vnyash
89 lines
3.2 KiB
Python
89 lines
3.2 KiB
Python
from typing import Tuple, Optional
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from time import sleep
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import gradio
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import deepfuze.globals
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from deepfuze import process_manager, wording
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from deepfuze.core import conditional_process
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from deepfuze.memory import limit_system_memory
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from deepfuze.normalizer import normalize_output_path
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from deepfuze.uis.core import get_ui_component
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from deepfuze.filesystem import clear_temp, is_image, is_video
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OUTPUT_IMAGE : Optional[gradio.Image] = None
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OUTPUT_VIDEO : Optional[gradio.Video] = None
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OUTPUT_START_BUTTON : Optional[gradio.Button] = None
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OUTPUT_CLEAR_BUTTON : Optional[gradio.Button] = None
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OUTPUT_STOP_BUTTON : Optional[gradio.Button] = None
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def render() -> None:
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global OUTPUT_IMAGE
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global OUTPUT_VIDEO
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global OUTPUT_START_BUTTON
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global OUTPUT_STOP_BUTTON
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global OUTPUT_CLEAR_BUTTON
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OUTPUT_IMAGE = gradio.Image(
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label = wording.get('uis.output_image_or_video'),
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visible = False
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)
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OUTPUT_VIDEO = gradio.Video(
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label = wording.get('uis.output_image_or_video')
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)
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OUTPUT_START_BUTTON = gradio.Button(
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value = wording.get('uis.start_button'),
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variant = 'primary',
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size = 'sm'
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)
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OUTPUT_STOP_BUTTON = gradio.Button(
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value = wording.get('uis.stop_button'),
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variant = 'primary',
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size = 'sm',
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visible = False
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)
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OUTPUT_CLEAR_BUTTON = gradio.Button(
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value = wording.get('uis.clear_button'),
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size = 'sm'
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)
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def listen() -> None:
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output_path_textbox = get_ui_component('output_path_textbox')
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if output_path_textbox:
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OUTPUT_START_BUTTON.click(start, outputs = [ OUTPUT_START_BUTTON, OUTPUT_STOP_BUTTON ])
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OUTPUT_START_BUTTON.click(process, outputs = [ OUTPUT_IMAGE, OUTPUT_VIDEO, OUTPUT_START_BUTTON, OUTPUT_STOP_BUTTON ])
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OUTPUT_STOP_BUTTON.click(stop, outputs = [ OUTPUT_START_BUTTON, OUTPUT_STOP_BUTTON ])
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OUTPUT_CLEAR_BUTTON.click(clear, outputs = [ OUTPUT_IMAGE, OUTPUT_VIDEO ])
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def start() -> Tuple[gradio.Button, gradio.Button]:
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while not process_manager.is_processing():
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sleep(0.5)
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return gradio.Button(visible = False), gradio.Button(visible = True)
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def process() -> Tuple[gradio.Image, gradio.Video, gradio.Button, gradio.Button]:
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normed_output_path = normalize_output_path(deepfuze.globals.target_path, deepfuze.globals.output_path)
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if deepfuze.globals.system_memory_limit > 0:
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limit_system_memory(deepfuze.globals.system_memory_limit)
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conditional_process()
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if is_image(normed_output_path):
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return gradio.Image(value = normed_output_path, visible = True), gradio.Video(value = None, visible = False), gradio.Button(visible = True), gradio.Button(visible = False)
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if is_video(normed_output_path):
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return gradio.Image(value = None, visible = False), gradio.Video(value = normed_output_path, visible = True), gradio.Button(visible = True), gradio.Button(visible = False)
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return gradio.Image(value = None), gradio.Video(value = None), gradio.Button(visible = True), gradio.Button(visible = False)
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def stop() -> Tuple[gradio.Button, gradio.Button]:
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process_manager.stop()
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return gradio.Button(visible = True), gradio.Button(visible = False)
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def clear() -> Tuple[gradio.Image, gradio.Video]:
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while process_manager.is_processing():
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sleep(0.5)
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if deepfuze.globals.target_path:
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clear_temp(deepfuze.globals.target_path)
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return gradio.Image(value = None), gradio.Video(value = None)
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