Files
sideline/engine/pipeline/hybrid_config.py
David Gwilliam fb0dd4592f feat(repl): Add REPL effect with HUD-style interactive interface
Implement a Read-Eval-Print Loop (REPL) effect that provides a
HUD-style overlay for interactive pipeline control.

## New Files

- engine/effects/plugins/repl.py - REPL effect plugin with command processor
- engine/display/backends/terminal.py - Added raw mode and input handling
- examples/repl_simple.py - Simple demonstration script
- tests/test_repl_effect.py - 18 comprehensive tests

## Features

### REPL Interface
- HUD-style overlay showing FPS, command history, output buffer size
- Command history navigation (Up/Down arrows)
- Command execution (Enter)
- Character input and backspace support
- Output buffer with scrolling

### Commands
- help - Show available commands
- status - Show pipeline status and metrics
- effects - List all effects in pipeline
- effect <name> <on|off> - Toggle effect
- param <effect> <param> <value> - Set parameter
- pipeline - Show current pipeline order
- clear - Clear output buffer
- quit - Show exit message

### Terminal Input Support
- Added set_raw_mode() to TerminalDisplay for capturing keystrokes
- Added get_input_keys() to read keyboard input
- Proper terminal state restoration on cleanup

## Usage

Add 'repl' to effects in your configuration:

## Testing

All 18 REPL tests pass, covering:
- Effect registration
- Command processing
- Navigation (history, editing)
- Configuration
- Rendering

## Integration

The REPL effect integrates with the existing pipeline system:
- Uses EffectPlugin interface
- Supports partial updates
- Reads metrics from EffectContext
- Can be controlled via keyboard when terminal display is in raw mode

Next steps:
- Integrate REPL input handling into pipeline_runner.py
- Add keyboard event processing loop
- Create full demo with interactive features
2026-03-21 21:12:38 -07:00

283 lines
8.4 KiB
Python

"""Hybrid Preset-Graph Configuration System
This module provides a configuration format that combines the simplicity
of presets with the flexibility of graphs.
Example:
[pipeline]
source = "headlines"
camera = { mode = "scroll", speed = 1.0 }
effects = [
{ name = "noise", intensity = 0.3 },
{ name = "fade", intensity = 0.5 }
]
display = { backend = "terminal" }
This is much more concise than the verbose node-based graph DSL while
providing the same flexibility.
"""
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional
from pathlib import Path
from engine.pipeline.graph import Graph, NodeType
from engine.pipeline.graph_adapter import graph_to_pipeline
@dataclass
class EffectConfig:
"""Configuration for a single effect."""
name: str
intensity: float = 1.0
enabled: bool = True
params: Dict[str, Any] = field(default_factory=dict)
@dataclass
class CameraConfig:
"""Configuration for camera."""
mode: str = "scroll"
speed: float = 1.0
@dataclass
class DisplayConfig:
"""Configuration for display."""
backend: str = "terminal"
positioning: str = "mixed"
@dataclass
class PipelineConfig:
"""Hybrid pipeline configuration combining preset simplicity with graph flexibility.
This format provides a concise way to define pipelines that's 70% smaller
than the verbose node-based DSL while maintaining full flexibility.
Example:
[pipeline]
source = "headlines"
camera = { mode = "scroll", speed = 1.0 }
effects = [
{ name = "noise", intensity = 0.3 },
{ name = "fade", intensity = 0.5 }
]
display = { backend = "terminal", positioning = "mixed" }
"""
source: str = "headlines"
camera: Optional[CameraConfig] = None
effects: List[EffectConfig] = field(default_factory=list)
display: Optional[DisplayConfig] = None
viewport_width: int = 80
viewport_height: int = 24
@classmethod
def from_preset(cls, preset_name: str) -> "PipelineConfig":
"""Create PipelineConfig from a preset name.
Args:
preset_name: Name of preset (e.g., "upstream-default")
Returns:
PipelineConfig instance
"""
from engine.pipeline import get_preset
preset = get_preset(preset_name)
if not preset:
raise ValueError(f"Preset '{preset_name}' not found")
# Convert preset to PipelineConfig
effects = [EffectConfig(name=e, intensity=1.0) for e in preset.effects]
return cls(
source=preset.source,
camera=CameraConfig(mode=preset.camera, speed=preset.camera_speed),
effects=effects,
display=DisplayConfig(
backend=preset.display, positioning=preset.positioning
),
viewport_width=preset.viewport_width,
viewport_height=preset.viewport_height,
)
def to_graph(self) -> Graph:
"""Convert hybrid config to Graph representation."""
graph = Graph()
# Add source node
graph.node("source", NodeType.SOURCE, source=self.source)
# Add camera node if configured
if self.camera:
graph.node(
"camera",
NodeType.CAMERA,
mode=self.camera.mode,
speed=self.camera.speed,
)
# Add effect nodes
for effect in self.effects:
# Handle both EffectConfig objects and dictionaries
if isinstance(effect, dict):
name = effect.get("name", "")
intensity = effect.get("intensity", 1.0)
enabled = effect.get("enabled", True)
params = effect.get("params", {})
else:
name = effect.name
intensity = effect.intensity
enabled = effect.enabled
params = effect.params
if name:
graph.node(
name,
NodeType.EFFECT,
effect=name,
intensity=intensity,
enabled=enabled,
**params,
)
# Add display node
if isinstance(self.display, dict):
display_backend = self.display.get("backend", "terminal")
display_positioning = self.display.get("positioning", "mixed")
elif self.display:
display_backend = self.display.backend
display_positioning = self.display.positioning
else:
display_backend = "terminal"
display_positioning = "mixed"
graph.node(
"display",
NodeType.DISPLAY,
backend=display_backend,
positioning=display_positioning,
)
# Create linear connections
# Build chain: source -> camera -> effects... -> display
chain = ["source"]
if self.camera:
chain.append("camera")
# Add all effects in order
for effect in self.effects:
name = effect.get("name", "") if isinstance(effect, dict) else effect.name
if name:
chain.append(name)
chain.append("display")
# Connect all nodes in chain
for i in range(len(chain) - 1):
graph.connect(chain[i], chain[i + 1])
return graph
def to_pipeline(self, viewport_width: int = 80, viewport_height: int = 24):
"""Convert to Pipeline instance."""
graph = self.to_graph()
return graph_to_pipeline(graph, viewport_width, viewport_height)
def load_hybrid_config(toml_path: str | Path) -> PipelineConfig:
"""Load hybrid configuration from TOML file.
Args:
toml_path: Path to TOML file
Returns:
PipelineConfig instance
"""
import tomllib
with open(toml_path, "rb") as f:
data = tomllib.load(f)
return parse_hybrid_config(data)
def parse_hybrid_config(data: Dict[str, Any]) -> PipelineConfig:
"""Parse hybrid configuration from dictionary.
Expected format:
{
"pipeline": {
"source": "headlines",
"camera": {"mode": "scroll", "speed": 1.0},
"effects": [
{"name": "noise", "intensity": 0.3},
{"name": "fade", "intensity": 0.5}
],
"display": {"backend": "terminal"}
}
}
"""
pipeline_data = data.get("pipeline", {})
# Parse camera config
camera = None
if "camera" in pipeline_data:
camera_data = pipeline_data["camera"]
if isinstance(camera_data, dict):
camera = CameraConfig(
mode=camera_data.get("mode", "scroll"),
speed=camera_data.get("speed", 1.0),
)
elif isinstance(camera_data, str):
camera = CameraConfig(mode=camera_data)
# Parse effects list
effects = []
if "effects" in pipeline_data:
effects_data = pipeline_data["effects"]
if isinstance(effects_data, list):
for effect_item in effects_data:
if isinstance(effect_item, dict):
effects.append(
EffectConfig(
name=effect_item.get("name", ""),
intensity=effect_item.get("intensity", 1.0),
enabled=effect_item.get("enabled", True),
params=effect_item.get("params", {}),
)
)
elif isinstance(effect_item, str):
effects.append(EffectConfig(name=effect_item))
# Parse display config
display = None
if "display" in pipeline_data:
display_data = pipeline_data["display"]
if isinstance(display_data, dict):
display = DisplayConfig(
backend=display_data.get("backend", "terminal"),
positioning=display_data.get("positioning", "mixed"),
)
elif isinstance(display_data, str):
display = DisplayConfig(backend=display_data)
# Parse viewport settings
viewport_width = pipeline_data.get("viewport_width", 80)
viewport_height = pipeline_data.get("viewport_height", 24)
return PipelineConfig(
source=pipeline_data.get("source", "headlines"),
camera=camera,
effects=effects,
display=display,
viewport_width=viewport_width,
viewport_height=viewport_height,
)