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Added HuggingFaceTool to Explore HF Hub #97

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Added HuggingFaceTool to Explore HF Hub #97

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29 changes: 29 additions & 0 deletions src/openagi/actions/tools/huggingface.py
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Original file line number Diff line number Diff line change
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from openagi.actions.base import ConfigurableAction
from pydantic import Field
import json

try:
from huggingface_hub import HfApi
except ImportError:
raise Exception("Install huggingface_hub with cmd `pip install huggingface`")

class HuggingFaceTool(ConfigurableAction):
"""
Tool for exploring Hugging Face models and datasets.
"""
query: str = Field(..., description="Dataset or model name to search on Hugging Face.")

def execute(self) -> str:
api = HfApi()
results_dict = {}

models = api.list_models(search=self.query, limit=15)
datasets = api.list_datasets(search=self.query, limit=15)

for model in models:
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can you also extract other details as well. just model ID is very limited context given to the LLM

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The API returns the following details:

ModelInfo(id='prithivMLmods/Qwen2-VL-Math-Prase-2B-Instruct', author=None, sha=None,
created_at=datetime.datetime(2024, 12, 19, 4, 38, 46, tzinfo=datetime.timezone.utc),
last_modified=None, private=False, disabled=None, downloads=248, downloads_all_time=None,
gated=None, gguf=None, inference=None, likes=8, library_name='transformers',
tags=['transformers', 'safetensors', 'qwen2_vl', 'image-text-to-text', 'conversational', 'en',
'base_model:Qwen/Qwen2-VL-2B-Instruct', 'base_model:finetune:Qwen/Qwen2-VL-2B-Instruct',
'license:apache-2.0', 'text-generation-inference', 'endpoints_compatible', 'region:us'],
pipeline_tag='image-text-to-text', mask_token=None, card_data=None, widget_data=None, model_index=None,
config=None, transformers_info=None, trending_score=8, siblings=None, spaces=None,
safetensors=None, security_repo_status=None)

I think we can add:

  • tags
  • pipeline_tag
  • config
  • transfotmers_info

@tarun-aiplanet please let me know which one's to add and i'll make the changes.

results_dict[model.modelId] = f"https://huggingface.co/{model.modelId}"

for dataset in datasets:
results_dict[dataset.id] = f"https://huggingface.co/datasets/{dataset.id}"

return json.dumps(results_dict)
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