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from __future__ import annotations
import os
import socket
from typing import Dict, List
import gradio as gr
from .constraints import ConstraintDefinition
from .services import PatchService
def create_app(service: PatchService | None = None) -> gr.Blocks:
"""Create and configure the Gradio application."""
patch_service = service or PatchService.from_data_root()
audio_choices = patch_service.audio_ids()
with gr.Blocks(title="BLT Patching Visualization", theme=gr.themes.Default()) as app:
gr.Markdown("# BLT Patching Visualization Explorer")
gr.Markdown("Explore entropy-based patching strategies for Byte Latent Transformers")
patches_state = gr.State([])
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("## Audio Selection")
audio_dropdown = gr.Dropdown(
choices=audio_choices,
value=audio_choices[0] if audio_choices else None,
label="Audio File",
interactive=True,
)
file_info = gr.Markdown()
gr.Markdown("## Patching Parameters")
metric_radio = gr.Radio(
choices=["Entropy", "Perplexity"],
value="Entropy",
label="Metric",
info="Underlying metric for patching decisions",
)
gr.Markdown("**Constraints** (select one or more)")
gr.Markdown("_Patch boundary created when ANY constraint is satisfied (OR logic)_")
constraint_defs: List[ConstraintDefinition] = list(
patch_service.constraint_definitions()
)
constraint_choices = [(definition.label, definition.name) for definition in constraint_defs]
constraint_selector = gr.CheckboxGroup(
choices=constraint_choices,
value=["global"],
label="Active Constraints",
info="Select multiple constraints to combine with OR logic",
)
guard_config = patch_service.guard_slider_config("entropy")
min_metric_slider = gr.Slider(
value=guard_config.default,
minimum=guard_config.minimum,
maximum=guard_config.maximum,
step=guard_config.step,
label=guard_config.label,
info=guard_config.description,
)
gr.Markdown("_Guard constraint uses AND logic with all selected constraints_")
threshold_inputs: Dict[str, gr.Slider] = {}
for definition in constraint_defs:
range_cfg = definition.range_for_metric("entropy")
threshold_inputs[definition.name] = gr.Slider(
value=range_cfg.default,
minimum=range_cfg.minimum,
maximum=range_cfg.maximum,
step=range_cfg.step,
label=f"{definition.label} Threshold",
info=definition.description,
visible=(definition.name == "global"),
)
threshold_names = list(threshold_inputs.keys())
apply_btn = gr.Button("Apply Patching", variant="primary", size="lg")
with gr.Column(scale=1):
stats_output = gr.Markdown("Click 'Apply Patching' to see statistics")
gr.Markdown("## Visualization")
plot_output = gr.Plot(label="Patch Visualization")
gr.Markdown("## Audio Playback & Comparison")
with gr.Tab("Original vs Reconstructed"):
gr.Markdown(
"**Compare original audio with WavTokenizer reconstruction** "
"(listen for compression artifacts)"
)
with gr.Row():
original_audio = gr.Audio(label="Original Audio", type="numpy")
reconstructed_audio = gr.Audio(label="WavTokenizer Reconstructed", type="numpy")
with gr.Tab("Play Until Boundary"):
gr.Markdown(
"**Play audio from a token position until the next patch boundary** "
"(hear what's in a single patch)"
)
with gr.Row():
current_token_input = gr.Number(
value=0,
label="Token Position",
precision=0,
info="Enter token index (0 to num_tokens-1)",
)
play_boundary_btn = gr.Button("▶ Play Until Next Boundary", variant="secondary")
patch_info_output = gr.Markdown()
boundary_audio = gr.Audio(label="Patch Audio", type="numpy", autoplay=True)
# ------------------------------------------------------------------
# Callbacks
# ------------------------------------------------------------------
def _update_file_info(file_id: str):
return patch_service.file_info_markdown(file_id)
def _update_threshold_visibility(selected: List[str]):
return {
threshold_inputs[name]: gr.update(visible=(name in selected))
for name in threshold_inputs
}
def _update_threshold_ranges(metric_name: str):
metric_defaults = patch_service.threshold_defaults(metric_name)
guard = patch_service.guard_slider_config(metric_name)
updates = {
min_metric_slider: gr.update(
value=guard.default,
minimum=guard.minimum,
maximum=guard.maximum,
step=guard.step,
label=guard.label,
info=guard.description,
)
}
for definition in constraint_defs:
params = definition.range_for_metric(metric_name)
updates[threshold_inputs[definition.name]] = gr.update(
value=metric_defaults[definition.name],
minimum=params.minimum,
maximum=params.maximum,
step=params.step,
)
return updates
def _handle_patching(file_id, metric_name, selected_constraints, min_threshold, *slider_values):
if not file_id:
return None, "No file selected", None, None, []
thresholds = {name: float(slider_values[idx]) for idx, name in enumerate(threshold_names)}
try:
return patch_service.run_patching(
file_id=file_id,
metric_name=metric_name,
selected_constraints=selected_constraints,
guard_threshold=min_threshold,
thresholds=thresholds,
)
except ValueError as exc:
return None, f"⚠️ {exc}", None, None, []
def _handle_play(file_id, current_token, patches):
if not file_id:
return None, "No file selected", 0
if not patches:
return None, "No patches computed yet - click 'Apply Patching' first", current_token
try:
return patch_service.play_until_boundary(file_id, int(current_token), patches)
except ValueError as exc:
return None, f"⚠️ {exc}", current_token
audio_dropdown.change(fn=_update_file_info, inputs=[audio_dropdown], outputs=[file_info])
constraint_selector.change(
fn=_update_threshold_visibility,
inputs=[constraint_selector],
outputs=list(threshold_inputs.values()),
)
metric_radio.change(
fn=_update_threshold_ranges,
inputs=[metric_radio],
outputs=[min_metric_slider, *threshold_inputs.values()],
)
apply_btn.click(
fn=_handle_patching,
inputs=[
audio_dropdown,
metric_radio,
constraint_selector,
min_metric_slider,
*threshold_inputs.values(),
],
outputs=[plot_output, stats_output, original_audio, reconstructed_audio, patches_state],
)
play_boundary_btn.click(
fn=_handle_play,
inputs=[audio_dropdown, current_token_input, patches_state],
outputs=[boundary_audio, patch_info_output, current_token_input],
)
app.load(fn=_update_file_info, inputs=[audio_dropdown], outputs=[file_info])
return app
def _find_open_port(preferred: int, attempts: int = 50) -> int:
"""Find an available TCP port starting from a preferred value."""
if preferred <= 0:
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
try:
sock.bind(("127.0.0.1", 0))
return sock.getsockname()[1]
except OSError:
return 7860
port = preferred
for _ in range(attempts):
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
try:
sock.bind(("127.0.0.1", port))
except OSError:
port += 1
continue
return port
raise OSError(f"Could not find open port starting at {preferred}")
def main() -> None:
"""CLI entry point for running the Gradio app."""
app = create_app()
port_env = os.getenv("GRADIO_SERVER_PORT")
try:
requested_port = int(port_env) if port_env is not None else 7860
except ValueError:
requested_port = 7860
try:
port = _find_open_port(requested_port)
except OSError as exc:
raise SystemExit(f"Failed to locate an open port near {requested_port}: {exc}") from exc
launch_kwargs = dict(server_name="0.0.0.0", share=True, debug=True, server_port=port)
print(f"Open in browser: http://localhost:{port}")
app.launch(**launch_kwargs)
# Gradio Spaces expects a variable named `demo`
demo = create_app()
if __name__ == "__main__":
main()
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