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.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Virtual environment
+venv/
+.venv/
+
+# Distribution
+dist/

LICENSE.md

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+The MIT License (MIT)
+
+Copyright (c) Hugging Face Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+the Software, and to permit persons to whom the Software is furnished to do so,
+subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

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+---
+title: Huggingface Mcp
+emoji: 📈
+colorFrom: green
+colorTo: yellow
+sdk: gradio
+sdk_version: 5.30.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import json
+from typing import Literal
+
+import gradio as gr
+from huggingface_hub import list_models, model_info, hf_hub_download
+
+
+def search_models(
+    search: str = None,
+    library: str = None,
+    tags: str = None,
+    pipeline_tag: str = None,
+    sort: Literal[
+        "trending_score", "last_modified", "created_at", "downloads", "likes"
+    ] = "trending_score",
+    direction: Literal["descending", "ascending"] = "descending",
+    limit: int = 20,
+) -> str:
+    """
+    Search models on Hugging Face Hub.
+
+    Use this tool to search for models by name, tags, or other filters, and to get a list of model IDs.
+    This is the first step when you need to find a specific model before retrieving its details.
+
+    Parameters:
+        search (str, optional): A string to search for in model IDs or names (e.g., "deepseek").
+        library (list[str], optional): List of libraries the models use (e.g., ["pytorch", "tensorflow"]).
+        tags (list[str], optional): List of tags to filter models by (e.g., ["text-generation", "llama"]).
+        pipeline_tag (str, optional): Filter by pipeline tag (e.g., "text-generation").
+        sort (Literal["trending_score", "last_modified", "created_at", "downloads", "likes"], default="trending_score"): Sort models by the specified key.
+        direction (int, default=-1): Sort direction: -1 for descending, 1 for ascending.
+        limit (int, default=20): Maximum number of models to return.
+
+    Returns:
+        list[str]: A list of model IDs matching the search criteria.
+
+    Examples:
+        - To find trending models: search_models(sort="trending_score", limit=10)
+        - To search for models related to "deepseek": search_models(search="deepseek", sort="likes", limit=5)
+        - To filter by tag: search_models(tags=["text-generation"], pipeline_tag="text-generation")
+    """
+    try:
+        library = library.split(",") if library else None
+        tags = tags.split(",") if tags else None
+        direction = -1 if direction == "descending" else 1
+        models = list_models(
+            library=library,
+            tags=tags,
+            search=search,
+            pipeline_tag=pipeline_tag,
+            sort=sort,
+            direction=direction,
+            limit=limit,
+        )
+        return json.dumps([model.modelId for model in models])
+    except Exception as e:
+        return f"Error: {e}"
+
+
+def get_model_info(model_id: str) -> dict:
+    """
+    Get structured metadata about a model on the Hugging Face Hub.
+
+    Use this when you need specific fields like downloads, tags, or other metadata.
+    For comprehensive model information, use `get_model_card`.
+
+    This tool requires the exact model ID, which can be obtained using `search_models`.
+    If you have a partial name or tag, use `search_models` first to find the exact ID.
+
+    Parameters:
+        model_id (str): The exact model ID in the format "organization/model-name" (e.g., "DeepSeek/DeepSeek-R1").
+
+    Returns:
+        dict: A dictionary containing model information including available fields such as:
+            - id: The model ID
+            - author: The author of the model
+            - created_at: The creation date
+            - last_modified: The last modified date
+            - downloads: Number of downloads
+            - likes: Number of likes
+            - tags: List of tags
+            - pipeline_tag: The pipeline tag
+            - library_name: The library name
+            - license: The model license
+            - base_model: The base model (if available)
+            - siblings: List of repository files (if available)
+            - datasets: Datasets used to train the model (if available)
+            - spaces: List of spaces using this model (if available)
+            - xet_enabled: Whether XET is enabled (if available)
+
+    Raises:
+        Exception: If the model_id is invalid or not found. Use search_models to find the correct ID.
+
+    Example:
+        - First, find the model ID: search_models(search="deepseek", sort="likes", limit=1)
+        - Then, get the model info: get_model_info("DeepSeek/DeepSeek-R1")
+    """
+    try:
+        model = model_info(model_id)
+        result = {}
+
+        if hasattr(model, "id") and model.id is not None:
+            result["id"] = model.id
+
+        if hasattr(model, "author") and model.author is not None:
+            result["author"] = model.author
+
+        if hasattr(model, "created_at") and model.created_at is not None:
+            result["created_at"] = model.created_at
+
+        if hasattr(model, "last_modified") and model.last_modified is not None:
+            result["last_modified"] = model.last_modified
+
+        if hasattr(model, "downloads") and model.downloads is not None:
+            result["downloads"] = model.downloads
+
+        if hasattr(model, "likes") and model.likes is not None:
+            result["likes"] = model.likes
+
+        if hasattr(model, "tags") and model.tags is not None:
+            result["tags"] = model.tags
+
+        if hasattr(model, "pipeline_tag") and model.pipeline_tag is not None:
+            result["pipeline_tag"] = model.pipeline_tag
+
+        if hasattr(model, "library_name") and model.library_name is not None:
+            result["library_name"] = model.library_name
+
+        if hasattr(model, "card_data") and model.card_data is not None:
+            if (
+                hasattr(model.card_data, "license")
+                and model.card_data.license is not None
+            ):
+                result["license"] = model.card_data.license
+
+            if (
+                hasattr(model.card_data, "base_model")
+                and model.card_data.base_model is not None
+            ):
+                result["base_model"] = model.card_data.base_model
+
+            if (
+                hasattr(model.card_data, "datasets")
+                and model.card_data.datasets is not None
+            ):
+                result["datasets"] = model.card_data.datasets
+
+        if hasattr(model, "siblings") and model.siblings is not None:
+            result["siblings"] = model.siblings
+
+        if hasattr(model, "spaces") and model.spaces is not None:
+            result["spaces"] = model.spaces
+
+        if hasattr(model, "xet_enabled") and model.xet_enabled is not None:
+            result["xet_enabled"] = model.xet_enabled
+
+        return json.dumps(result)
+    except Exception as e:
+        return f"Error: {e}"
+
+
+def get_model_card(model_id: str) -> str:
+    """
+    Get the complete model card (README.md) for a specific model on Hugging Face Hub.
+
+    Use this when you need comprehensive model documentation including usage examples, model limitations, etc.
+    For only structured metadata, use `get_model_info` instead.
+
+    This tool requires the exact model ID, which can be obtained using `search_models`.
+    If you have a partial name or tag, use `search_models` first to find the exact ID.
+
+    Args:
+        model_id (str): The model ID in the format "organization/model-name" (e.g., "DeepSeek/DeepSeek-R1").
+
+    Returns:
+        str: The markdown content of the model card.
+
+    Example:
+        - First, find the model ID: search_models(search="deepseek", sort="likes", limit=1)
+        - Then, get the model card: get_model_card("DeepSeek/DeepSeek-R1")
+    """
+    try:
+        filepath = hf_hub_download(model_id, "README.md")
+        with open(filepath, "r", encoding="utf-8") as f:
+            content = f.read()
+
+        return content
+    except Exception as e:
+        return f"Error: {e}"
+
+
+search_models = gr.Interface(
+    fn=search_models,
+    inputs=[
+        gr.Textbox(label="search", value=""),
+        gr.Textbox(label="library", value=""),
+        gr.Textbox(label="tags", value=""),
+        gr.Textbox(label="pipeline_tag", value=""),
+        gr.Radio(label="sort", choices=["trending_score", "last_modified", "created_at", "downloads", "likes"], value="trending_score"),
+        gr.Radio(label="direction", choices=["descending", "ascending"], value="descending"),
+        gr.Number(label="limit", value=20),
+    ],
+    outputs="text")
+
+get_model_info = gr.Interface(
+    fn=get_model_info,
+    inputs=[
+        gr.Textbox(label="model_id", value=""),
+    ],
+    outputs="text")
+
+get_model_card = gr.Interface(
+    fn=get_model_card,
+    inputs=[
+        gr.Textbox(label="model_id", value=""),
+    ],
+    outputs="text")
+
+demo = gr.TabbedInterface([search_models, get_model_info, get_model_card], ["search_models", "get_model_info", "get_model_card"])
+demo.launch(mcp_server=True)