app.py

import gradio as gr

# --- Grey-Box V3 Universal：每一層的說明（簡化版，可之後自行替換成完整版） ---

LAYER_DESCRIPTIONS = {
    "1. Semantic Nodes": """
**Layer 1 — Semantic Nodes**

Semantic nodes are context-conditioned meaning units derived from token embeddings,
positional encodings and local interactions. They are not just words, but
high-dimensional vectors that represent micro-concepts under the current context.
""",
    "2. Attention Mapping": """
**Layer 2 — Attention Mapping**

Attention mapping describes which node looks at which other node, and with what strength.
It forms a directed graph of semantic relevance, and is the structural backbone of reasoning.
""",
    "3. Semantic Flow": """
**Layer 3 — Semantic Flow**

Semantic flow is the emergent propagation of meaning across layers as attention patterns
interact. It is non-linear, multi-directional and can converge or diverge depending on
the prompt and context.
""",
    "4. Weight Heatmap": """
**Layer 4 — Weight Heatmap**

The weight heatmap highlights which nodes or features are considered important.
It combines attention relevance with saliency / gradient-based importance to show
what the model focuses on when making a decision.
""",
    "5. Flow Velocity Field": """
**Layer 5 — Flow Velocity Field**

The velocity field represents the speed and stability of semantic propagation.
Fast convergence tends to correlate with stable reasoning, while turbulence
or oscillation can hint at drift or hallucination.
""",
    "6. Intervention Ring": """
**Layer 6 — Intervention Ring**

The intervention ring is the set of all human-accessible control points:
prompt engineering, constraints, rules, safety policies and bias correction.
It is where external input can reshape internal reasoning behaviour.
""",
    "7. Semantic Field": """
**Layer 7 — Semantic Field (NEW in V3)**

The semantic field is the global meaning landscape inside the model: attractor basins,
drift zones, dense regions of meaning and stability areas. It explains where and how
the model finally collapses its internal state into an output.
""",
}

OVERVIEW_TEXT = """
# Grey-Box Visualization Framework V3.0 (Universal Edition)

This Space is a simple **interactive viewer** for the Grey-Box V3 framework.

Use the selector on the left to explore each of the seven layers:
semantic nodes, attention mapping, semantic flow, weight heatmap,
flow velocity field, intervention ring and the semantic field.

Grey-Box V3 is a **model-agnostic interpretability architecture** for
Transformer-based LLMs. It describes how meaning forms, propagates,
stabilizes and finally collapses into an output.
"""

WHITEPAPER_SNIPPET = """
### Technical Whitepaper (Summary)

Grey-Box V3 introduces a seven-layer semantic reasoning pipeline and
a new global **semantic field** layer, along with cross-layer dynamics.

It is not a tensor-level introspection tool, but a conceptual model that
helps researchers and engineers reason about:
- semantic propagation
- attention-driven structure
- drift and hallucination attractors
- stabilising mechanisms inside LLMs

For the full whitepaper, please refer to the `whitepaper.md` file in the
accompanying dataset / repository.
"""


def show_layer(layer_name: str) -> str:
    """Return markdown description for the selected layer."""
    return LAYER_DESCRIPTIONS.get(layer_name, "No description available.")


with gr.Blocks(title="Grey-Box V3 Viewer") as demo:
    gr.Markdown(OVERVIEW_TEXT)

    with gr.Row():
        with gr.Column(scale=1):
            layer_selector = gr.Radio(
                list(LAYER_DESCRIPTIONS.keys()),
                value="1. Semantic Nodes",
                label="Select a Grey-Box layer"
            )
        with gr.Column(scale=2):
            layer_output = gr.Markdown(
                value=show_layer("1. Semantic Nodes"),
                label="Layer description"
            )

    gr.Markdown(WHITEPAPER_SNIPPET)

    layer_selector.change(fn=show_layer, inputs=layer_selector, outputs=layer_output)

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