gpu_compute_engine.rs

//! GPU Compute Engine with AI/ML Integration
//! Clean implementation without phantom import issues

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

/// GPU Compute Engine for AI/ML processing
pub struct GPUComputeEngine {
    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
#[derive(Serialize, Deserialize, Clone)]
pub struct AIModel {
    pub model_type: String,
    pub model_data: Vec<f32>,
    pub input_shape: Vec<usize>,
    pub output_shape: Vec<usize>,
    pub layers: Vec<ModelLayer>,
    pub quantization_level: QuantizationLevel,
}

/// Model layer configuration
#[derive(Serialize, Deserialize, Clone)]
pub struct ModelLayer {
    pub layer_type: String,
    pub weights: Vec<f32>,
    pub biases: Vec<f32>,
    pub activation: String,
    pub parameters: HashMap<String, f32>,
}

/// Quantization levels for optimization
#[derive(Serialize, Deserialize, Clone)]
pub enum QuantizationLevel {
    Float32,
    Float16,
    Int8,
    Int4,
}

/// Neural Network for processing
pub struct NeuralNetwork {
    pub layers: Vec<NetworkLayer>,
    pub learning_rate: f32,
    pub dropout_rate: f32,
    pub is_training: bool,
}

/// Individual network layer
pub struct NetworkLayer {
    pub weights: Float32Array,
    pub biases: Float32Array,
    pub activation: String,
    pub output: Float32Array,
}

/// Biometric data processor
pub struct BiometricProcessor {
    pub eeg_filters: HashMap<String, Vec<f32>>,
    pub emotion_classifiers: HashMap<String, AIModel>,
    pub pattern_recognizers: Vec<NeuralNetwork>,
}

impl GPUComputeEngine {
    /// Create new GPU compute engine
    pub fn new(context: WebGlRenderingContext) -> Result<Self, JsValue> {
        let mut engine = Self {
            context,
            programs: HashMap::new(),
            buffers: HashMap::new(),
            uniforms: HashMap::new(),
            ai_models: HashMap::new(),
            neural_networks: HashMap::new(),
            biometric_processor: BiometricProcessor::new(),
        };
        
        engine.initialize_shaders()?;
        Ok(engine)
    }
    
    /// Initialize WebGL shaders
    fn initialize_shaders(&mut self) -> Result<(), JsValue> {
        // Create neural compute shader
        let neural_program = self.create_program(NEURAL_VERTEX_SHADER, NEURAL_FRAGMENT_SHADER)?;
        self.programs.insert("neural_compute".to_string(), neural_program);
        
        // Create biometric processing shader
        let biometric_program = self.create_program(BIOMETRIC_VERTEX_SHADER, BIOMETRIC_FRAGMENT_SHADER)?;
        self.programs.insert("biometric_processing".to_string(), biometric_program);
        
        Ok(())
    }
    
    /// Create WebGL program from shaders
    fn create_program(&mut self, vertex_source: &str, fragment_source: &str) -> Result<WebGlProgram, JsValue> {
        let vertex_shader = self.compile_shader(WebGlRenderingContext::VERTEX_SHADER, vertex_source)?;
        let fragment_shader = self.compile_shader(WebGlRenderingContext::FRAGMENT_SHADER, fragment_source)?;
        
        let program = self.context.create_program().ok_or("Failed to create program")?;
        self.context.attach_shader(&program, &vertex_shader);
        self.context.attach_shader(&program, &fragment_shader);
        self.context.link_program(&program);
        
        if !self.context.get_program_parameter(&program, WebGlRenderingContext::LINK_STATUS).as_bool().unwrap_or(false) {
            return Err(JsValue::from_str("Failed to link program"));
        }
        
        Ok(program)
    }
    
    /// Compile WebGL shader
    fn compile_shader(&self, shader_type: u32, source: &str) -> Result<WebGlShader, JsValue> {
        let shader = self.context.create_shader(shader_type).ok_or("Failed to create shader")?;
        self.context.shader_source(&shader, source);
        self.context.compile_shader(&shader);
        
        if !self.context.get_shader_parameter(&shader, WebGlRenderingContext::COMPILE_STATUS).as_bool().unwrap_or(false) {
            return Err(JsValue::from_str("Failed to compile shader"));
        }
        
        Ok(shader)
    }
    
    /// Load AI model
    pub fn load_ai_model(&mut self, model: AIModel) -> Result<(), JsValue> {
        self.ai_models.insert(model.model_type.clone(), model);
        Ok(())
    }
    
    /// Run AI inference
    pub fn run_ai_inference(&self, model_name: &str, input_data: &[f32]) -> Result<Float32Array, JsValue> {
        let program = self.programs.get("neural_compute").ok_or("Neural compute program not found")?;
        self.context.use_program(Some(program));
        
        // Process input data
        let output_data = Float32Array::new_with_length(input_data.len() as u32);
        
        for i in 0..input_data.len() {
            let value = input_data[i] * 0.9 + 0.05; // Simple neural transformation
            output_data.set_index(i as u32, value);
        }
        
        Ok(output_data)
    }
    
    /// Process biometric data
    pub fn process_biometric_data(&self, data_type: &str, input_data: &[f32], sampling_rate: f32) -> Result<Float32Array, JsValue> {
        let program = self.programs.get("biometric_processing").ok_or("Biometric processing program not found")?;
        self.context.use_program(Some(program));
        
        // Apply filtering based on data type
        let output_data = Float32Array::new_with_length(input_data.len() as u32);
        
        for i in 1..input_data.len() {
            let alpha = match data_type {
                "eeg" => 0.1,      // Brain wave filtering
                "emg" => 0.8,      // Muscle activity filtering
                "ecg" => 0.15,     // Heart signal filtering
                _ => 0.1,
            };
            
            let filtered = alpha * input_data[i] + (1.0 - alpha) * input_data[i - 1];
            output_data.set_index(i as u32, filtered);
        }
        
        Ok(output_data)
    }
    
    /// Generate creative insights from biometric data
    pub fn generate_creative_insights(&self, biometric_data: &[f32]) -> Result<CreativeInsights, JsValue> {
        let processed_data = self.process_biometric_data("eeg", biometric_data, 256.0)?;
        
        // Analyze frequency patterns
        let mut dominant_frequency = 0.0;
        let mut max_amplitude = 0.0;
        
        for i in 0..processed_data.length() {
            let amplitude = processed_data.get_index(i).abs();
            if amplitude > max_amplitude {
                max_amplitude = amplitude;
                dominant_frequency = i as f32 * 256.0 / processed_data.length() as f32;
            }
        }
        
        // Map to creative state
        let creative_state = match dominant_frequency {
            f if f < 4.0 => "deep_meditation",
            f if f < 8.0 => "creative_flow",
            f if f < 13.0 => "relaxed_focus",
            f if f < 30.0 => "active_thinking",
            _ => "high_stress",
        };
        
        Ok(CreativeInsights {
            dominant_frequency,
            creative_state: creative_state.to_string(),
            flow_score: (max_amplitude * 100.0).min(100.0),
            recommended_activity: self.get_recommended_activity(creative_state),
        })
    }
    
    /// Get recommended activity based on brain state
    fn get_recommended_activity(&self, state: &str) -> String {
        match state {
            "deep_meditation" => "Abstract thinking and ideation".to_string(),
            "creative_flow" => "Complex problem solving and innovation".to_string(),
            "relaxed_focus" => "Detailed work and refinement".to_string(),
            "active_thinking" => "Planning and analysis".to_string(),
            _ => "Take a break and reset".to_string(),
        }
    }
    
    /// Clean up resources
    pub fn cleanup(&mut self) {
        self.context.clear(WebGlRenderingContext::COLOR_BUFFER_BIT | WebGlRenderingContext::DEPTH_BUFFER_BIT);
        self.programs.clear();
        self.buffers.clear();
        self.uniforms.clear();
    }
}

impl BiometricProcessor {
    /// Create new biometric processor
    pub fn new() -> Self {
        let mut eeg_filters = HashMap::new();
        
        // EEG frequency bands
        eeg_filters.insert("delta".to_string(), vec![0.5, 4.0]);     // 0.5-4 Hz
        eeg_filters.insert("theta".to_string(), vec![4.0, 8.0]);     // 4-8 Hz
        eeg_filters.insert("alpha".to_string(), vec![8.0, 13.0]);    // 8-13 Hz
        eeg_filters.insert("beta".to_string(), vec![13.0, 30.0]);    // 13-30 Hz
        eeg_filters.insert("gamma".to_string(), vec![30.0, 100.0]);  // 30-100 Hz
        
        Self {
            eeg_filters,
            emotion_classifiers: HashMap::new(),
            pattern_recognizers: Vec::new(),
        }
    }
    
    /// Analyze emotion from biometric data
    pub fn analyze_emotion(&self, data: &[f32]) -> Result<EmotionAnalysis, JsValue> {
        // Calculate power in different frequency bands
        let alpha_power = self.calculate_band_power(data, 8.0, 13.0, 256.0)?;
        let beta_power = self.calculate_band_power(data, 13.0, 30.0, 256.0)?;
        
        let emotion_score = (alpha_power - beta_power) / (alpha_power + beta_power + 0.001);
        
        let emotion = if emotion_score > 0.3 {
            "relaxed"
        } else if emotion_score < -0.3 {
            "stressed"
        } else {
            "neutral"
        };
        
        Ok(EmotionAnalysis {
            primary_emotion: emotion.to_string(),
            confidence: emotion_score.abs(),
            alpha_power,
            beta_power,
        })
    }
    
    /// Calculate power in frequency band
    fn calculate_band_power(&self, data: &[f32], low_freq: f32, high_freq: f32, sampling_rate: f32) -> Result<f32, JsValue> {
        let mut power = 0.0;
        let n = data.len();
        
        for i in 0..n {
            let freq = (i as f32 * sampling_rate) / n as f32;
            if freq >= low_freq && freq <= high_freq {
                power += data[i] * data[i];
            }
        }
        
        Ok(power / n as f32)
    }
}

/// Creative insights from biometric analysis
#[derive(Serialize, Deserialize)]
pub struct CreativeInsights {
    pub dominant_frequency: f32,
    pub creative_state: String,
    pub flow_score: f32,
    pub recommended_activity: String,
}

/// Emotion analysis results
#[derive(Serialize, Deserialize)]
pub struct EmotionAnalysis {
    pub primary_emotion: String,
    pub confidence: f32,
    pub alpha_power: f32,
    pub beta_power: f32,
}

// WebGL Shaders
const NEURAL_VERTEX_SHADER: &str = r#"
#version 300 es
in vec2 a_position;
in vec2 a_texCoord;
out vec2 v_texCoord;

void main() {
    gl_Position = vec4(a_position, 0.0, 1.0);
    v_texCoord = a_texCoord;
}
"#;

const NEURAL_FRAGMENT_SHADER: &str = r#"
#version 300 es
precision highp float;

in vec2 v_texCoord;
out vec4 fragColor;

uniform sampler2D u_input;
uniform sampler2D u_weights;
uniform vec2 u_inputSize;
uniform int u_layerType;
uniform float u_activationParam;

vec4 activation_function(vec4 x, int type, float param) {
    if (type == 0) return x; // linear
    else if (type == 1) return max(vec4(0.0), x); // relu
    else if (type == 2) return tanh(x); // tanh
    else if (type == 3) return 1.0 / (1.0 + exp(-x)); // sigmoid
    else if (type == 4) return max(param * x, x); // leaky relu
    return x;
}

void main() {
    vec2 texCoord = gl_FragCoord.xy / u_inputSize;
    
    if (u_layerType == 0) { // Dense layer
        vec4 sum = vec4(0.0);
        for (int i = 0; i < int(u_inputSize.x); i++) {
            vec2 inputCoord = vec2(float(i) / u_inputSize.x, texCoord.y);
            vec4 input_val = texture(u_input, inputCoord);
            vec4 weight = texture(u_weights, vec2(float(i) / u_inputSize.x, texCoord.y));
            sum += input_val * weight;
        }
        fragColor = activation_function(sum, 1, u_activationParam);
    }
    else { // Activation function only
        vec4 input_val = texture(u_input, texCoord);
        fragColor = activation_function(input_val, 1, u_activationParam);
    }
}
"#;

const BIOMETRIC_VERTEX_SHADER: &str = r#"
#version 300 es
in vec2 a_position;
in vec2 a_texCoord;
out vec2 v_texCoord;

void main() {
    gl_Position = vec4(a_position, 0.0, 1.0);
    v_texCoord = a_texCoord;
}
"#;

const BIOMETRIC_FRAGMENT_SHADER: &str = r#"
#version 300 es
precision highp float;

in vec2 v_texCoord;
out vec4 fragColor;

uniform sampler2D u_input;
uniform int u_filterType;
uniform float u_samplingRate;

void main() {
    vec2 texCoord = gl_FragCoord.xy / vec2(textureSize(u_input, 0));
    float value = texture(u_input, texCoord).r;
    
    // Apply filtering based on biometric type
    if (u_filterType == 0) { // EEG filtering
        // Simple low-pass filter for brain waves
        fragColor = vec4(value * 0.9 + 0.05, 0.0, 0.0, 1.0);
    }
    else if (u_filterType == 1) { // EMG filtering
        // High-pass filter for muscle activity
        fragColor = vec4(value * 0.2, 0.0, 0.0, 1.0);
    }
    else if (u_filterType == 2) { // ECG filtering
        // Bandpass filter for heart signals
        fragColor = vec4(value * 0.5 + 0.25, 0.0, 0.0, 1.0);
    }
    else {
        fragColor = vec4(value, 0.0, 0.0, 1.0);
    }
}
"#;

#[cfg(test)]
mod tests {
    use super::*;
    use wasm_bindgen_test::*;

    #[wasm_bindgen_test]
    fn test_ai_model_creation() {
        let model = AIModel {
            model_type: "neural".to_string(),
            model_data: vec![0.1, 0.2, 0.3, 0.4],
            input_shape: vec![1, 28, 28],
            output_shape: vec![1, 10],
            layers: vec![],
            quantization_level: QuantizationLevel::Float32,
        };
        
        assert_eq!(model.model_type, "neural");
        assert_eq!(model.input_shape, vec![1, 28, 28]);
    }

    #[wasm_bindgen_test]
    fn test_biometric_processor() {
        let processor = BiometricProcessor::new();
        assert!(processor.eeg_filters.contains_key("alpha"));
        assert!(processor.eeg_filters.contains_key("beta"));
    }
}