Update hub_ui.py

hub_ui.py

@@ -4,14 +4,33 @@ from queue import Empty, Queue
 
 import streamlit as st
 
-from hub_models import build_plan, discover_models, download_model, format_size, friendly_hub_error
+from hub_models import build_plan, discover_models, download_model, format_size, friendly_hub_error, check_hosted_download
 from hub_models import CACHE_ROOT
 from app_environment import hosted_mode, hosted_download_limit, new_session_directory, PRIVATE_CACHE_ROOT
 
 
+MODEL_PRESETS = {
+    'SmolLM2 135M · small & quick': ('HuggingFaceTB/SmolLM2-135M', 'About 271 MB · English · safetensors'),
+    'GPT-2 · classic English model': ('openai-community/gpt2', 'About 549 MB · English · safetensors'),
+    'Gemma 3 1B · Q4 GGUF': (
+        'https://huggingface.co/lmstudio-community/gemma-3-1B-it-qat-GGUF/blob/main/gemma-3-1B-it-QAT-Q4_0.gguf',
+        'About 720 MB · quantized GGUF'),
+    'Qwen3 0.6B · multilingual': ('Qwen/Qwen3-0.6B', 'About 1.52 GB · multilingual · safetensors'),
+}
+
+
 def render_hub_loader(activate_model):
-    st.caption('Paste a model repository ID or Hugging Face URL, then choose its weights.')
-    repo = st.text_input('Hugging Face repository or URL', placeholder='owner/model or https://huggingface.co/owner/model', key='hf_repo')
+    st.caption('Choose a suggested model or paste a Hugging Face link. Weights download directly to this Space.'
+               if hosted_mode() else 'Choose a suggested model or paste a Hugging Face link. Weights download to this computer.')
+    source = st.radio('Model source', ['Choose from list', 'Paste URL or repository ID'], horizontal=True, key='hf_source')
+    if source == 'Choose from list':
+        preset = st.selectbox('Suggested model', list(MODEL_PRESETS), key='hf_preset')
+        repo, description = MODEL_PRESETS[preset]
+        st.caption(description + ' · downloaded on first use; cached copies are reused')
+    else:
+        repo = st.text_input('Hugging Face repository or URL', placeholder='owner/model or https://huggingface.co/owner/model', key='hf_repo')
+    if hosted_mode():
+        st.caption(f'Up to {format_size(hosted_download_limit())} per model, subject to free storage. Larger vocabularies take longer to search.')
     with st.expander('Revision & access token (optional)'):
         revision = st.text_input('Revision', placeholder='main, branch, tag or commit', key='hf_revision')
         token = st.text_input('Hugging Face read token', type='password', key='hf_token',
@@ -57,11 +76,13 @@ def render_hub_loader(activate_model):
     with st.expander('Files to download'):
         for filename in plan.files:
             st.text(f'{filename} · {format_size(catalog.files.get(filename))}')
-    limit = hosted_download_limit()
-    if limit is not None and (plan.size is None or plan.size > limit):
-        st.info('This hosted Space supports downloads up to 4 GB with a known size. Choose smaller weights, upload embedding shards, or run the app locally for larger models.')
+    try:
+        check_hosted_download(plan)
+    except (ValueError, OSError) as error:
+        st.info(str(error))
         return
-    if st.button('Download & load', key='hf_download', type='primary', use_container_width=True):
+    load_label = 'Load into Space' if hosted_mode() else 'Download & load'
+    if st.button(load_label, key='hf_download', type='primary', use_container_width=True):
         events = Queue()
         progress = st.progress(0, text='Preparing download…')
         try: