THIS_REPO = "Civarchivist/test_progress" import gradio as gr import numpy as np import random # # import spaces #[uncomment to use ZeroGPU] # from diffusers import DiffusionPipeline # import torch # device = "cuda" if torch.cuda.is_available() else "cpu" # model_repo_id = "stabilityai/sdxl-turbo" # Replace to the model you would like to use # if torch.cuda.is_available(): # torch_dtype = torch.float16 # else: # torch_dtype = torch.float32 # pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype) # pipe = pipe.to(device) # MAX_SEED = np.iinfo(np.int32).max # MAX_IMAGE_SIZE = 1024 # # @spaces.GPU #[uncomment to use ZeroGPU] # def infer( # prompt, # negative_prompt, # seed, # randomize_seed, # width, # height, # guidance_scale, # num_inference_steps, # progress=gr.Progress(track_tqdm=True), # ): # if randomize_seed: # seed = random.randint(0, MAX_SEED) # generator = torch.Generator().manual_seed(seed) # image = pipe( # prompt=prompt, # negative_prompt=negative_prompt, # guidance_scale=guidance_scale, # num_inference_steps=num_inference_steps, # width=width, # height=height, # generator=generator, # ).images[0] # return image, seed # examples = [ # "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", # "An astronaut riding a green horse", # "A delicious ceviche cheesecake slice", # ] # css = """ # #col-container { # margin: 0 auto; # max-width: 640px; # } # """ # with gr.Blocks(css=css) as demo: # with gr.Column(elem_id="col-container"): # gr.Markdown(" # Text-to-Image Gradio Template") # with gr.Row(): # prompt = gr.Text( # label="Prompt", # show_label=False, # max_lines=1, # placeholder="Enter your prompt", # container=False, # ) # run_button = gr.Button("Run", scale=0, variant="primary") # result = gr.Image(label="Result", show_label=False) # with gr.Accordion("Advanced Settings", open=False): # negative_prompt = gr.Text( # label="Negative prompt", # max_lines=1, # placeholder="Enter a negative prompt", # visible=False, # ) # seed = gr.Slider( # label="Seed", # minimum=0, # maximum=MAX_SEED, # step=1, # value=0, # ) # randomize_seed = gr.Checkbox(label="Randomize seed", value=True) # with gr.Row(): # width = gr.Slider( # label="Width", # minimum=256, # maximum=MAX_IMAGE_SIZE, # step=32, # value=1024, # Replace with defaults that work for your model # ) # height = gr.Slider( # label="Height", # minimum=256, # maximum=MAX_IMAGE_SIZE, # step=32, # value=1024, # Replace with defaults that work for your model # ) # with gr.Row(): # guidance_scale = gr.Slider( # label="Guidance scale", # minimum=0.0, # maximum=10.0, # step=0.1, # value=0.0, # Replace with defaults that work for your model # ) # num_inference_steps = gr.Slider( # label="Number of inference steps", # minimum=1, # maximum=50, # step=1, # value=2, # Replace with defaults that work for your model # ) # gr.Examples(examples=examples, inputs=[prompt]) # gr.on( # triggers=[run_button.click, prompt.submit], # fn=infer, # inputs=[ # prompt, # negative_prompt, # seed, # randomize_seed, # width, # height, # guidance_scale, # num_inference_steps, # ], # outputs=[result, seed], # ) import requests import os import gradio as gr from huggingface_hub import update_repo_visibility, whoami, upload_folder, create_repo, upload_file, hf_hub_download, update_repo_visibility, file_exists, list_models import subprocess import gradio as gr import re import uuid from typing import Optional import json import time from pathlib import Path from apscheduler.schedulers.background import BackgroundScheduler from huggingface_hub import Repository, HfApi api = HfApi() def slowly_reverse(word, progress=gr.Progress()): progress(0, desc="Starting\nPrepare yourself to be obliterated\nFilthy human") time.sleep(1) progress(0.05) new_string = "" eh = "start" for letter in progress.tqdm(word, desc="Reversing" + eh): time.sleep(0.25) new_string = letter + new_string eh = new_string progress(0, desc="You fool! This isn't even my final form!") time.sleep(5) progress(0.5, desc="You fool! This isn't even my final form!") return new_string #demo = gr.Interface(slowly_reverse, gr.Text(), gr.Text()) css = ''' #login { width: 100% !important; margin: 0 auto; } #disabled_upload{ opacity: 0.5; pointer-events:none; } .error-log { max-height: 300px; overflow-y: auto; background-color: #f8d7da; padding: 10px; border-radius: 5px; margin-top: 10px; } ''' error_log = [] def log_error(message): error_log.append(f"{time.strftime('%Y-%m-%d %H:%M:%S')} - {message}") return "\n".join(error_log[-10:]) # Show last 10 errors def restart_space(): try: api.restart_space(repo_id=THIS_REPO, token=os.environ["HF_TOKEN"]) except Exception as e: return log_error(f"Error restarting space: {str(e)}") with gr.Blocks(css=css) as demo: with gr.Column(): input_text = gr.Text("Whatever", interactive = True) output_text = gr.Text("Output") submit_btn = gr.Button("Upload to Hugging Face", interactive=True) upload_progress = gr.Progress(0) output = gr.Markdown(label="Upload Progress") def run_test(word, progress = gr.Progress()): vout = slowly_reverse(word, progress) return None, vout submit_btn.click( fn=run_test, # inputs=[input_text, upload_progress], # Does not work, progress has no id and therefore cannot be used an input. inputs=[input_text], outputs=[output, output_text] ) if __name__ == "__main__": scheduler = BackgroundScheduler() scheduler.add_job(restart_space, 'interval', seconds=3600) scheduler.start() demo.queue(default_concurrency_limit=5) demo.launch()