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use anyhow::{Context, Result, bail};
use crate::cli::*;
use crate::tensor;
/// Handle emotional state tensor commands
pub(crate) fn handle_state(cmd: StateCommands) -> Result<()> {
use std::io::{self, Read as IoRead};
use std::path::PathBuf;
// Helper to load tensor schema by ID or path. The `--schema` argument
// accepts either a schema ID (looked up under `$MX_HOME/state/schemas/`)
// or a direct path to a YAML/JSON schema file.
//
// Path-vs-ID heuristic: IDs may legitimately contain dots (e.g.
// "acme.tensor"), so a bare dot is NOT enough to flip the classification
// to "path". The argument is treated as a path only when it contains a
// slash OR ends with a recognized schema extension (.yaml/.yml/.json).
// Edge case: `--schema my.schema` (no slash, no recognized extension)
// is classified as an ID and routed to `load_by_id` -- callers who mean
// a relative file should use `./my.schema` or include the extension.
let load_tensor_schema = |schema_arg: Option<String>| -> Result<tensor::TensorSchema> {
match schema_arg {
Some(s)
if s.contains('/')
|| s.ends_with(".yaml")
|| s.ends_with(".yml")
|| s.ends_with(".json") =>
{
// Looks like a path
tensor::TensorSchema::load(&PathBuf::from(s))
}
Some(id) => tensor::TensorSchema::load_by_id(&id),
None => tensor::TensorSchema::load_default(),
}
};
match cmd {
// === TENSOR-BASED COMMANDS ===
StateCommands::Encode {
values,
dimensions,
file,
schema,
guided,
format,
runes,
} => {
let schema = load_tensor_schema(schema)?;
let tensor = if guided {
// Interactive guided mode
tensor::guided_capture(&schema)?
} else if let Some(dims_str) = dimensions {
// Parse named dimensions
tensor::StateTensor::parse_named_dimensions(&schema, &dims_str)?
} else if let Some(file_path) = file {
// Read from file
let content = std::fs::read_to_string(&file_path)
.with_context(|| format!("Failed to read file: {}", file_path))?;
// Try pipe-separated first, then newline-separated
let values_str = if content.contains('|') {
content.trim().to_string()
} else {
content
.lines()
.map(|l| l.trim())
.filter(|l| !l.is_empty())
.collect::<Vec<_>>()
.join("|")
};
tensor::StateTensor::parse_values(&schema, &values_str)?
} else if let Some(values_str) = values {
// Parse from argument
tensor::StateTensor::parse_values(&schema, &values_str)?
} else {
// Default tensor
tensor::StateTensor::default_from_schema(&schema)
};
// Output in requested format
match format.as_str() {
"json" => println!("{}", serde_json::to_string_pretty(&tensor)?),
"human" => {
println!("{}", tensor.describe(&schema));
if let Some((mood_name, mood, distance)) = tensor.nearest_mood(&schema) {
println!("\nNearest mood: {} (distance: {:.3})", mood_name, distance);
println!(" {}", mood.description);
}
}
"bootstrap" => {
// Self-documenting bootstrap format
println!("{}", tensor.format_bootstrap(&schema)?);
}
_ => {
// tensor format
if runes {
println!("{}", tensor.encode_with_runes(&schema));
} else {
println!("{}", tensor.encode());
}
}
}
}
StateCommands::Decode {
input,
schema,
format,
} => {
// Get input from arg or stdin
let input_str = match input {
Some(s) => s,
None => {
let mut buf = String::new();
io::stdin().read_to_string(&mut buf)?;
buf.trim().to_string()
}
};
// Decode the tensor (schema ID is embedded in the string)
let tensor = tensor::StateTensor::decode(&input_str)?;
// Load schema (use argument if provided, otherwise use ID from tensor)
let schema = match schema {
Some(s) => load_tensor_schema(Some(s))?,
None => tensor::TensorSchema::load_by_id(&tensor.schema_id)?,
};
match format.as_str() {
"json" => println!("{}", serde_json::to_string_pretty(&tensor)?),
"tensor" => println!("{}", tensor.encode()),
"mood" => {
if let Some((mood_name, mood, distance)) = tensor.nearest_mood(&schema) {
println!("{}", mood_name);
println!(" Description: {}", mood.description);
println!(" Distance: {:.3}", distance);
} else {
println!("(unnamed region)");
}
}
_ => {
// human format
println!("{}", tensor.describe(&schema));
if let Some((mood_name, mood, distance)) = tensor.nearest_mood(&schema) {
println!("\nNearest mood: {} (distance: {:.3})", mood_name, distance);
println!(" {}", mood.description);
}
}
}
}
StateCommands::Schemas { json } => {
let schemas = tensor::TensorSchema::list_available()?;
if json {
let schema_list: Vec<serde_json::Value> = schemas
.iter()
.filter_map(|schema_id| {
tensor::TensorSchema::load_by_id(schema_id).ok().map(|s| {
serde_json::json!({
"id": s.id,
"name": s.name,
"dimensions": s.dimensions.len(),
"moods": s.moods.len(),
})
})
})
.collect();
println!("{}", serde_json::to_string_pretty(&schema_list)?);
} else if schemas.is_empty() {
let dir = crate::paths::state_schemas_dir();
println!("No schemas found (checked {})", dir.display());
println!("\nCreate a schema file (YAML or JSON) to get started.");
} else {
println!("Available schemas:\n");
for schema_id in schemas {
match tensor::TensorSchema::load_by_id(&schema_id) {
Ok(schema) => {
println!(
" {} - {} ({} dimensions, {} moods)",
schema.id,
schema.name,
schema.dimensions.len(),
schema.moods.len()
);
}
Err(_) => {
println!(" {} - (failed to load)", schema_id);
}
}
}
}
}
StateCommands::Moods { schema, mood, json } => {
let schema = load_tensor_schema(schema)?;
if let Some(mood_name) = mood {
// Show specific mood
match schema.moods.get(&mood_name) {
Some(mood_def) => {
if json {
println!("{}", serde_json::to_string_pretty(&mood_def)?);
} else {
println!("Mood: {}", mood_name);
println!("Description: {}", mood_def.description);
println!("Tolerance: {:.2}", mood_def.tolerance);
println!("\nTensor values:");
for (i, value) in mood_def.tensor.iter().enumerate() {
let dim_name = schema
.dimensions
.get(i)
.map(|d| d.name.as_str())
.unwrap_or("?");
let weight = mood_def
.weights
.as_ref()
.and_then(|w| w.get(i))
.copied()
.unwrap_or(1.0);
println!(" {}: {:.2} (weight: {:.2})", dim_name, value, weight);
}
}
}
None => {
bail!(
"Unknown mood '{}'. Available moods: {}",
mood_name,
schema.moods.keys().cloned().collect::<Vec<_>>().join(", ")
);
}
}
} else {
// List all moods
if json {
println!("{}", serde_json::to_string_pretty(&schema.moods)?);
} else {
println!("Moods for schema '{}' ({}):\n", schema.id, schema.name);
for (name, mood_def) in &schema.moods {
let tensor_str: Vec<String> = mood_def
.tensor
.iter()
.map(|v| format!("{:.2}", v))
.collect();
println!(" {:12} [{}]", name, tensor_str.join("|"));
println!(" {}", mood_def.description);
}
}
}
}
StateCommands::Info { schema, json } => {
let schema = load_tensor_schema(schema)?;
if json {
println!("{}", serde_json::to_string_pretty(&schema)?);
} else {
println!("Schema: {} ({})", schema.name, schema.id);
println!("Version: {}", schema.version);
println!();
println!("Dimensions ({}):", schema.dimensions.len());
for dim in &schema.dimensions {
let rune = dim
.rune
.as_ref()
.map(|r| format!(" {}", r))
.unwrap_or_default();
println!(" {}{}:", dim.name, rune);
println!(" Low: {}", dim.anchors.low);
if let Some(mid) = &dim.anchors.mid {
println!(" Mid: {}", mid);
}
println!(" High: {}", dim.anchors.high);
println!(" Default: {:.2}", dim.default);
}
println!();
println!("Moods ({}):", schema.moods.len());
for (name, mood) in &schema.moods {
println!(
" {:12} - {} (tol: {:.2})",
name, mood.description, mood.tolerance
);
}
}
}
}
Ok(())
}