Update app.py

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 from huggingface_hub import HfApi, whoami
 from config import howManyModelsToUse,num_models,max_images,inference_timeout,MAX_SEED,thePrompt,preSetPrompt,negPreSetPrompt
-from all_models import models as default_models
+from all_models import models 
 import asyncio
 import os
 import pandas as pd
@@ -10,20 +10,13 @@ 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
+
+default_models = models[:num_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
-
-
-
-
-
-
-
-
-
 def extend_choices(choices):
     return choices[:num_models] + (num_models - len(choices[:num_models])) * ['NA']
 def update_imgbox(choices):
@@ -33,8 +26,15 @@ def random_choices():
     import random
     random.seed()
     return random.choices(models, k=num_models)
-
-
+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)
 
 
 
@@ -49,7 +49,14 @@ load_fn(default_models,HF_TOKEN)
  
 '''
 
+
+
 with gr.Blocks(fill_width=True) as demo:
+    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)
     with gr.Tab(str(num_models) + ' Models'):
         with gr.Column(scale=2):
             with gr.Group():