app.py

import gradio as gr
from huggingface_hub import HfApi, whoami
from all_models import models
from config import howManyModelsToUse,num_models,max_images,inference_timeout,MAX_SEED,thePrompt,preSetPrompt,negPreSetPrompt
default_models = models[:num_models]
import asyncio
import os
import pandas as pd
from datetime import datetime
from threading import RLock
lock = RLock()
HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None # If private or gated models aren't used, ENV setting is unnecessary.
# --- Step 2: Authenticate and fetch your models
api = HfApi()
user_info = whoami(token=HF_TOKEN)
username = user_info["name"]
from handle_models import load_fn,infer,gen_fn
from externalmod import gr_Interface_load, save_image, randomize_seed
from handlemodelradio import extend_choices,update_imgbox,random_choices
#anything but huggingface_hub==0.26.2 will result in token error
# Get all models owned by the user
#models = api.list_models(author=username, token=HF_TOKEN)
mymodels = list(api.list_models(author=username, token=HF_TOKEN))
model_ids = [m.modelId for m in mymodels]
if not model_ids:
    raise ValueError(f"No models found for user '{username}'")
# --- Step 3: Build Gradio UI
def handle_model_selection(selected_models):
    if not selected_models:
        return "No models selected."
    return "✅ Selected models:\n" + "\n".join(selected_models)


def get_current_time():
    now = datetime.now()
    current_time = now.strftime("%y-%m-%d %H:%M:%S")
    return current_time
load_fn(models)


'''
 
'''
with gr.Blocks(fill_width=True) as demo:
    with gr.Row():
        gr.Markdown(f"# ({username}) you are logged in")
        model_selector = gr.CheckboxGroup(choices=model_ids,value=model_ids, label="your models",        interactive=True,    )
        output_box = gr.Textbox(lines=10, label="Selected Models")
        model_selector.change(fn=handle_model_selection, inputs=model_selector, outputs=output_box)   
    with gr.Tab(str(num_models) + ' Models'):
        with gr.Column(scale=2):
            with gr.Group():
                txt_input = gr.Textbox(label='Your prompt:', value=preSetPrompt, lines=3, autofocus=1)
                with gr.Accordion("Advanced", open=False, visible=True):
                    with gr.Row():
                        neg_input = gr.Textbox(label='Negative prompt:', value=negPreSetPrompt, lines=1)
                    with gr.Row():    
                        width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
                        height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
                    with gr.Row():
                        steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
                        cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
                        seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
                        seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary")
                        seed_rand.click(randomize_seed, None, [seed], queue=False)
            with gr.Row():
                gen_button = gr.Button(f'Generate up to {int(num_models)} images', variant='primary', scale=3, elem_classes=["butt"])
                random_button = gr.Button(f'Randomize Models', variant='secondary', scale=1)
        with gr.Column(scale=1):
            with gr.Group():
                with gr.Row():
                    output = [gr.Image(label=m, show_download_button=True, elem_classes=["image-monitor"],
                              interactive=False, width=112, height=112, show_share_button=False, format="png",
                              visible=True) for m in default_models]
                    current_models = [gr.Textbox(m, visible=False) for m in default_models]
        for m, o in zip(current_models, output):
            gen_event = gr.on(triggers=[gen_button.click, txt_input.submit], fn=gen_fn,
                              inputs=[m, txt_input, neg_input, height, width, steps, cfg, seed], outputs=[o],
                              concurrency_limit=None, queue=False)
        with gr.Column(scale=4):
            with gr.Accordion('Model selection'):
                model_choice = gr.CheckboxGroup(models, label = f'Choose up to {int(num_models)} different models from the {len(models)} available!', value=default_models, interactive=True)
                model_choice.change(update_imgbox, model_choice, output)
                model_choice.change(extend_choices, model_choice, current_models)
                random_button.click(random_choices, None, model_choice)
    
            

demo.launch(show_api=False, max_threads=400)



















'''

# --- Step 2: Fetch user's Spaces
spaces = list(api.list_spaces(author=username, token=HF_TOKEN))
space_df = pd.DataFrame([{"Space Name": f"<a href='#' data-space='{space.id}'>{space.id.split('/')[-1]}</a>",
    "Last Modified": space.lastModified,} for space in spaces])

def load_space_files(evt: gr.SelectData):
    clicked_html = evt.value
    space_id = clicked_html.split("data-space='")[1].split("'")[0]
    files = api.list_repo_files(repo_id=space_id, repo_type="space", token=HF_TOKEN)
    file_df = pd.DataFrame([{ "File": f"<a href='https://huggingface.co/spaces/{username}/{space_id.split('/')[-1]}/edit/main/{file}' target='_blank'>{file}</a>"
    } for file in files])
    return file_df

# --- Step 4: Build Gradio interface
    gr.Markdown(f"# Hugging Face Spaces for `{username}`")
    with gr.Row():
        left_df = gr.Dataframe(value=space_df, label="Your Spaces (click a name)",
            interactive=False,  datatype="str", max_rows=len(space_df), wrap=True )
        right_df = gr.Dataframe( value=pd.DataFrame(columns=["File"]),
            label="Files in Selected Space",  interactive=False, wrap=True )

    left_df.select(fn=load_space_files, outputs=right_df)
'''