from __future__ import annotations

import gradio as gr

from layout import cell


def render_wrap_up_cell() -> None:
    """Render a closing cell that ties the core demos back to the full Aileen 3 stack."""
    with cell("🔚 Conclusion & wrap-up"):
        gr.Markdown(
            """
### 👩🏻‍🏫 What you have seen
This notebook-style Space walked through the core building blocks that **Aileen 3 Core** provides:

- A problem statement cell that made Automatic Speech Recognition (ASR) hallucinations tangible.
- A contextual transcription demo that showed how lightweight priors can already steer ASR models.
- An expectation-driven media analysis cell that turns conference video consumption into surprise hunting using rich priors.
- A slide translation cell that lets you selectively translate only the most informative artefacts.

Together, these pieces form a small but robust MCP server for **information foraging**: instead of passively consuming hours of conference
talks, you provide expectations and questions, and Aileen 3 Core helps you jump straight to the meaningful surprises and exportable assets.

### 🚀 Where this goes next
This Space only shows the core MCP tools in isolation. The full **Aileen 3 Agent** project builds on Aileen 3 Core with the aim to:

- orchestrate retrieval, analysis and follow-up questions,
- plug expectation-driven analysis into agent workflows and memory banks,
- and expose Aileen as a long-running assistant that keeps track of what you already know.

If you are interested in going beyond this demo, the next step is to explore Aileen 3 Agent - our [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) - and wire Aileen 3 Core into your
own MCP-capable client or agent stack.
            """
        )