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import os
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if os.environ.get("SPACES_ZERO_GPU") is not None:
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import spaces
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else:
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class spaces:
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@staticmethod
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def GPU(func):
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def wrapper(*args, **kwargs):
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return func(*args, **kwargs)
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return wrapper
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import gradio as gr
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import subprocess
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from huggingface_hub import HfApi
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@spaces.GPU
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def infer(filter: str, sort: str, sort_dir: bool, infer: str, gated: str, appr: list[str]):
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try:
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api = HfApi()
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kwargs = {}
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if filter: kwargs["filter"] = filter
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if gated == "gated": kwargs["gated"] = True
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elif gated == "non-gated": kwargs["gated"] = False
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if sort_dir: kwargs["direction"] = -1
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models = api.list_models(inference=infer, sort=sort, cardData=True, **kwargs)
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md = "### Results:\n"
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for model in models:
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if model.gated and model.gated not in appr: continue
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md += "1. "
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md += f"[{model.id}](https://hf.co/{model.id})"
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md += f" Inference: '{infer}'"
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md += f" Gated: '{gated}'"
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if model.library_name: md += f" Lib:'{model.library_name}'"
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if model.pipeline_tag: md += f" Pipeline:'{model.pipeline_tag}'"
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if model.last_modified: md += f" LastMod:'{model.last_modified}'"
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if model.likes: md += f" Likes:'{model.likes}'"
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if model.downloads: md += f" DLs:'{model.downloads}'"
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if model.downloads_all_time: md += f" AllDLs:'{model.downloads_all_time}'"
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md += "\n"
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return md
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except Exception as e:
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raise gr.Error(e)
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with gr.Blocks() as demo:
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filter = gr.Textbox(label="Query", value="")
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with gr.Row(equal_height=True):
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infer_status = gr.Radio(label="Inference status", choices=["warm", "cold", "frozen"], value="warm")
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gated_status = gr.Radio(label="Gated status", choices=["gated", "non-gated", "all"], value="non-gated")
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sort = gr.Radio(label="Sort", choices=["last_modified", "likes", "downloads"], value="likes")
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sort_dir = gr.Checkbox(label="Sort by descending order", value=False)
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appr_status = gr.CheckboxGroup(label="Approval method", choices=["auto", "manual"], value=["auto", "manual"], visible=False)
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run_button = gr.Button("Search", variant="primary")
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output_md = gr.Markdown("<br><br>")
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run_button.click(infer, [filter, sort, sort_dir, infer_status, gated_status, appr_status], [output_md])
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demo.launch()
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