Update app_demo.py

app_demo.py

@@ -1,101 +1,9 @@
-import gradio as gr
 from huggingface_hub import HfApi, snapshot_download
 from concurrent.futures import ThreadPoolExecutor
 import asyncio
 import ast
 import os
-
-api = HfApi()
-executor = ThreadPoolExecutor()
-model_cache = {}
-
-def validate_and_list_models(hfuser):
-    try:
-        models = api.list_models(author=hfuser)
-        return [model.modelId for model in models if model.pipeline_tag == "text-to-image"]
-    except Exception:
-        return []
-
-def parse_user_model_dict(user_model_dict_str):
-    try:
-        data = ast.literal_eval(user_model_dict_str)
-        if isinstance(data, dict) and all(isinstance(v, list) for v in data.values()):
-            return data
-        return {}
-    except Exception:
-        return {}
-
-def load_model(model_id):
-    if model_id in model_cache:
-        return f"{model_id} loaded from cache"
-    try:
-        path = snapshot_download(repo_id=model_id, cache_dir="model_cache", token=os.getenv("HF_TOKEN"))
-        model_cache[model_id] = path
-        return f"{model_id} loaded successfully"
-    except Exception as e:
-        return f"{model_id} failed to load: {str(e)}"
-
-def run_models(models, parallel):
-    if parallel:
-        futures = [executor.submit(load_model, m) for m in models]
-        return [f.result() for f in futures]
-    else:
-        return [load_model(m) for m in models]
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column(scale=1):
-            with gr.Row():
-                hfuser_input = gr.Textbox(label="Hugging Face Username")
-                hfuser_models = gr.Dropdown(label="Models from User", choices=[], multiselect=True)
-                user_model_dict = gr.Textbox(visible=False, label="Dict Input (e.g., {'username': ['model1', 'model2']})")
-            with gr.Row():
-                run_btn = gr.Button("Load Models")
-        with gr.Column(scale=3):        
-            with gr.Row():
-                parallel_toggle = gr.Checkbox(label="Load in Parallel", value=True)
-            with gr.Row():    
-                output = gr.Textbox(label="Output", lines=3)
-    with gr.Row():    
-        gr.HTML(
-            f"""
-            <p id="project-links" align="center">
-                <a href='https://huggingface.co/spaces/charliebaby2023/Fast_Stable_diffusion_CPU/edit/main/app_demo.py'>Edit this app_demo py file</a>
-                <p> this is currently running the following model array </p>
-                
-            </p>
-            """
-        )
-        
-    def update_models(hfuser):
-        if hfuser:
-            models = validate_and_list_models(hfuser)
-            label = f"Models found: {len(models)}" 
-        else:
-            models = ''
-            label = ''
-        return gr.update(choices=models,  label=label)
-
-    def update_from_dict(dict_str):
-        parsed = parse_user_model_dict(dict_str)
-        if not parsed:
-            return gr.update(), gr.update()
-        hfuser = next(iter(parsed))
-        models = parsed[hfuser]
-        label = f"Models found: {len(models)}" 
-        return gr.update(value=hfuser), gr.update(choices=models, value=models,  label=label)
-        #return gr.update(value=hfuser), gr.update(choices=parsed[hfuser], value=parsed[hfuser])
-
-    hfuser_input.change(update_models, hfuser_input, hfuser_models)
-    user_model_dict.change(update_from_dict, user_model_dict, [hfuser_input, hfuser_models])
-    run_btn.click(run_models, [hfuser_models, parallel_toggle], output)
-
-demo.launch()
-
-
-'''#!/usr/bin/env python
 from __future__ import annotations
-import os
 import random
 import time
 import gradio as gr
@@ -103,7 +11,6 @@ import numpy as np
 import PIL.Image
 import torch
 from diffusers import StableDiffusionPipeline
-from concurrent.futures import ThreadPoolExecutor
 import uuid
 
 model_id = "Lykon/dreamshaper-xl-v2-turbo"
@@ -112,11 +19,28 @@ DESCRIPTION = '''# Fast Stable Diffusion CPU with Latent Consistency Model
 '''
 if not torch.cuda.is_available():
     DESCRIPTION += "\n<p>running on CPU.</p>"
-
 MAX_SEED = np.iinfo(np.int32).max
 CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
 MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "768"))
 DTYPE = torch.float32
+api = HfApi()
+executor = ThreadPoolExecutor()
+model_cache = {}
+
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+
 
 # Load pipeline once, disabling NSFW filter at construction time
 pipe = StableDiffusionPipeline.from_pretrained(
@@ -196,7 +120,112 @@ examples = [
     "Portrait of a cyborg queen, hyper‑detailed",
 ]
 
-with gr.Blocks(css="style.css") as demo:
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+def validate_and_list_models(hfuser):
+    try:
+        models = api.list_models(author=hfuser)
+        return [model.modelId for model in models if model.pipeline_tag == "text-to-image"]
+    except Exception:
+        return []
+
+def parse_user_model_dict(user_model_dict_str):
+    try:
+        data = ast.literal_eval(user_model_dict_str)
+        if isinstance(data, dict) and all(isinstance(v, list) for v in data.values()):
+            return data
+        return {}
+    except Exception:
+        return {}
+
+def load_model(model_id):
+    if model_id in model_cache:
+        return f"{model_id} loaded from cache"
+    try:
+        path = snapshot_download(repo_id=model_id, cache_dir="model_cache", token=os.getenv("HF_TOKEN"))
+        model_cache[model_id] = path
+        return f"{model_id} loaded successfully"
+    except Exception as e:
+        return f"{model_id} failed to load: {str(e)}"
+
+def run_models(models, parallel):
+    if parallel:
+        futures = [executor.submit(load_model, m) for m in models]
+        return [f.result() for f in futures]
+    else:
+        return [load_model(m) for m in models]
+#with gr.Blocks(css="style.css") as demo:
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column(scale=1):
+            with gr.Row():
+                hfuser_input = gr.Textbox(label="Hugging Face Username")
+                hfuser_models = gr.Dropdown(label="Models from User", choices=[], multiselect=True)
+                user_model_dict = gr.Textbox(visible=False, label="Dict Input (e.g., {'username': ['model1', 'model2']})")
+            with gr.Row():
+                run_btn = gr.Button("Load Models")
+        with gr.Column(scale=3):        
+            with gr.Row():
+                parallel_toggle = gr.Checkbox(label="Load in Parallel", value=True)
+            with gr.Row():    
+                output = gr.Textbox(label="Output", lines=3)
+    with gr.Row():    
+        gr.HTML(
+            f"""
+            <p id="project-links" align="center">
+                <a href='https://huggingface.co/spaces/charliebaby2023/Fast_Stable_diffusion_CPU/edit/main/app_demo.py'>Edit this app_demo py file</a>
+                <p> this is currently running the following model array </p>
+                
+            </p>
+            """
+        )
+        
+    def update_models(hfuser):
+        if hfuser:
+            models = validate_and_list_models(hfuser)
+            label = f"Models found: {len(models)}" 
+        else:
+            models = ''
+            label = ''
+        return gr.update(choices=models,  label=label)
+
+    def update_from_dict(dict_str):
+        parsed = parse_user_model_dict(dict_str)
+        if not parsed:
+            return gr.update(), gr.update()
+        hfuser = next(iter(parsed))
+        models = parsed[hfuser]
+        label = f"Models found: {len(models)}" 
+        return gr.update(value=hfuser), gr.update(choices=models, value=models,  label=label)
+        #return gr.update(value=hfuser), gr.update(choices=parsed[hfuser], value=parsed[hfuser])
+
+    hfuser_input.change(update_models, hfuser_input, hfuser_models)
+    user_model_dict.change(update_from_dict, user_model_dict, [hfuser_input, hfuser_models])
+    run_btn.click(run_models, [hfuser_models, parallel_toggle], output)
+
+
     gr.Markdown(DESCRIPTION)
     with gr.Group():
         with gr.Row():
@@ -232,6 +261,15 @@ with gr.Blocks(css="style.css") as demo:
         cache_examples=CACHE_EXAMPLES,
     )
 
+
+
+demo.launch()
+
+
+'''#!/usr/bin/env python
+
+
+
     demo.queue()
     demo.launch()
 '''