCredits=True) elif tts_engine == AssetComponentsUtils.EDGE_TTS: language = Language(language_edge.lower().capitalize()) voice_module = EdgeTTSVoiceModule(EDGE_TTS_VOICENAME_MAPPING[language]['male']) elif tts_engine == AssetComponentsUtils.COQUI_TTS: language = Language(language_coqui.lower().capitalize()) voice_module = CoquiVoiceModule(voice_coqui, LANGUAGE_ACRONYM_MAPPING[language]) for i in range(numShorts): shortEngine = self.create_short_engine(short_type=short_type, voice_module=voice_module, language=language, numImages=numImages, watermark=watermark, background_video=background_videos[i], background_music=background_musics[i], facts_subject=facts_subject) num_steps = shortEngine.get_total_steps() def logger(prog_str): progress(self.progress_counter / (num_steps * numShorts), f"Making short {i+1}/{numShorts} - {prog_str}") shortEngine.set_logger(logger) for step_num, step_info in shortEngine.makeContent(): progress(self.progress_counter / (num_steps * numShorts), f"Making short {i+1}/{numShorts} - {step_info}") self.progress_counter += 1 video_path = shortEngine.get_video_output_path() current_url = self.shortGptUI.share_url+"/" if self.shortGptUI.share else self.shortGptUI.local_url file_url_path = f"{current_url}file={video_path}" file_name = video_path.split("/")[-1].split("\\")[-1] self.embedHTML += f'''
''' yield self.embedHTML + '
', gr.Button.update(visible=True), gr.update(visible=False) except Exception as e: traceback_str = ''.join(traceback.format_tb(e.__traceback__)) error_name = type(e).__name__.capitalize() + " : " + f"{e.args[0]}" print("Error", traceback_str) error_html = GradioComponentsHTML.get_html_error_template().format(error_message=error_name, stack_trace=traceback_str) yield self.embedHTML + '', gr.Button.update(visible=True), gr.HTML.update(value=error_html, visible=True) def inspect_create_inputs(self, background_video_list, background_music_list, watermark, short_type, facts_subject): if short_type == "Custom Facts shorts": if not facts_subject: raise gr.Error("Please write down your facts short's subject") if not background_video_list: raise gr.Error("Please select at least one background video.") if not background_music_list: raise gr.Error("Please select at least one background music.") if watermark != "": if not watermark.replace(" ", "").isalnum(): raise gr.Error("Watermark should only contain letters and numbers.") if len(watermark) > 25: raise gr.Error("Watermark should not exceed 25 characters.") if len(watermark) < 3: raise gr.Error("Watermark should be at least 3 characters long.") openai_key = ApiKeyManager.get_api_key("OPENAI") if not openai_key: raise gr.Error("OPENAI API key is missing. Please go to the config tab and enter the API key.") eleven_labs_key = ApiKeyManager.get_api_key("ELEVEN LABS") if self.tts_engine == AssetComponentsUtils.ELEVEN_TTS and not eleven_labs_key: raise gr.Error("ELEVEN LABS API key is missing. Please go to the config tab and enter the API key.") return gr.update(visible=False) def create_short_engine(self, short_type, voice_module, language, numImages, watermark, background_video, background_music, facts_subject): if short_type == "Reddit Story shorts": return RedditShortEngine(voice_module, background_video_name=background_video, background_music_name=background_music, num_images=numImages, watermark=watermark, language=language) if "fact" in short_type.lower(): if "custom" in short_type.lower(): facts_subject = facts_subject else: facts_subject = short_type return FactsShortEngine(voice_module, facts_type=facts_subject, background_video_name=background_video, background_music_name=background_music, num_images=50, watermark=watermark, language=language) raise gr.Error(f"Short type does not have a valid short engine: {short_type}")