demo/app.py

from __future__ import annotations

import os

import gradio as gr

import health
from layout import CELL_CSS, cell
from problem_cell import render_problem_cell
from solution_cell import render_solution_cell
from setup_cell import render_setup_cell
from context_biased_transcription_cell import render_context_biased_transcription_cell
from media_analysis_cell import render_media_analysis_cell
from translation_cell import render_translation_cell
from wrap_up_cell import render_wrap_up_cell


def render_health_panel(gemini_api_key: str | None = None) -> str:
    return health.render_health_notice(gemini_api_key)


def create_app() -> gr.Blocks:
    """Create the Gradio Blocks application used in the Hugging Face Space.

    The layout is intentionally notebook-like: each conceptual unit
    (problem, health, demos, wrap-up) is encapsulated in its own module
    and rendered as a "cell" to keep the main app glue straightforward.
    """
    with gr.Blocks(title="Aileen3 Demo") as demo:
        gr.HTML(f"<style>{CELL_CSS}</style>")

        with cell("📘 Introduction"):
            gr.Markdown(
                """
# Aileen 3 Core
<div style="display: flex; justify-content: center; gap: 10px;">
<a href="https://ndurner.de/links/aileen3-linkedin">
  <img alt="LinkedIn post" src="https://img.shields.io/badge/🔗 LinkedIn-Post-blue?logo=linkedin">
</a>
<a href="https://youtu.be/r56najKVS4I">
  <img alt="Demo video" src="https://img.shields.io/badge/YouTube-MCP%20demo%20video-red?logo=youtube">
</a>
</div>

Large Language Models (LLMs) rely on tools – sometimes provided by MCP servers – to interact with the outside world.
Aileen 3 Core is an MCP server focused on **Information Foraging**: mining high-noise sources for novel insights and turning
them into dense briefings you can consume quickly. Grounded in cognitive science, Aileen 3 models novelty as prediction error against
explicit priors such as user expectations, facts from an AI Memory Bank, or media context.

> **"Information is surprises. You learn something when things don’t turn out the way you expected."** ⸺ Roger Schank

To that end, the Aileen 3 Core MCP server provides media access and analysis services backed by Google Gemini.

This Space shows a small, notebook-style slice of that system so you can verify the core MCP server is reachable and working.

Today, Aileen 3 Core is a contender in the **[MCP's 1st Birthday – Hosted by Anthropic and Gradio](https://huggingface.co/MCP-1st-Birthday)**
hackathon. It also powers the **[Aileen 3 Agent](https://github.com/ndurner/aileen3-agent)** project, a
[capstone project](https://ndurner.de/links/aileen3-kaggle-writeup) for the
[*AI Agents Intensive Course with Google*](https://www.kaggle.com/learn-guide/5-day-agents). Its predecessor, Aileen 2, explored autonomous
personalized summarization of German parliament proceedings and won Honorable Mention in the
[Generative AI Agents Developer Contest by NVIDIA and LangChain](https://www.nvidia.com/en-in/ai-data-science/generative-ai/developer-contest-with-langchain/).

For more details on setup, usage, and background, see the full project README. Aileen 3 Core has been tested with Claude Desktop and Aileen 3 Agent.
                """
            )

        with cell("🧭 How this demo is organized"):
            gr.Markdown(
                """
Think of this interface as a lightweight Jupyter notebook: instead of code cells, you get a sequence of interactive “cells” that walk through key aspects of the Aileen 3 Core MCP server.

- The **Introduction** cell gives you the high-level context from the README.
- The **Health check** cell below connects to the MCP server used by the Space and shows whether it can be started successfully.
- Future cells will demonstrate the problem statement and core information-foraging capabilities.
                """
            )

        render_problem_cell()

        render_solution_cell()

        gemini_key_box = render_setup_cell()

        with cell("👩🏻‍⚕️ Health check"):
            health_panel = gr.HTML(value=health.render_placeholder_notice())
            run_button = gr.Button("Run health check", variant="primary")
            run_button.click(
                fn=render_health_panel,
                inputs=gemini_key_box,
                outputs=health_panel,
                queue=False,
            )

        render_context_biased_transcription_cell(gemini_key_box)
        render_media_analysis_cell(gemini_key_box)
        render_translation_cell(gemini_key_box)
        render_wrap_up_cell()

    return demo


def main() -> None:
    app = create_app()
    server_name = os.environ.get("GRADIO_SERVER_NAME") or os.environ.get("HOST") or "0.0.0.0"
    port_value = os.environ.get("GRADIO_SERVER_PORT") or os.environ.get("PORT") or "7860"
    try:
        server_port = int(port_value)
    except ValueError:
        server_port = 7860
    app.launch(
        server_name=server_name,
        server_port=server_port,
    )


if __name__ == "__main__":
    main()