app.py

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()