wrap-up cell

demo/app.py

@@ -12,6 +12,7 @@ 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:
@@ -83,6 +84,7 @@ Think of this interface as a lightweight Jupyter notebook: instead of code cells
         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
 

demo/wrap_up_cell.py

@@ -0,0 +1,36 @@
+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.
+            """
+        )
+