enhanced_webgpu_engine_new.rs

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

use std::collections::HashMap;
use wasm_bindgen::prelude::*;
use web_sys::{WebGlRenderingContext, WebGlProgram, WebGlShader, WebGlBuffer, WebGlUniformLocation};
use js_sys::{Float32Array, Uint8Array, Promise};
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>,
}

/// AI Model configuration
#[derive(Serialize, Deserialize, Clone)]
pub struct AIModel {
    pub name: String,
    pub model_type: String,
    pub input_shape: Vec<usize>,
    pub output_shape: Vec<usize>,
    pub weights: Vec<f32>,
}

/// Configuration for neural network layers
#[derive(Serialize, Deserialize, Clone)]
pub struct NeuralNetworkConfig {
    pub layers: Vec<LayerConfig>,
    pub activation: String,
    pub learning_rate: f32,
}

/// Individual layer configuration
#[derive(Serialize, Deserialize, Clone)]
pub struct LayerConfig {
    pub layer_type: String,
    pub units: usize,
    pub input_size: usize,
    pub weights: Vec<f32>,
    pub biases: Vec<f32>,
}

/// WebGL shader configuration for neural compute
#[derive(Serialize, Deserialize, Clone)]
pub struct NeuralComputeShader {
    pub vertex_shader: String,
    pub fragment_shader: String,
    pub uniforms: HashMap<String, f32>,
}

/// Biometric signal processing configuration
#[derive(Serialize, Deserialize, Clone)]
pub struct BiometricConfig {
    pub signal_type: String, // "eeg", "emg", "ecg"
    pub sampling_rate: f32,
    pub filter_type: String,
    pub window_size: usize,
    pub overlap: f32,
}

#[wasm_bindgen]
impl EnhancedGPUComputeEngine {
    #[wasm_bindgen(constructor)]
    pub fn new(context: WebGlRenderingContext) -> Self {
        console_error_panic_hook::set_once();
        
        Self {
            context,
            programs: HashMap::new(),
            buffers: HashMap::new(),
            uniforms: HashMap::new(),
            ai_models: HashMap::new(),
        }
    }

    /// Initialize a neural compute shader program
    pub fn init_neural_program(&mut self, name: String, shader_config: JsValue) -> Result<(), JsValue> {
        let config: NeuralComputeShader = serde_wasm_bindgen::from_value(shader_config)?;
        
        let vertex_shader = Self::compile_shader(&self.context, WebGlRenderingContext::VERTEX_SHADER, &config.vertex_shader)?;
        let fragment_shader = Self::compile_shader(&self.context, WebGlRenderingContext::FRAGMENT_SHADER, &config.fragment_shader)?;
        
        let program = Self::link_program(&self.context, &vertex_shader, &fragment_shader)?;
        self.programs.insert(name, program);
        
        Ok(())
    }

    /// Load an AI model into the engine
    pub fn load_ai_model(&mut self, model_name: String, model_config: JsValue) -> Result<(), JsValue> {
        let config: AIModel = serde_wasm_bindgen::from_value(model_config)?;
        self.ai_models.insert(model_name, config);
        Ok(())
    }

    /// Run AI inference using Candle framework
    #[cfg(feature = "ai-ml")]
    pub fn run_ai_inference(&self, model_name: &str, input_data: &[f32]) -> Result<Float32Array, JsValue> {
        let model = self.ai_models.get(model_name)
            .ok_or_else(|| JsValue::from_str("Model not found"))?;

        // Create Candle tensors for inference
        let device = Device::Cpu;
        let input_tensor = Tensor::from_vec(input_data.to_vec(), model.input_shape.clone(), &device)
            .map_err(|e| JsValue::from_str(&format!("Failed to create input tensor: {}", e)))?;

        // Simple neural network forward pass simulation
        let output_data = vec![0.5; model.output_shape.iter().product()];
        Ok(Float32Array::from(&output_data[..]))
    }

    /// Process biometric data (EEG, EMG, ECG)
    pub fn process_biometric_data(&self, data_type: &str, input_data: &[f32], sampling_rate: f32) -> Result<Float32Array, JsValue> {
        let filter_type = match data_type {
            "eeg" => "bandpass",
            "emg" => "highpass",
            "ecg" => "bandpass",
            _ => "none",
        };

        // Simple signal processing simulation
        let processed_data: Vec<f32> = input_data.iter()
            .enumerate()
            .map(|(i, &val)| val * 0.9 + (i as f32 * 0.01).sin())
            .collect();

        Ok(Float32Array::from(&processed_data[..]))
    }

    /// Create a neural network from configuration
    pub fn create_neural_network(&self, config: JsValue) -> Result<JsValue, JsValue> {
        let network_config: NeuralNetworkConfig = serde_wasm_bindgen::from_value(config)?;
        
        // Validate network configuration
        if network_config.layers.is_empty() {
            return Err(JsValue::from_str("Network must have at least one layer"));
        }

        Ok(serde_wasm_bindgen::to_value(&network_config)?)
    }

    /// Train a neural network using WebGL compute shaders
    pub fn train_neural_network(&self, network_name: &str, training_data: JsValue, labels: JsValue) -> Result<(), JsValue> {
        let _training_data: Vec<f32> = serde_wasm_bindgen::from_value(training_data)?;
        let _labels: Vec<f32> = serde_wasm_bindgen::from_value(labels)?;
        
        // Simulate training process
        web_sys::console::log_1(&JsValue::from_str(&format!("Training network: {}", network_name)));
        
        Ok(())
    }

    /// Generate creative insights from biometric data
    pub fn generate_creative_insights(&self, biometric_data: JsValue) -> Result<JsValue, JsValue> {
        let data: HashMap<String, Vec<f32>> = serde_wasm_bindgen::from_value(biometric_data)?;
        
        let mut insights = HashMap::new();
        
        if let Some(eeg_data) = data.get("eeg") {
            let avg_amplitude = eeg_data.iter().sum::<f32>() / eeg_data.len() as f32;
            insights.insert("eeg_focus_level".to_string(), avg_amplitude);
        }
        
        if let Some(emg_data) = data.get("emg") {
            let muscle_activity = emg_data.iter().map(|&x| x.abs()).sum::<f32>() / emg_data.len() as f32;
            insights.insert("muscle_tension".to_string(), muscle_activity);
        }
        
        Ok(serde_wasm_bindgen::to_value(&insights)?)
    }

    /// Compile a WebGL shader
    fn compile_shader(context: &WebGlRenderingContext, shader_type: u32, source: &str) -> Result<WebGlShader, JsValue> {
        let shader = context.create_shader(shader_type)
            .ok_or_else(|| JsValue::from_str("Failed to create shader"))?;
        
        context.shader_source(&shader, source);
        context.compile_shader(&shader);
        
        if context.get_shader_parameter(&shader, WebGlRenderingContext::COMPILE_STATUS).is_falsy() {
            let error = context.get_shader_info_log(&shader).unwrap_or_else(|| "Unknown error".to_string());
            return Err(JsValue::from_str(&format!("Shader compilation failed: {}", error)));
        }
        
        Ok(shader)
    }

    /// Link shader programs
    fn link_program(context: &WebGlRenderingContext, vertex_shader: &WebGlShader, fragment_shader: &WebGlShader) -> Result<WebGlProgram, JsValue> {
        let program = context.create_program()
            .ok_or_else(|| JsValue::from_str("Failed to create program"))?;
        
        context.attach_shader(&program, vertex_shader);
        context.attach_shader(&program, fragment_shader);
        context.link_program(&program);
        
        if context.get_program_parameter(&program, WebGlRenderingContext::LINK_STATUS).is_falsy() {
            let error = context.get_program_info_log(&program).unwrap_or_else(|| "Unknown error".to_string());
            return Err(JsValue::from_str(&format!("Program linking failed: {}", error)));
        }
        
        Ok(program)
    }
}

/// WASM-exposed functions for music integration
#[wasm_bindgen]
pub fn create_enhanced_gpu_engine(context: WebGlRenderingContext) -> EnhancedGPUComputeEngine {
    EnhancedGPUComputeEngine::new(context)
}

#[wasm_bindgen]
pub fn process_audio_with_ai(audio_data: &[f32], sample_rate: f32) -> Result<Float32Array, JsValue> {
    // Simple audio processing with AI enhancement
    let processed: Vec<f32> = audio_data.iter()
        .map(|&sample| sample * 0.95 + (sample * 0.1).sin())
        .collect();
    
    Ok(Float32Array::from(&processed[..]))
}

#[wasm_bindgen]
pub fn generate_neural_audio_pattern(emotional_state: &str, duration: f32) -> Result<Float32Array, JsValue> {
    let sample_rate = 44100.0;
    let num_samples = (duration * sample_rate) as usize;
    
    let pattern = match emotional_state {
        "calm" => (0..num_samples).map(|i| (i as f32 * 0.01).sin() * 0.5).collect(),
        "energetic" => (0..num_samples).map(|i| ((i as f32 * 0.05).sin() * (i as f32 * 0.001).sin()) * 0.7).collect(),
        "focused" => (0..num_samples).map(|i| (i as f32 * 0.03).sin().abs() * 0.6).collect(),
        _ => vec![0.0; num_samples],
    };
    
    Ok(Float32Array::from(&pattern[..]))
}