Delete app.py

app.py

@@ -1,427 +0,0 @@
-# app.py
-#
-# Copyright (C) August 4, 2025  Carlos Rodrigues dos Santos
-#
-# Version: 2.0.2
-#
-# Contact:
-# Carlos Rodrigues dos Santos
-# carlex22@gmail.com
-#
-# Related Repositories and Projects:
-# GitHub: https://github.com/carlex22/Aduc-sdr
-# YouTube (Results): https://m.youtube.com/channel/UC3EgoJi_Fv7yuDpvfYNtoIQ
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Affero General Public License as published by the
-# Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Affero General Public License for more details.
-#
-# You should have received a copy of the GNU Affero General Public License
-# along with this program. If not, see <https://www.gnu.org/licenses/>.
-#
-# PENDING PATENT NOTICE: The ADUC method and system implemented in this
-# software is in the process of being patented. Please see NOTICE.md for details.
-
-"""
-This file serves as the main entry point for the ADUC-SDR Gradio user interface.
-It orchestrates the multi-step workflow for AI-driven film creation, from
-pre-production (storyboarding, keyframing) to production (original video rendering)
-and post-production (upscaling, HD mastering, audio generation).
-
-The UI is structured using Accordion blocks to guide the user through a logical
-sequence of operations, while `gr.State` components manage the flow of data
-(file paths of generated artifacts) between these independent steps.
-"""
-
-import gradio as gr
-import yaml
-import logging
-import os
-import sys
-import shutil
-import time
-import json
-
-from aduc_orchestrator import AducOrchestrator
-
-# --- 1. CONFIGURATION AND INITIALIZATION ---
-# This section sets up logging, loads internationalization strings, and initializes
-# the core AducOrchestrator which manages all AI specialist models.
-
-LOG_FILE_PATH = "aduc_log.txt"
-if os.path.exists(LOG_FILE_PATH):
-    os.remove(LOG_FILE_PATH)
-
-log_format = '%(asctime)s - %(levelname)s - [%(name)s:%(funcName)s] - %(message)s'
-root_logger = logging.getLogger()
-root_logger.setLevel(logging.INFO)
-root_logger.handlers.clear()
-
-stream_handler = logging.StreamHandler(sys.stdout)
-stream_handler.setLevel(logging.INFO)
-stream_handler.setFormatter(logging.Formatter(log_format))
-root_logger.addHandler(stream_handler)
-
-file_handler = logging.FileHandler(LOG_FILE_PATH, mode='w', encoding='utf-8')
-file_handler.setLevel(logging.INFO)
-file_handler.setFormatter(logging.Formatter(log_format))
-root_logger.addHandler(file_handler)
-
-logger = logging.getLogger(__name__)
-
-# Load translation strings for the UI
-i18n = {}
-try:
-    with open("i18n.json", "r", encoding="utf-8") as f:
-        i18n = json.load(f)
-except Exception as e:
-    logger.error(f"Error loading i18n.json: {e}")
-    i18n = {"pt": {}, "en": {}, "zh": {}}
-
-# Fallback for missing languages
-if 'pt' not in i18n: i18n['pt'] = i18n.get('en', {})
-if 'en' not in i18n: i18n['en'] = {}
-if 'zh' not in i18n: i18n['zh'] = i18n.get('en', {})
-
-# Initialize the main orchestrator from the configuration file
-try:
-    with open("config.yaml", 'r') as f: config = yaml.safe_load(f)
-    WORKSPACE_DIR = config['application']['workspace_dir']
-    aduc = AducOrchestrator(workspace_dir=WORKSPACE_DIR)
-    logger.info("ADUC Orchestrator and Specialists initialized successfully.")
-except Exception as e:
-    logger.error(f"CRITICAL ERROR during initialization: {e}", exc_info=True)
-    exit()
-
-# --- 2. UI WRAPPER FUNCTIONS ---
-# These functions act as intermediaries between the Gradio UI components and the
-# AducOrchestrator. They handle input validation, progress tracking, and updating
-# the UI state after each operation.
-
-def run_pre_production_wrapper(prompt, num_keyframes, ref_files, resolution_str, duration_per_fragment, progress=gr.Progress()):
-    if not ref_files:
-        raise gr.Error("Please provide at least one reference image.")
-
-    ref_paths = [aduc.process_image_for_story(f.name, 480, f"ref_processed_{i}.png") for i, f in enumerate(ref_files)]
-
-    progress(0.1, desc="Generating storyboard...")
-    storyboard, initial_ref_path, _ = aduc.task_generate_storyboard(prompt, num_keyframes, ref_paths, progress)
-
-    resolution = int(resolution_str.split('x')[0])
-
-    def cb_factory(scene_index, total_scenes):
-        start_time = time.time()
-        total_steps = 12
-        def callback(pipe_self, step, timestep, callback_kwargs):
-            elapsed = time.time() - start_time
-            current_step = step + 1
-            if current_step > 0:
-                it_per_sec = current_step / elapsed
-                eta = (total_steps - current_step) / it_per_sec if it_per_sec > 0 else 0
-                desc = f"Keyframe {scene_index}/{total_scenes}: {int((current_step/total_steps)*100)}% | {current_step}/{total_steps} [{elapsed:.0f}s<{eta:.0f}s, {it_per_sec:.2f}it/s]"
-                base_progress = 0.2 + (scene_index - 1) * (0.8 / total_scenes)
-                step_progress = (current_step / total_steps) * (0.8 / total_scenes)
-                progress(base_progress + step_progress, desc=desc)
-            return {}
-        return callback
-
-    final_keyframes = aduc.task_generate_keyframes(storyboard, initial_ref_path, prompt, resolution, cb_factory)
-
-    return gr.update(value=storyboard), gr.update(value=final_keyframes), gr.update(visible=True, open=True)
-
-def run_pre_production_photo_wrapper(prompt, num_keyframes, ref_files, progress=gr.Progress()):
-    if not ref_files or len(ref_files) < 2:
-        raise gr.Error("Photographer Mode requires at least 2 images: one base and one for the scene pool.")
-
-    base_ref_paths = [aduc.process_image_for_story(ref_files[0].name, 480, "base_ref_processed_0.png")]
-    pool_ref_paths = [aduc.process_image_for_story(f.name, 480, f"pool_ref_{i+1}.png") for i, f in enumerate(ref_files[1:])]
-
-    progress(0.1, desc="Generating storyboard...")
-    storyboard, _, _ = aduc.task_generate_storyboard(prompt, num_keyframes, base_ref_paths, progress)
-
-    progress(0.5, desc="AI Photographer is selecting the best scenes...")
-    selected_keyframes = aduc.task_select_keyframes(storyboard, base_ref_paths, pool_ref_paths)
-
-    return gr.update(value=storyboard), gr.update(value=selected_keyframes), gr.update(visible=True, open=True)
-
-def run_original_production_wrapper(keyframes, prompt, duration,
-                                     trim_percent, handler_strength, destination_convergence_strength,
-                                     guidance_scale, stg_scale, inference_steps,
-                                     video_resolution,
-                                     progress=gr.Progress()):
-    """Wrapper for Step 3: Production. Correctly handles the return dictionary."""
-    yield {
-        original_video_output: gr.update(value=None, visible=True, label="🎬 Producing your original master video... Please wait."),
-        final_video_output: gr.update(value=None, visible=True, label="🎬 Production in progress..."),
-        step4_accordion: gr.update(visible=False)
-    }
-
-    resolution = int(video_resolution.split('x')[0])
-    
-    result = aduc.task_produce_original_movie(
-        keyframes, prompt, duration,
-        int(trim_percent), handler_strength, destination_convergence_strength,
-        guidance_scale, stg_scale, int(inference_steps),
-        resolution, use_continuity_director=True, progress=progress
-    )
-    
-    original_latents = result["latent_paths"]
-    original_video = result["final_path"]
-
-    yield {
-        original_video_output: gr.update(value=original_video, label="✅ Original Master Video"),
-        final_video_output: gr.update(value=original_video, label="Final Film (Result of the Last Step)"),
-        step4_accordion: gr.update(visible=True, open=True),
-        original_latents_paths_state: original_latents,
-        original_video_path_state: original_video,
-        current_source_video_state: original_video,
-    }
-
-def run_upscaler_wrapper(latent_paths, chunk_size, progress=gr.Progress()):
-    """Wrapper for Step 4A: Latent Upscaler. Correctly handles the generator."""
-    if not latent_paths:
-        raise gr.Error("Cannot run Upscaler. No original latents found. Please complete Step 3 first.")
-        
-    yield {
-        upscaler_video_output: gr.update(value=None, visible=True, label="Upscaling latents and decoding video..."),
-        final_video_output: gr.update(label="Post-Production in progress: Latent Upscaling...")
-    }
-
-    final_path = None
-    for update in aduc.task_run_latent_upscaler(latent_paths, int(chunk_size), progress=progress):
-        final_path = update['final_path']
-
-    yield {
-        upscaler_video_output: gr.update(value=final_path, label="✅ Latent Upscale Complete"),
-        final_video_output: gr.update(value=final_path),
-        upscaled_video_path_state: final_path,
-        current_source_video_state: final_path,
-    }
-
-def run_hd_wrapper(source_video, model_version, steps, global_prompt, progress=gr.Progress()):
-    """Wrapper for Step 4B: HD Mastering. Correctly handles the generator."""
-    if not source_video:
-        raise gr.Error("Cannot run HD Mastering. No source video found. Please complete a previous step first.")
-
-    yield {
-        hd_video_output: gr.update(value=None, visible=True, label="Applying HD mastering... This may take a while."),
-        final_video_output: gr.update(label="Post-Production in progress: HD Mastering...")
-    }
-    
-    final_path = None
-    for update in aduc.task_run_hd_mastering(source_video, model_version, int(steps), global_prompt, progress=progress):
-        final_path = update['final_path']
-
-    yield {
-        hd_video_output: gr.update(value=final_path, label="✅ HD Mastering Complete"),
-        final_video_output: gr.update(value=final_path),
-        hd_video_path_state: final_path,
-        current_source_video_state: final_path,
-    }
-
-def run_audio_wrapper(source_video, audio_prompt, global_prompt, progress=gr.Progress()):
-    """Wrapper for Step 4C: Audio Generation. Correctly handles the generator."""
-    if not source_video:
-        raise gr.Error("Cannot run Audio Generation. No source video found. Please complete a previous step first.")
-
-    yield {
-        audio_video_output: gr.update(value=None, visible=True, label="Generating audio and muxing..."),
-        final_video_output: gr.update(label="Post-Production in progress: Audio Generation...")
-    }
-
-    final_audio_prompt = audio_prompt if audio_prompt and audio_prompt.strip() else global_prompt
-
-    final_path = None
-    for update in aduc.task_run_audio_generation(source_video, final_audio_prompt, progress=progress):
-        final_path = update['final_path']
-
-    yield {
-        audio_video_output: gr.update(value=final_path, label="✅ Audio Generation Complete"),
-        final_video_output: gr.update(value=final_path),
-    }
-
-def get_log_content():
-    try:
-        with open(LOG_FILE_PATH, "r", encoding="utf-8") as f:
-            return f.read()
-    except FileNotFoundError:
-        return "Log file not yet created. Start a generation."
-
-def update_ui_language(lang_emoji):
-    lang_code_map = {"🇧🇷": "pt", "🇺🇸": "en", "🇨🇳": "zh"}
-    lang_code = lang_code_map.get(lang_emoji, "en")
-    lang_map = i18n.get(lang_code, i18n.get('en', {}))
-    return {
-        # General
-        title_md: gr.update(value=f"# {lang_map.get('app_title')}"),
-        subtitle_md: gr.update(value=lang_map.get('app_subtitle')),
-        lang_selector: gr.update(label=lang_map.get('lang_selector_label')),
-        # Step 1: Pre-Production
-        step1_accordion: gr.update(label=lang_map.get('step1_accordion')),
-        prompt_input: gr.update(label=lang_map.get('prompt_label')),
-        ref_image_input: gr.update(label=lang_map.get('ref_images_label')),
-        num_keyframes_slider: gr.update(label=lang_map.get('keyframes_label')),
-        duration_per_fragment_slider: gr.update(label=lang_map.get('duration_label'), info=lang_map.get('duration_info')),
-        storyboard_and_keyframes_button: gr.update(value=lang_map.get('storyboard_and_keyframes_button')),
-        storyboard_from_photos_button: gr.update(value=lang_map.get('storyboard_from_photos_button')),
-        step1_mode_b_info_md: gr.update(value=f"*{lang_map.get('step1_mode_b_info')}*"),
-        storyboard_output: gr.update(label=lang_map.get('storyboard_output_label')),
-        keyframe_gallery: gr.update(label=lang_map.get('keyframes_gallery_label')),
-        # Step 3: Production
-        step3_accordion: gr.update(label=lang_map.get('step3_accordion')),
-        step3_description_md: gr.update(value=lang_map.get('step3_description')),
-        produce_original_button: gr.update(value=lang_map.get('produce_original_button')),
-        ltx_advanced_options_accordion: gr.update(label=lang_map.get('ltx_advanced_options')),
-        causality_accordion: gr.update(label=lang_map.get('causality_controls_title')),
-        trim_percent_slider: gr.update(label=lang_map.get('trim_percent_label'), info=lang_map.get('trim_percent_info')),
-        forca_guia_slider: gr.update(label=lang_map.get('forca_guia_label'), info=lang_map.get('forca_guia_info')),
-        convergencia_destino_slider: gr.update(label=lang_map.get('convergencia_final_label'), info=lang_map.get('convergencia_final_info')),
-        ltx_pipeline_accordion: gr.update(label=lang_map.get('ltx_pipeline_options')),
-        guidance_scale_slider: gr.update(label=lang_map.get('guidance_scale_label'), info=lang_map.get('guidance_scale_info')),
-        stg_scale_slider: gr.update(label=lang_map.get('stg_scale_label'), info=lang_map.get('stg_scale_info')),
-        inference_steps_slider: gr.update(label=lang_map.get('steps_label'), info=lang_map.get('steps_info')),
-        # Step 4: Post-Production
-        step4_accordion: gr.update(label=lang_map.get('step4_accordion')),
-        step4_description_md: gr.update(value=lang_map.get('step4_description')),
-        sub_step_a_accordion: gr.update(label=lang_map.get('sub_step_a_upscaler')),
-        upscaler_description_md: gr.update(value=lang_map.get('upscaler_description')),
-        upscaler_options_accordion: gr.update(label=lang_map.get('upscaler_options')),
-        upscaler_chunk_size_slider: gr.update(label=lang_map.get('upscaler_chunk_size_label'), info=lang_map.get('upscaler_chunk_size_info')),
-        run_upscaler_button: gr.update(value=lang_map.get('run_upscaler_button')),
-        sub_step_b_accordion: gr.update(label=lang_map.get('sub_step_b_hd')),
-        hd_description_md: gr.update(value=lang_map.get('hd_description')),
-        hd_options_accordion: gr.update(label=lang_map.get('hd_options')),
-        hd_model_radio: gr.update(label=lang_map.get('hd_model_label')),
-        hd_steps_slider: gr.update(label=lang_map.get('hd_steps_label'), info=lang_map.get('hd_steps_info')),
-        run_hd_button: gr.update(value=lang_map.get('run_hd_button')),
-        sub_step_c_accordion: gr.update(label=lang_map.get('sub_step_c_audio')),
-        audio_description_md: gr.update(value=lang_map.get('audio_description')),
-        audio_options_accordion: gr.update(label=lang_map.get('audio_options')),
-        audio_prompt_input: gr.update(label=lang_map.get('audio_prompt_label'), info=lang_map.get('audio_prompt_info')),
-        run_audio_button: gr.update(value=lang_map.get('run_audio_button')),
-        # Final Outputs & Logs
-        final_video_output: gr.update(label=lang_map.get('final_video_label')),
-        log_accordion: gr.update(label=lang_map.get('log_accordion_label')),
-        log_display: gr.update(label=lang_map.get('log_display_label')),
-        update_log_button: gr.update(value=lang_map.get('update_log_button')),
-    }
-
-# --- 3. GRADIO UI DEFINITION ---
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    default_lang = i18n.get('pt', {})
-    
-    original_latents_paths_state = gr.State(value=None)
-    original_video_path_state = gr.State(value=None)
-    upscaled_video_path_state = gr.State(value=None)
-    hd_video_path_state = gr.State(value=None)
-    current_source_video_state = gr.State(value=None)
-
-    title_md = gr.Markdown(f"# {default_lang.get('app_title')}")
-    subtitle_md = gr.Markdown(default_lang.get('app_subtitle'))
-    with gr.Row():
-        lang_selector = gr.Radio(["🇧🇷", "🇺🇸", "🇨🇳"], value="🇧🇷", label=default_lang.get('lang_selector_label'))
-        resolution_selector = gr.Radio(["480x480", "720x720", "960x960"], value="480x480", label="Base Resolution")
-
-    with gr.Accordion(default_lang.get('step1_accordion'), open=True) as step1_accordion:
-        prompt_input = gr.Textbox(label=default_lang.get('prompt_label'), value="A majestic lion walks across the savanna, sits down, and then roars at the setting sun.")
-        ref_image_input = gr.File(label=default_lang.get('ref_images_label'), file_count="multiple", file_types=["image"])
-        with gr.Row():
-            num_keyframes_slider = gr.Slider(minimum=3, maximum=42, value=5, step=1, label=default_lang.get('keyframes_label'))
-            duration_per_fragment_slider = gr.Slider(label=default_lang.get('duration_label'), info=default_lang.get('duration_info'), minimum=2.0, maximum=10.0, value=4.0, step=0.1)
-        with gr.Row():
-            storyboard_and_keyframes_button = gr.Button(default_lang.get('storyboard_and_keyframes_button'), variant="primary")
-            storyboard_from_photos_button = gr.Button(default_lang.get('storyboard_from_photos_button'))
-        step1_mode_b_info_md = gr.Markdown(f"*{default_lang.get('step1_mode_b_info')}*")
-        storyboard_output = gr.JSON(label=default_lang.get('storyboard_output_label'))
-        keyframe_gallery = gr.Gallery(label=default_lang.get('keyframes_gallery_label'), visible=True, object_fit="contain", height="auto", type="filepath")
-
-    with gr.Accordion(default_lang.get('step3_accordion'), open=False, visible=False) as step3_accordion:
-        step3_description_md = gr.Markdown(default_lang.get('step3_description'))
-        with gr.Accordion(default_lang.get('ltx_advanced_options'), open=False) as ltx_advanced_options_accordion:
-            with gr.Accordion(default_lang.get('causality_controls_title'), open=True) as causality_accordion:
-                trim_percent_slider = gr.Slider(minimum=10, maximum=90, value=50, step=5, label=default_lang.get('trim_percent_label'), info=default_lang.get('trim_percent_info'))
-                with gr.Row():
-                    forca_guia_slider = gr.Slider(label=default_lang.get('forca_guia_label'), minimum=0.0, maximum=1.0, value=0.5, step=0.05, info=default_lang.get('forca_guia_info'))
-                    convergencia_destino_slider = gr.Slider(label=default_lang.get('convergencia_final_label'), minimum=0.0, maximum=1.0, value=0.75, step=0.05, info=default_lang.get('convergencia_final_info'))
-            with gr.Accordion(default_lang.get('ltx_pipeline_options'), open=True) as ltx_pipeline_accordion:
-                with gr.Row():
-                    guidance_scale_slider = gr.Slider(minimum=1.0, maximum=10.0, value=2.0, step=0.1, label=default_lang.get('guidance_scale_label'), info=default_lang.get('guidance_scale_info'))
-                    stg_scale_slider = gr.Slider(minimum=0.0, maximum=1.0, value=0.025, step=0.005, label=default_lang.get('stg_scale_label'), info=default_lang.get('stg_scale_info'))
-                inference_steps_slider = gr.Slider(minimum=10, maximum=50, value=20, step=1, label=default_lang.get('steps_label'), info=default_lang.get('steps_info'))
-        produce_original_button = gr.Button(default_lang.get('produce_original_button'), variant="primary")
-        original_video_output = gr.Video(label="Original Master Video", visible=False, interactive=False)
-
-    with gr.Accordion(default_lang.get('step4_accordion'), open=False, visible=False) as step4_accordion:
-        step4_description_md = gr.Markdown(default_lang.get('step4_description'))
-        with gr.Accordion(default_lang.get('sub_step_a_upscaler'), open=True) as sub_step_a_accordion:
-            upscaler_description_md = gr.Markdown(default_lang.get('upscaler_description'))
-            with gr.Accordion(default_lang.get('upscaler_options'), open=False) as upscaler_options_accordion:
-                upscaler_chunk_size_slider = gr.Slider(minimum=1, maximum=10, value=2, step=1, label=default_lang.get('upscaler_chunk_size_label'), info=default_lang.get('upscaler_chunk_size_info'))
-            run_upscaler_button = gr.Button(default_lang.get('run_upscaler_button'), variant="secondary")
-            upscaler_video_output = gr.Video(label="Upscaled Video", visible=False, interactive=False)
-        with gr.Accordion(default_lang.get('sub_step_b_hd'), open=True) as sub_step_b_accordion:
-            hd_description_md = gr.Markdown(default_lang.get('hd_description'))
-            with gr.Accordion(default_lang.get('hd_options'), open=False) as hd_options_accordion:
-                hd_model_radio = gr.Radio(["3B", "7B"], value="7B", label=default_lang.get('hd_model_label'))
-                hd_steps_slider = gr.Slider(minimum=20, maximum=150, value=100, step=5, label=default_lang.get('hd_steps_label'), info=default_lang.get('hd_steps_info'))
-            run_hd_button = gr.Button(default_lang.get('run_hd_button'), variant="secondary")
-            hd_video_output = gr.Video(label="HD Mastered Video", visible=False, interactive=False)
-        with gr.Accordion(default_lang.get('sub_step_c_audio'), open=True) as sub_step_c_accordion:
-            audio_description_md = gr.Markdown(default_lang.get('audio_description'))
-            with gr.Accordion(default_lang.get('audio_options'), open=False) as audio_options_accordion:
-                audio_prompt_input = gr.Textbox(label=default_lang.get('audio_prompt_label'), info=default_lang.get('audio_prompt_info'), lines=3)
-            run_audio_button = gr.Button(default_lang.get('run_audio_button'), variant="secondary")
-            audio_video_output = gr.Video(label="Video with Audio", visible=False, interactive=False)
-
-    final_video_output = gr.Video(label=default_lang.get('final_video_label'), visible=False, interactive=False)
-    with gr.Accordion(default_lang.get('log_accordion_label'), open=False) as log_accordion:
-        log_display = gr.Textbox(label=default_lang.get('log_display_label'), lines=20, interactive=False, autoscroll=True)
-        update_log_button = gr.Button(default_lang.get('update_log_button'))
-
-    # --- 4. UI EVENT CONNECTIONS ---
-    all_ui_components = list(update_ui_language('🇧🇷').keys())
-    lang_selector.change(fn=update_ui_language, inputs=lang_selector, outputs=all_ui_components)
-
-    storyboard_and_keyframes_button.click(fn=run_pre_production_wrapper, inputs=[prompt_input, num_keyframes_slider, ref_image_input, resolution_selector, duration_per_fragment_slider], outputs=[storyboard_output, keyframe_gallery, step3_accordion])
-    storyboard_from_photos_button.click(fn=run_pre_production_photo_wrapper, inputs=[prompt_input, num_keyframes_slider, ref_image_input], outputs=[storyboard_output, keyframe_gallery, step3_accordion])
-
-    produce_original_button.click(
-        fn=run_original_production_wrapper,
-        inputs=[keyframe_gallery, prompt_input, duration_per_fragment_slider, trim_percent_slider, forca_guia_slider, convergencia_destino_slider, guidance_scale_slider, stg_scale_slider, inference_steps_slider, resolution_selector],
-        outputs=[original_video_output, final_video_output, step4_accordion, original_latents_paths_state, original_video_path_state, current_source_video_state]
-    )
-    
-    run_upscaler_button.click(
-        fn=run_upscaler_wrapper,
-        inputs=[original_latents_paths_state, upscaler_chunk_size_slider],
-        outputs=[upscaler_video_output, final_video_output, upscaled_video_path_state, current_source_video_state]
-    )
-    
-    run_hd_button.click(
-        fn=run_hd_wrapper,
-        inputs=[current_source_video_state, hd_model_radio, hd_steps_slider, prompt_input],
-        outputs=[hd_video_output, final_video_output, hd_video_path_state, current_source_video_state]
-    )
-
-    run_audio_button.click(
-        fn=run_audio_wrapper,
-        inputs=[current_source_video_state, audio_prompt_input, prompt_input],
-        outputs=[audio_video_output, final_video_output]
-    )
-    
-    update_log_button.click(fn=get_log_content, inputs=[], outputs=[log_display])
-
-# --- 5. APPLICATION LAUNCH ---
-if __name__ == "__main__":
-    if os.path.exists(WORKSPACE_DIR):
-        logger.info(f"Clearing previous workspace at: {WORKSPACE_DIR}")
-        shutil.rmtree(WORKSPACE_DIR)
-    os.makedirs(WORKSPACE_DIR)
-    logger.info(f"Application started. Launching Gradio interface...")
-    demo.queue().launch()