app.py

import gradio as gr
import inspect
import typing
from typing import get_type_hints

from academia_mcp.tools import (
    arxiv_search,
    arxiv_download,
    s2_citations,
    hf_datasets_search,
    anthology_search,
)


def infer_gradio_interface(func):
    """
    Automatically infer Gradio interface parameters from function signature
    and docstring.
    """
    # Get function signature
    sig = inspect.signature(func)
    type_hints = get_type_hints(func)

    # Parse docstring
    docstring = inspect.getdoc(func) or ""

    # Extract title and description
    title = func.__name__.replace("_", " ").title()
    # Use the full docstring as description, convert newlines to HTML breaks
    if docstring.strip():
        description = docstring.strip().replace("\n", "<br>")
    else:
        description = f"Interface for {func.__name__}"

    # Infer inputs
    inputs = []
    for param_name, param in sig.parameters.items():
        param_type = type_hints.get(param_name, str)

        # Get default value if available
        default_value = None
        if param.default is not inspect.Parameter.empty:
            default_value = param.default

        label = param_name.replace("_", " ").title()

        if param_type == str or param_type == typing.Optional[str]:
            inputs.append(
                gr.Textbox(
                    label=label,
                    placeholder=f"Enter {param_name}",
                    value=default_value if default_value is not None else "",
                )
            )
        elif param_type == int or param_type == typing.Optional[int]:
            inputs.append(
                gr.Number(
                    label=label,
                    precision=0,
                    value=default_value if default_value is not None else 0,
                )
            )
        elif param_type == float or param_type == typing.Optional[float]:
            inputs.append(
                gr.Number(
                    label=label,
                    value=default_value if default_value is not None else 0.0,
                )
            )
        elif param_type == bool or param_type == typing.Optional[bool]:
            inputs.append(
                gr.Checkbox(
                    label=label,
                    value=default_value if default_value is not None else False,
                )
            )
        else:
            # Default to textbox for unknown types
            inputs.append(
                gr.Textbox(
                    label=label,
                    value=(str(default_value) if default_value is not None else ""),
                )
            )

    # Infer outputs
    return_type = type_hints.get("return", str)
    if return_type == str:
        outputs = gr.Textbox(label="Result")
    elif return_type == int:
        outputs = gr.Number(label="Result", precision=0)
    elif return_type == float:
        outputs = gr.Number(label="Result")
    elif return_type == list:
        outputs = gr.JSON(label="Results")
    elif return_type == dict:
        outputs = gr.JSON(label="Result")
    else:
        # Default to textbox for unknown return types
        outputs = gr.Textbox(label="Result")

    return {
        "fn": func,
        "inputs": inputs,
        "outputs": outputs,
        "title": title,
        "description": description,
    }


arxiv_search_interface = infer_gradio_interface(arxiv_search)
arxiv_download_interface = infer_gradio_interface(arxiv_download)
s2_citations_interface = infer_gradio_interface(s2_citations)
hf_datasets_search_interface = infer_gradio_interface(hf_datasets_search)
anthology_search_interface = infer_gradio_interface(anthology_search)

search_demo = gr.Interface(**arxiv_search_interface)
download_demo = gr.Interface(**arxiv_download_interface)
s2_citations_demo = gr.Interface(**s2_citations_interface)
hf_datasets_search_demo = gr.Interface(**hf_datasets_search_interface)
anthology_search_demo = gr.Interface(**anthology_search_interface)

demo = gr.TabbedInterface(
    [
        search_demo,
        download_demo,
        s2_citations_demo,
        hf_datasets_search_demo,
        anthology_search_demo,
    ],
    [
        "ArXiv Search",
        "ArXiv Download",
        "S2 Citations",
        "HF Datasets Search",
        "Anthology Search",
    ],
    title="Academia MCP Tools",
)

if __name__ == "__main__":
    demo.launch(mcp_server=True)