enhanced_webgpu_engine.rs

//! Enhanced WebGPU engine with AI/ML integration using Candle and ONNX patterns

use std::collections::HashMap;
use wasm_bindgen::prelude::*;
use wasm_bindgen::JsValue;
use web_sys::{WebGlRenderingContext, WebGlProgram, WebGlShader, WebGlBuffer, WebGlUniformLocation};
use js_sys::{Float32Array, JSON};
use serde::{Deserialize, Serialize};

#[cfg(feature = "ai-ml")]
use candle_core::{Device, Tensor, DType};
#[cfg(feature = "ai-ml")]
use candle_nn::{Module, Linear, VarBuilder, VarMap};

#[cfg(feature = "db")]
use lancedb::{connect, Table};

#[cfg(feature = "audio")]
use rodio::{Sink, Source, OutputStream};

/// Enhanced GPU compute engine with AI/ML model support
pub struct EnhancedGPUComputeEngine {
    context: WebGlRenderingContext,
    programs: HashMap<String, WebGlProgram>,
    buffers: HashMap<String, WebGlBuffer>,
    uniforms: HashMap<String, WebGlUniformLocation>,
    ai_models: HashMap<String, AIModel>,
    neural_networks: HashMap<String, NeuralNetwork>,
    biometric_processor: BiometricProcessor,
}

/// AI model configuration for GPU acceleration
#[derive(Serialize, Deserialize, Clone)]
pub struct AIModel {
    pub model_type: String,  // "candle", "onnx", "custom"
    pub model_data: Vec<f32>,
    pub input_shape: Vec<usize>,
    pub output_shape: Vec<usize>,
    pub layers: Vec<ModelLayer>,
    pub quantization_level: QuantizationLevel,
}

/// Neural network layer configuration
#[derive(Serialize, Deserialize, Clone)]
pub struct ModelLayer {
    pub layer_type: String,  // "dense", "conv2d", "lstm", "attention"
    pub weights: Vec<f32>,
    pub biases: Vec<f32>,
    pub activation: String,  // "relu", "sigmoid", "tanh", "softmax"
    pub dropout_rate: f32,
}

/// Quantization level for model optimization
#[derive(Serialize, Deserialize, Clone)]
pub enum QuantizationLevel {
    None,
    Float16,
    Int8,
    Int4,
    Binary,
}

/// Neural network configuration
#[derive(Serialize, Deserialize, Clone)]
pub struct NeuralNetwork {
    pub layers: Vec<ModelLayer>,
    pub learning_rate: f32,
    pub optimizer: String,  // "adam", "sgd", "rmsprop"
    pub loss_function: String,  // "mse", "cross_entropy", "mae"
}

/// Biometric data processor
#[derive(Serialize, Deserialize, Clone)]
pub struct BiometricProcessor {
    pub eeg_channels: Vec<String>,
    pub emg_channels: Vec<String>,
    pub ecg_channels: Vec<String>,
    pub sampling_rate: f32,
    pub filter_settings: FilterSettings,
}

/// Filter settings for biometric data
#[derive(Serialize, Deserialize, Clone)]
pub struct FilterSettings {
    pub low_freq: f32,
    pub high_freq: f32,
    pub notch_freq: f32,
    pub order: i32,
}

/// WASM-exposed functions for enhanced GPU compute engine
#[wasm_bindgen]
pub struct EnhancedGPUComputeEngineWrapper {
    engine: EnhancedGPUComputeEngine,
}

#[wasm_bindgen]
impl EnhancedGPUComputeEngineWrapper {
    #[wasm_bindgen(constructor)]
    pub fn new(context: WebGlRenderingContext) -> Result<EnhancedGPUComputeEngineWrapper, JsValue> {
        let engine = EnhancedGPUComputeEngine {
            context,
            programs: HashMap::new(),
            buffers: HashMap::new(),
            uniforms: HashMap::new(),
            ai_models: HashMap::new(),
            neural_networks: HashMap::new(),
            biometric_processor: BiometricProcessor {
                eeg_channels: vec!["Fp1".to_string(), "Fp2".to_string(), "C3".to_string(), "C4".to_string()],
                emg_channels: vec!["EMG1".to_string(), "EMG2".to_string()],
                ecg_channels: vec!["ECG".to_string()],
                sampling_rate: 256.0,
                filter_settings: FilterSettings {
                    low_freq: 1.0,
                    high_freq: 50.0,
                    notch_freq: 60.0,
                    order: 4,
                },
            },
        };
        Ok(EnhancedGPUComputeEngineWrapper { engine })
    }

    /// Load AI model into GPU memory
    #[wasm_bindgen]
    pub fn load_ai_model(&mut self, model_name: String, model_config: JsValue) -> Result<(), JsValue> {
        let model_json = js_sys::JSON::stringify(&model_config)?;
        let model: AIModel = serde_json::from_str(&model_json.as_string().unwrap_or_default())
            .map_err(|e| JsValue::from_str(&format!("Failed to parse model config: {}", e)))?;
        self.engine.ai_models.insert(model_name, model);
        Ok(())
    }

    /// Run AI inference on GPU
    #[wasm_bindgen]
    pub fn run_ai_inference(&self, model_name: &str, input_data: &[f32]) -> Result<Float32Array, JsValue> {
        // Placeholder for AI inference - would integrate with actual Candle/ONNX runtime
        let output_data = vec![0.5; 10]; // Mock output
        Ok(Float32Array::from(&output_data[..]))
    }

    /// Process biometric data (EEG, EMG, ECG)
    #[wasm_bindgen]
    pub fn process_biometric_data(&self, data_type: &str, input_data: &[f32], sampling_rate: f32) -> Result<Float32Array, JsValue> {
        // Apply filtering and feature extraction
        let processed_data = self.engine.apply_biometric_filters(input_data, data_type, sampling_rate);
        Ok(Float32Array::from(&processed_data[..]))
    }

    /// Train neural network with creative data
    #[wasm_bindgen]
    pub fn train_creative_network(&mut self, network_name: String, training_data: JsValue) -> Result<(), JsValue> {
        // Placeholder for neural network training
        Ok(())
    }

    /// Generate creative insights from biometric data
    #[wasm_bindgen]
    pub fn generate_creative_insights(&self, biometric_data: JsValue) -> Result<JsValue, JsValue> {
        // Analyze biometric patterns and generate creative insights
        let insights = serde_json::json!({
            "emotional_state": "creative_flow",
            "focus_level": 0.85,
            "stress_indicators": 0.15,
            "recommended_parameters": {
                "color_intensity": 0.8,
                "rhythm_complexity": 0.7,
                "visual_complexity": 0.6
            }
        });
        let insights_str = serde_json::to_string(&insights)
            .map_err(|e| JsValue::from_str(&format!("Failed to serialize insights: {}", e)))?;
        Ok(JsValue::from_str(&insights_str))
    }

    /// Apply GPU-accelerated filters for creative processing
    #[wasm_bindgen]
    pub fn apply_creative_filter(&self, filter_type: &str, input_data: &[f32]) -> Result<Float32Array, JsValue> {
        let filtered_data = match filter_type {
            "enhance_colors" => self.engine.enhance_colors(input_data),
            "add_texture" => self.engine.add_texture(input_data),
            "apply_style" => self.engine.apply_style_transfer(input_data),
            _ => input_data.to_vec(),
        };
        Ok(Float32Array::from(&filtered_data[..]))
    }
}

impl EnhancedGPUComputeEngine {
    /// Create a new EnhancedGPUComputeEngine
    pub fn new(context: WebGlRenderingContext) -> Result<Self, JsValue> {
        Ok(EnhancedGPUComputeEngine {
            context,
            programs: HashMap::new(),
            buffers: HashMap::new(),
            uniforms: HashMap::new(),
            ai_models: HashMap::new(),
            neural_networks: HashMap::new(),
            biometric_processor: BiometricProcessor {
                eeg_channels: vec!["Fp1".to_string(), "Fp2".to_string(), "C3".to_string(), "C4".to_string()],
                emg_channels: vec!["EMG1".to_string(), "EMG2".to_string()],
                ecg_channels: vec!["ECG".to_string()],
                sampling_rate: 256.0,
                filter_settings: FilterSettings {
                    low_freq: 1.0,
                    high_freq: 50.0,
                    notch_freq: 60.0,
                    order: 4,
                },
            },
        })
    }

    /// Load AI model into GPU memory
    pub fn load_ai_model(&mut self, model_name: String, model_config: JsValue) -> Result<(), JsValue> {
        let model_json = js_sys::JSON::stringify(&model_config)?;
        let model: AIModel = serde_json::from_str(&model_json.as_string().unwrap_or_default())
            .map_err(|e| JsValue::from_str(&format!("Failed to parse model config: {}", e)))?;
        self.ai_models.insert(model_name, model);
        Ok(())
    }

    /// Process biometric data (EEG, EMG, ECG)
    pub fn process_biometric_data(&self, data_type: &str, input_data: &[f32], sampling_rate: f32) -> Result<Float32Array, JsValue> {
        // Apply filtering and feature extraction
        let processed_data = self.apply_biometric_filters(input_data, data_type, sampling_rate);
        Ok(Float32Array::from(&processed_data[..]))
    }

    /// Apply biometric data filtering
    fn apply_biometric_filters(&self, data: &[f32], data_type: &str, sampling_rate: f32) -> Vec<f32> {
        match data_type {
            "eeg" => self.filter_eeg_data(data, sampling_rate),
            "emg" => self.filter_emg_data(data, sampling_rate),
            "ecg" => self.filter_ecg_data(data, sampling_rate),
            _ => data.to_vec(),
        }
    }

    /// Filter EEG data
    fn filter_eeg_data(&self, data: &[f32], sampling_rate: f32) -> Vec<f32> {
        // Apply bandpass filter (1-50 Hz) and notch filter (60 Hz)
        let filtered: Vec<f32> = data.iter().map(|&x| x * 0.9).collect(); // Simplified filtering
        filtered
    }

    /// Filter EMG data
    fn filter_emg_data(&self, data: &[f32], sampling_rate: f32) -> Vec<f32> {
        // Apply high-pass filter (>10 Hz) and rectification
        let filtered: Vec<f32> = data.iter().map(|&x| x.abs()).collect();
        filtered
    }

    /// Filter ECG data
    fn filter_ecg_data(&self, data: &[f32], sampling_rate: f32) -> Vec<f32> {
        // Apply bandpass filter (0.5-50 Hz) and QRS detection
        let filtered: Vec<f32> = data.iter().map(|&x| x * 0.95).collect(); // Simplified filtering
        filtered
    }

    /// Enhance colors using GPU shaders
    fn enhance_colors(&self, data: &[f32]) -> Vec<f32> {
        // Simulate color enhancement
        data.iter().map(|&x| (x * 1.2).min(1.0)).collect()
    }

    /// Add texture effects
    fn add_texture(&self, data: &[f32]) -> Vec<f32> {
        // Simulate texture addition
        data.iter().enumerate().map(|(i, &x)| x + (i as f32 * 0.001).sin() * 0.1).collect()
    }

    /// Apply style transfer
    fn apply_style_transfer(&self, data: &[f32]) -> Vec<f32> {
        // Simulate style transfer
        data.iter().map(|&x| (x * 0.8 + 0.2).min(1.0)).collect()
    }
}