ruvector-sona 0.2.1

//! WASM bindings for SONA
//!
//! Enable with feature flag: `wasm`
//!
//! ## Usage in JavaScript
//!
//! ```javascript
//! import init, { WasmSonaEngine } from './pkg/sona.js';
//!
//! async function main() {
//!   await init();
//!
//!   const engine = new WasmSonaEngine(256); // hidden_dim = 256
//!
//!   // Start trajectory
//!   const embedding = new Float32Array(256).fill(0.1);
//!   const trajectoryId = engine.start_trajectory(embedding);
//!
//!   // Record steps
//!   engine.record_step(trajectoryId, 42, 0.8, 1000);
//!
//!   // End trajectory
//!   engine.end_trajectory(trajectoryId, 0.85);
//!
//!   // Apply LoRA
//!   const input = new Float32Array(256).fill(1.0);
//!   const output = engine.apply_lora(input);
//!
//!   console.log('Transformed output:', output);
//! }
//! ```

#![cfg(feature = "wasm")]

use crate::trajectory::TrajectoryBuilder;
use crate::{LearningSignal, SonaConfig, SonaEngine};
use parking_lot::RwLock;
use std::collections::HashMap;
use std::sync::atomic::{AtomicU64, Ordering};
use std::sync::Arc;
use wasm_bindgen::prelude::*;

/// WASM-compatible SONA Engine wrapper
///
/// Provides JavaScript bindings for the SONA adaptive learning system.
#[wasm_bindgen]
pub struct WasmSonaEngine {
    inner: Arc<RwLock<SonaEngine>>,
    /// Active trajectory builders keyed by the ID handed to JS,
    /// paired with the query embedding for step recording (fixes #519).
    active_trajectories: RwLock<HashMap<u64, (TrajectoryBuilder, Vec<f32>)>>,
    /// Last query embedding seen, used to synthesize feedback trajectories.
    last_embedding: RwLock<Vec<f32>>,
    /// Trajectory handle generator.
    next_trajectory_id: AtomicU64,
}

#[wasm_bindgen]
impl WasmSonaEngine {
    /// Create a new SONA engine with specified hidden dimension
    ///
    /// # Arguments
    /// * `hidden_dim` - Size of hidden layer (typically 256, 512, or 1024)
    ///
    /// # Example
    /// ```javascript
    /// const engine = new WasmSonaEngine(256);
    /// ```
    #[wasm_bindgen(constructor)]
    pub fn new(hidden_dim: usize) -> Result<WasmSonaEngine, JsValue> {
        #[cfg(feature = "console_error_panic_hook")]
        console_error_panic_hook::set_once();

        Ok(Self {
            inner: Arc::new(RwLock::new(SonaEngine::new(hidden_dim))),
            active_trajectories: RwLock::new(HashMap::new()),
            last_embedding: RwLock::new(Vec::new()),
            next_trajectory_id: AtomicU64::new(1),
        })
    }

    /// Create engine with custom configuration
    ///
    /// # Arguments
    /// * `config` - JSON configuration object
    ///
    /// # Example
    /// ```javascript
    /// const config = {
    ///   hidden_dim: 256,
    ///   embedding_dim: 256,
    ///   micro_lora_rank: 2,
    ///   base_lora_rank: 16,
    ///   micro_lora_lr: 0.001,
    ///   base_lora_lr: 0.0001,
    ///   ewc_lambda: 1000.0,
    ///   pattern_clusters: 128,
    ///   trajectory_capacity: 10000,
    ///   quality_threshold: 0.6
    /// };
    /// const engine = WasmSonaEngine.with_config(config);
    /// ```
    #[wasm_bindgen(js_name = withConfig)]
    pub fn with_config(config: JsValue) -> Result<WasmSonaEngine, JsValue> {
        #[cfg(feature = "console_error_panic_hook")]
        console_error_panic_hook::set_once();

        let config: SonaConfig = serde_wasm_bindgen::from_value(config)?;

        Ok(Self {
            inner: Arc::new(RwLock::new(SonaEngine::with_config(config))),
            active_trajectories: RwLock::new(HashMap::new()),
            last_embedding: RwLock::new(Vec::new()),
            next_trajectory_id: AtomicU64::new(1),
        })
    }

    /// Start recording a new trajectory
    ///
    /// # Arguments
    /// * `query_embedding` - Query vector as Float32Array
    ///
    /// # Returns
    /// Trajectory ID (u64)
    ///
    /// # Example
    /// ```javascript
    /// const embedding = new Float32Array(256).fill(0.1);
    /// const trajectoryId = engine.start_trajectory(embedding);
    /// ```
    #[wasm_bindgen(js_name = startTrajectory)]
    pub fn start_trajectory(&self, query_embedding: Vec<f32>) -> u64 {
        let builder = {
            let engine = self.inner.read();
            engine.begin_trajectory(query_embedding.clone())
        };

        *self.last_embedding.write() = query_embedding.clone();

        let id = self.next_trajectory_id.fetch_add(1, Ordering::Relaxed);
        self.active_trajectories
            .write()
            .insert(id, (builder, query_embedding));
        id
    }

    /// Record a step in the trajectory
    ///
    /// # Arguments
    /// * `trajectory_id` - ID returned from start_trajectory
    /// * `node_id` - Graph node visited
    /// * `score` - Step quality score [0.0, 1.0]
    /// * `latency_us` - Step latency in microseconds
    ///
    /// # Example
    /// ```javascript
    /// engine.record_step(trajectoryId, 42, 0.8, 1000);
    /// ```
    #[wasm_bindgen(js_name = recordStep)]
    pub fn record_step(&self, trajectory_id: u64, node_id: u32, score: f32, _latency_us: u64) {
        let mut active = self.active_trajectories.write();
        if let Some((builder, embedding)) = active.get_mut(&trajectory_id) {
            // The query embedding is the only activation signal available at the
            // JS boundary; node_id is preserved as the step's layer name.
            builder.add_named_step(
                &format!("node-{}", node_id),
                embedding.clone(),
                Vec::new(),
                score,
            );
        }
    }

    /// End the trajectory and submit for learning
    ///
    /// # Arguments
    /// * `trajectory_id` - ID returned from start_trajectory
    /// * `final_score` - Overall trajectory quality [0.0, 1.0]
    ///
    /// # Example
    /// ```javascript
    /// engine.end_trajectory(trajectoryId, 0.85);
    /// ```
    #[wasm_bindgen(js_name = endTrajectory)]
    pub fn end_trajectory(&self, trajectory_id: u64, final_score: f32) {
        let entry = self.active_trajectories.write().remove(&trajectory_id);
        if let Some((mut builder, embedding)) = entry {
            // Ensure at least one step so a learning signal can be derived.
            if builder.step_count() == 0 {
                builder.add_step(embedding, Vec::new(), final_score);
            }
            let engine = self.inner.read();
            engine.end_trajectory(builder, final_score);
        }
    }

    /// Apply learning from user feedback
    ///
    /// # Arguments
    /// * `success` - Whether the operation succeeded
    /// * `latency_ms` - Operation latency in milliseconds
    /// * `quality` - User-perceived quality [0.0, 1.0]
    ///
    /// # Example
    /// ```javascript
    /// engine.learn_from_feedback(true, 50.0, 0.9);
    /// ```
    #[wasm_bindgen(js_name = learnFromFeedback)]
    pub fn learn_from_feedback(&self, success: bool, latency_ms: f32, quality: f32) {
        let quality = quality.clamp(0.0, 1.0);
        // Negative reward on failure flips the gradient direction (unlearn).
        let reward = if success { quality } else { -quality };

        // Reuse the last query embedding so feedback is attributed to the most
        // recent inference; fall back to a uniform unit vector otherwise.
        let embedding = {
            let last = self.last_embedding.read();
            if last.is_empty() {
                let dim = self.inner.read().config().hidden_dim;
                vec![1.0 / (dim as f32).sqrt(); dim]
            } else {
                last.clone()
            }
        };

        let engine = self.inner.read();
        let mut builder = engine.begin_trajectory(embedding.clone());
        builder.add_step(embedding, Vec::new(), reward);
        let latency_us = (latency_ms.max(0.0) * 1000.0) as u64;
        let trajectory = builder.build_with_latency(quality, latency_us);
        engine.submit_trajectory(trajectory);
        // Apply the accumulated micro-LoRA gradient immediately so a single
        // feedback call produces an actual weight update (fixes #519).
        engine.flush();
    }

    /// Apply LoRA transformation to input vector
    ///
    /// # Arguments
    /// * `input` - Input vector as Float32Array
    ///
    /// # Returns
    /// Transformed vector as Float32Array
    ///
    /// # Example
    /// ```javascript
    /// const input = new Float32Array(256).fill(1.0);
    /// const output = engine.apply_lora(input);
    /// ```
    #[wasm_bindgen(js_name = applyLora)]
    pub fn apply_lora(&self, input: Vec<f32>) -> Vec<f32> {
        let mut output = vec![0.0; input.len()];
        let engine = self.inner.read();
        engine.apply_micro_lora(&input, &mut output);
        output
    }

    /// Apply LoRA transformation to specific layer
    ///
    /// # Arguments
    /// * `layer_idx` - Layer index
    /// * `input` - Input vector as Float32Array
    ///
    /// # Returns
    /// Transformed vector as Float32Array
    #[wasm_bindgen(js_name = applyLoraLayer)]
    pub fn apply_lora_layer(&self, layer_idx: usize, input: Vec<f32>) -> Vec<f32> {
        let mut output = vec![0.0; input.len()];
        let engine = self.inner.read();
        engine.apply_base_lora(layer_idx, &input, &mut output);
        output
    }

    /// Run instant learning cycle
    ///
    /// Flushes accumulated micro-LoRA updates
    ///
    /// # Example
    /// ```javascript
    /// engine.run_instant_cycle();
    /// ```
    #[wasm_bindgen(js_name = runInstantCycle)]
    pub fn run_instant_cycle(&self) {
        let engine = self.inner.read();
        engine.flush();
    }

    /// Try to run background learning cycle
    ///
    /// Returns true if cycle was executed, false if not due yet
    ///
    /// # Example
    /// ```javascript
    /// if (engine.tick()) {
    ///   console.log('Background learning completed');
    /// }
    /// ```
    #[wasm_bindgen]
    pub fn tick(&self) -> bool {
        let engine = self.inner.read();
        engine.tick().is_some()
    }

    /// Force background learning cycle
    ///
    /// # Returns
    /// Learning statistics as JSON string
    ///
    /// # Example
    /// ```javascript
    /// const stats = engine.force_learn();
    /// console.log('Learning results:', stats);
    /// ```
    #[wasm_bindgen(js_name = forceLearn)]
    pub fn force_learn(&self) -> String {
        let engine = self.inner.read();
        engine.force_learn()
    }

    /// Get engine statistics
    ///
    /// # Returns
    /// Statistics as JSON object
    ///
    /// # Example
    /// ```javascript
    /// const stats = engine.get_stats();
    /// console.log('Trajectories buffered:', stats.trajectories_buffered);
    /// console.log('Patterns learned:', stats.patterns_learned);
    /// ```
    #[wasm_bindgen(js_name = getStats)]
    pub fn get_stats(&self) -> JsValue {
        let engine = self.inner.read();
        let stats = engine.stats();
        serde_wasm_bindgen::to_value(&stats).unwrap_or(JsValue::NULL)
    }

    /// Enable or disable the engine
    ///
    /// # Arguments
    /// * `enabled` - Whether to enable the engine
    ///
    /// # Example
    /// ```javascript
    /// engine.set_enabled(false); // Pause learning
    /// ```
    #[wasm_bindgen(js_name = setEnabled)]
    pub fn set_enabled(&self, enabled: bool) {
        let mut engine = self.inner.write();
        engine.set_enabled(enabled);
    }

    /// Check if engine is enabled
    ///
    /// # Returns
    /// true if enabled, false otherwise
    #[wasm_bindgen(js_name = isEnabled)]
    pub fn is_enabled(&self) -> bool {
        let engine = self.inner.read();
        engine.is_enabled()
    }

    /// Get configuration
    ///
    /// # Returns
    /// Configuration as JSON object
    #[wasm_bindgen(js_name = getConfig)]
    pub fn get_config(&self) -> JsValue {
        let engine = self.inner.read();
        let config = engine.config();
        serde_wasm_bindgen::to_value(config).unwrap_or(JsValue::NULL)
    }

    /// Find similar patterns to query
    ///
    /// # Arguments
    /// * `query_embedding` - Query vector as Float32Array
    /// * `k` - Number of patterns to return
    ///
    /// # Returns
    /// Array of similar patterns as JSON
    ///
    /// # Example
    /// ```javascript
    /// const query = new Float32Array(256).fill(0.5);
    /// const patterns = engine.find_patterns(query, 5);
    /// console.log('Similar patterns:', patterns);
    /// ```
    #[wasm_bindgen(js_name = findPatterns)]
    pub fn find_patterns(&self, query_embedding: Vec<f32>, k: usize) -> JsValue {
        let engine = self.inner.read();
        let patterns = engine.find_patterns(&query_embedding, k);
        serde_wasm_bindgen::to_value(&patterns).unwrap_or(JsValue::NULL)
    }
}

/// Initialize WASM module (called automatically)
#[wasm_bindgen(start)]
pub fn wasm_init() {
    #[cfg(feature = "console_error_panic_hook")]
    console_error_panic_hook::set_once();

    web_sys::console::log_1(&"SONA WASM module initialized".into());
}

// ============================================================================
// Federated Learning WASM Bindings
// ============================================================================

use crate::training::{
    EphemeralAgent as RustEphemeralAgent, FederatedCoordinator as RustFederatedCoordinator,
    FederatedTopology,
};

/// WASM-compatible Ephemeral Agent for federated learning
///
/// Lightweight agent wrapper (~5MB footprint) for distributed training.
/// Agents process tasks, collect trajectories, and export state for aggregation.
///
/// # Example
/// ```javascript
/// const agent = new WasmEphemeralAgent("agent-1");
///
/// // Process tasks
/// const embedding = new Float32Array(256).fill(0.1);
/// agent.process_task(embedding, 0.85);
///
/// // Export state for coordinator
/// const state = agent.export_state();
/// ```
#[wasm_bindgen]
pub struct WasmEphemeralAgent {
    inner: RustEphemeralAgent,
}

#[wasm_bindgen]
impl WasmEphemeralAgent {
    /// Create a new ephemeral agent with default config
    ///
    /// # Arguments
    /// * `agent_id` - Unique identifier for this agent
    ///
    /// # Example
    /// ```javascript
    /// const agent = new WasmEphemeralAgent("agent-1");
    /// ```
    #[wasm_bindgen(constructor)]
    pub fn new(agent_id: &str) -> Result<WasmEphemeralAgent, JsValue> {
        let config = SonaConfig::for_ephemeral();
        Ok(Self {
            inner: RustEphemeralAgent::new(agent_id, config),
        })
    }

    /// Create agent with custom configuration
    ///
    /// # Arguments
    /// * `agent_id` - Unique identifier
    /// * `config` - JSON configuration object
    ///
    /// # Example
    /// ```javascript
    /// const config = {
    ///   hidden_dim: 256,
    ///   trajectory_capacity: 500,
    ///   pattern_clusters: 25
    /// };
    /// const agent = WasmEphemeralAgent.with_config("agent-1", config);
    /// ```
    #[wasm_bindgen(js_name = withConfig)]
    pub fn with_config(agent_id: &str, config: JsValue) -> Result<WasmEphemeralAgent, JsValue> {
        let config: SonaConfig = serde_wasm_bindgen::from_value(config)?;
        Ok(Self {
            inner: RustEphemeralAgent::new(agent_id, config),
        })
    }

    /// Process a task and record trajectory
    ///
    /// # Arguments
    /// * `embedding` - Query embedding as Float32Array
    /// * `quality` - Task quality score [0.0, 1.0]
    ///
    /// # Example
    /// ```javascript
    /// const embedding = new Float32Array(256).fill(0.1);
    /// agent.process_task(embedding, 0.85);
    /// ```
    #[wasm_bindgen(js_name = processTask)]
    pub fn process_task(&mut self, embedding: Vec<f32>, quality: f32) {
        self.inner.process_task(embedding, quality);
    }

    /// Process task with model route information
    ///
    /// # Arguments
    /// * `embedding` - Query embedding
    /// * `quality` - Quality score
    /// * `route` - Model route used (e.g., "gpt-4", "claude-3")
    #[wasm_bindgen(js_name = processTaskWithRoute)]
    pub fn process_task_with_route(&mut self, embedding: Vec<f32>, quality: f32, route: &str) {
        self.inner
            .process_task_with_route(embedding, quality, route);
    }

    /// Export agent state for coordinator aggregation
    ///
    /// # Returns
    /// JSON object containing agent state, trajectories, and statistics
    ///
    /// # Example
    /// ```javascript
    /// const state = agent.export_state();
    /// console.log('Trajectories:', state.trajectories.length);
    /// coordinator.aggregate(state);
    /// ```
    #[wasm_bindgen(js_name = exportState)]
    pub fn export_state(&self) -> JsValue {
        let export = self.inner.export_state();
        serde_wasm_bindgen::to_value(&export).unwrap_or(JsValue::NULL)
    }

    /// Get agent statistics
    ///
    /// # Returns
    /// JSON object with trajectory count, quality stats, uptime
    #[wasm_bindgen(js_name = getStats)]
    pub fn get_stats(&self) -> JsValue {
        let stats = self.inner.stats();
        serde_wasm_bindgen::to_value(&stats).unwrap_or(JsValue::NULL)
    }

    /// Get number of collected trajectories
    #[wasm_bindgen(js_name = trajectoryCount)]
    pub fn trajectory_count(&self) -> usize {
        self.inner.trajectory_count()
    }

    /// Get average quality of collected trajectories
    #[wasm_bindgen(js_name = averageQuality)]
    pub fn average_quality(&self) -> f32 {
        self.inner.average_quality()
    }

    /// Get agent uptime in seconds
    #[wasm_bindgen(js_name = uptimeSeconds)]
    pub fn uptime_seconds(&self) -> u64 {
        self.inner.uptime_seconds()
    }

    /// Clear collected trajectories (after export)
    #[wasm_bindgen]
    pub fn clear(&mut self) {
        self.inner.clear();
    }

    /// Force learning cycle on agent's engine
    #[wasm_bindgen(js_name = forceLearn)]
    pub fn force_learn(&self) -> String {
        self.inner.force_learn()
    }

    /// Get learned patterns from agent
    #[wasm_bindgen(js_name = getPatterns)]
    pub fn get_patterns(&self) -> JsValue {
        let patterns = self.inner.get_patterns();
        serde_wasm_bindgen::to_value(&patterns).unwrap_or(JsValue::NULL)
    }
}

/// WASM-compatible Federated Coordinator
///
/// Central aggregator for federated learning with quality filtering.
/// Coordinates multiple ephemeral agents using star topology.
///
/// # Example
/// ```javascript
/// const coordinator = new WasmFederatedCoordinator("central");
///
/// // Aggregate agent exports
/// const agentState = agent.export_state();
/// const result = coordinator.aggregate(agentState);
///
/// // Check stats
/// const stats = coordinator.get_stats();
/// console.log('Total agents:', stats.total_agents);
/// ```
#[wasm_bindgen]
pub struct WasmFederatedCoordinator {
    inner: RustFederatedCoordinator,
}

#[wasm_bindgen]
impl WasmFederatedCoordinator {
    /// Create a new federated coordinator with default config
    ///
    /// # Arguments
    /// * `coordinator_id` - Unique identifier for this coordinator
    ///
    /// # Example
    /// ```javascript
    /// const coordinator = new WasmFederatedCoordinator("central");
    /// ```
    #[wasm_bindgen(constructor)]
    pub fn new(coordinator_id: &str) -> Result<WasmFederatedCoordinator, JsValue> {
        let config = SonaConfig::for_coordinator();
        Ok(Self {
            inner: RustFederatedCoordinator::new(coordinator_id, config),
        })
    }

    /// Create coordinator with custom configuration
    ///
    /// # Arguments
    /// * `coordinator_id` - Unique identifier
    /// * `config` - JSON configuration object
    ///
    /// # Example
    /// ```javascript
    /// const config = {
    ///   hidden_dim: 256,
    ///   trajectory_capacity: 50000,
    ///   pattern_clusters: 200,
    ///   ewc_lambda: 2000.0
    /// };
    /// const coordinator = WasmFederatedCoordinator.with_config("central", config);
    /// ```
    #[wasm_bindgen(js_name = withConfig)]
    pub fn with_config(
        coordinator_id: &str,
        config: JsValue,
    ) -> Result<WasmFederatedCoordinator, JsValue> {
        let config: SonaConfig = serde_wasm_bindgen::from_value(config)?;
        Ok(Self {
            inner: RustFederatedCoordinator::new(coordinator_id, config),
        })
    }

    /// Set quality threshold for accepting trajectories
    ///
    /// # Arguments
    /// * `threshold` - Minimum quality [0.0, 1.0], default 0.4
    #[wasm_bindgen(js_name = setQualityThreshold)]
    pub fn set_quality_threshold(&mut self, threshold: f32) {
        self.inner.set_quality_threshold(threshold);
    }

    /// Aggregate agent export into coordinator
    ///
    /// # Arguments
    /// * `agent_export` - JSON export from agent.export_state()
    ///
    /// # Returns
    /// JSON aggregation result with accepted/rejected counts
    ///
    /// # Example
    /// ```javascript
    /// const agentState = agent.export_state();
    /// const result = coordinator.aggregate(agentState);
    /// console.log('Accepted:', result.accepted);
    /// ```
    #[wasm_bindgen]
    pub fn aggregate(&mut self, agent_export: JsValue) -> JsValue {
        use crate::training::AgentExport;

        match serde_wasm_bindgen::from_value::<AgentExport>(agent_export) {
            Ok(export) => {
                let result = self.inner.aggregate(export);
                serde_wasm_bindgen::to_value(&result).unwrap_or(JsValue::NULL)
            }
            Err(e) => {
                web_sys::console::error_1(&format!("Failed to parse agent export: {:?}", e).into());
                JsValue::NULL
            }
        }
    }

    /// Consolidate learning from all aggregated trajectories
    ///
    /// Should be called periodically after aggregating multiple agents.
    ///
    /// # Returns
    /// Learning result as JSON string
    #[wasm_bindgen]
    pub fn consolidate(&self) -> String {
        self.inner.consolidate()
    }

    /// Get coordinator statistics
    ///
    /// # Returns
    /// JSON object with agent count, trajectory count, quality stats
    #[wasm_bindgen(js_name = getStats)]
    pub fn get_stats(&self) -> JsValue {
        let stats = self.inner.stats();
        serde_wasm_bindgen::to_value(&stats).unwrap_or(JsValue::NULL)
    }

    /// Get total number of contributing agents
    #[wasm_bindgen(js_name = agentCount)]
    pub fn agent_count(&self) -> usize {
        self.inner.agent_count()
    }

    /// Get total trajectories aggregated
    #[wasm_bindgen(js_name = totalTrajectories)]
    pub fn total_trajectories(&self) -> usize {
        self.inner.total_trajectories()
    }

    /// Get all learned patterns from coordinator
    #[wasm_bindgen(js_name = getPatterns)]
    pub fn get_patterns(&self) -> JsValue {
        let patterns = self.inner.get_all_patterns();
        serde_wasm_bindgen::to_value(&patterns).unwrap_or(JsValue::NULL)
    }

    /// Find similar patterns to query
    ///
    /// # Arguments
    /// * `query_embedding` - Query vector
    /// * `k` - Number of patterns to return
    #[wasm_bindgen(js_name = findPatterns)]
    pub fn find_patterns(&self, query_embedding: Vec<f32>, k: usize) -> JsValue {
        let patterns = self.inner.find_patterns(&query_embedding, k);
        serde_wasm_bindgen::to_value(&patterns).unwrap_or(JsValue::NULL)
    }

    /// Apply coordinator's learned LoRA to input
    #[wasm_bindgen(js_name = applyLora)]
    pub fn apply_lora(&self, input: Vec<f32>) -> Vec<f32> {
        self.inner.apply_lora(&input)
    }

    /// Clear all agent contributions (reset coordinator)
    #[wasm_bindgen]
    pub fn clear(&mut self) {
        self.inner.clear();
    }
}

// Additional helper for serde support
#[cfg(feature = "wasm")]
mod serde_wasm_bindgen {
    use super::*;
    use serde::Serialize;

    pub fn to_value<T: Serialize>(value: &T) -> Result<JsValue, JsValue> {
        serde_json::to_string(value)
            .map(|s| JsValue::from_str(&s))
            .map_err(|e| JsValue::from_str(&e.to_string()))
    }

    pub fn from_value<T: serde::de::DeserializeOwned>(value: JsValue) -> Result<T, JsValue> {
        if let Some(s) = value.as_string() {
            serde_json::from_str(&s).map_err(|e| JsValue::from_str(&e.to_string()))
        } else {
            Err(JsValue::from_str("Expected JSON string"))
        }
    }
}