amberjs 1.17.0

// Model Manager
//
// Manages AI/ML models for predictions and optimizations.

use super::error::{AIOpsError, Result};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::Arc;
use tokio::sync::RwLock;

/// Model types supported by the AI Ops engine
#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum ModelType {
    /// Anomaly detection model
    AnomalyDetection,
    /// Trend prediction model
    TrendPrediction,
    /// Failure prediction model
    FailurePrediction,
    /// Performance optimization model
    PerformanceOptimization,
    /// Resource allocation model
    ResourceAllocation,
    /// Architecture adaptation model
    ArchitectureAdaptation,
}
/// Model metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelMetadata {
    /// Model type
    pub model_type: ModelType,
    /// Model version
    pub version: String,
    /// Model accuracy
    pub accuracy: Option<f64>,
    /// Training data size
    pub training_data_size: usize,
    /// Model creation timestamp
    pub created_at: std::time::SystemTime,
    /// Model last updated timestamp
    pub updated_at: std::time::SystemTime,
}
/// Model representation (simplified)
#[derive(Debug, Clone)]
pub struct Model {
    /// Model metadata
    pub metadata: ModelMetadata,
    /// Model data (placeholder for actual model)
    pub data: Vec<u8>,
}
/// Model Manager
///
/// Manages the lifecycle of AI/ML models including:
/// - Model loading and unloading
/// - Model versioning
/// - Model validation
/// - Model metadata management
pub struct ModelManager {
    /// Models cache
    models: Arc<RwLock<HashMap<String, Model>>>,
    /// Model metadata
    metadata: Arc<RwLock<HashMap<String, ModelMetadata>>>,
}
impl ModelManager {
    /// Create a new model manager
    pub fn new() -> Self {
        Self {
            models: Arc::new(RwLock::new(HashMap::new())),
            metadata: Arc::new(RwLock::new(HashMap::new())),
        }
    }
    /// Load a model
    ///
    /// # Arguments
    ///
    /// * `model_id` - Unique model identifier
    /// * `model_type` - Type of the model
    /// * `model_data` - Model data bytes
    ///
    /// # Returns
    ///
    /// Returns `Result<()>` indicating success or failure
    pub async fn load_model(
        &self,
        model_id: &str,
        model_type: ModelType,
        model_data: Vec<u8>,
    ) -> Result<()> {
        let now: _ = std::time::SystemTime::now();
        let metadata: _ = ModelMetadata {
            model_type,
            version: "1.0.0".to_string(),
            accuracy: None,
            training_data_size: model_data.len(),
            created_at: now,
            updated_at: now,
        };
        let model: _ = Model {
            metadata: metadata.clone(),
            data: model_data,
        };
        let mut models = self.models.write().await;
        let mut metadata_map = self.metadata.write().await;
        models.insert(model_id.to_string(), model);
        metadata_map.insert(model_id.to_string(), metadata);
        Ok(())
    }
    /// Unload a model
    ///
    /// # Arguments
    ///
    /// * `model_id` - Model identifier to unload
    ///
    /// # Returns
    ///
    /// Returns `Result<()>` indicating success or failure
    pub async fn unload_model(&self, model_id: &str) -> Result<()> {
        let mut models = self.models.write().await;
        let mut metadata_map = self.metadata.write().await;
        models.remove(model_id);
        metadata_map.remove(model_id);
        Ok(())
    }
    /// Get model metadata
    ///
    /// # Arguments
    ///
    /// * `model_id` - Model identifier
    ///
    /// # Returns
    ///
    /// Returns `Option<ModelMetadata>` containing the metadata
    pub async fn get_metadata(&self, model_id: &str) -> Option<ModelMetadata> {
        let metadata_map: _ = self.metadata.read().await;
        metadata_map.get(model_id).cloned()
    }
    /// List all model IDs
    ///
    /// # Returns
    ///
    /// Returns `Vec<String>` containing all model IDs
    pub async fn list_models(&self) -> Vec<String> {
        let models: _ = self.models.read().await;
        models.keys().cloned().collect()
    }
    /// Check if a model exists
    ///
    /// # Arguments
    ///
    /// * `model_id` - Model identifier
    ///
    /// # Returns
    ///
    /// Returns `bool` indicating whether the model exists
    pub async fn model_exists(&self, model_id: &str) -> bool {
        let models: _ = self.models.read().await;
        models.contains_key(model_id)
    }
    /// Validate model integrity
    ///
    /// # Arguments
    ///
    /// * `model_id` - Model identifier
    ///
    /// # Returns
    ///
    /// Returns `Result<bool>` indicating whether the model is valid
    pub async fn validate_model(&self, model_id: &str) -> Result<bool> {
        let models: _ = self.models.read().await;
        match models.get(model_id) {
            Some(model) => {
                // Basic validation: check if model has data
                Ok(!model.data.is_empty())
            }
            None => Err(AIOpsError::model(format!("Model {} not found", model_id))),
        }
    }
}
impl Default for ModelManager {
    fn default() -> Self {
        Self::new()
    }
}