/**
 * @file mvgal_ai.c
 * @brief AI/ML-driven scheduling model inference implementation
 *
 * Multi-Vendor GPU Aggregation Layer for Linux
 */

#include "mvgal/mvgal_ai.h"
#include "mvgal/mvgal_log.h"
#include <stdlib.h>
#include <string.h>
#include <stdio.h>
#include <time.h>

/* Internal model structure */
struct mvgal_ai_model {
    char path[256];
    bool initialized;
};

mvgal_ai_model_t mvgal_ai_model_load(const char* model_path) {
    if (!model_path) return NULL;

    mvgal_ai_model_t model = (mvgal_ai_model_t)malloc(sizeof(struct mvgal_ai_model));
    if (!model) return NULL;

    strncpy(model->path, model_path, sizeof(model->path) - 1);
    model->initialized = true;

    mvgal_log_info("AI model loaded from: %s", model_path);
    return model;
}

int mvgal_ai_model_predict(mvgal_ai_model_t model,
                           const mvgal_ai_features_t* features,
                           mvgal_ai_prediction_t* out_prediction) {
    if (!model || !features || !out_prediction) return -1;

    struct timespec start, end;
    clock_gettime(CLOCK_MONOTONIC, &start);

    /* 
     * Mock AI Inference logic:
     * In a real implementation, this would call an ONNX Runtime or similar.
     * For now, we use a simple heuristic that mimics a model's output.
     */
    
    int best_gpu = -1;
    float best_score = -1.0f;

    for (uint32_t i = 0; i < features->num_gpus; i++) {
        /* Heuristic score: lower utilization and lower VRAM pressure is better */
        float score = (1.0f - features->gpu_utilization[i]) * 0.6f + 
                      (1.0f - features->vram_pressure[i]) * 0.4f;
        
        out_prediction->gpu_scores[i] = score;
        
        if (score > best_score) {
            best_score = score;
            best_gpu = i;
        }
    }

    out_prediction->recommended_gpu = best_gpu;
    out_prediction->confidence = best_score;

    clock_gettime(CLOCK_MONOTONIC, &end);
    out_prediction->inference_time_us = (uint32_t)((end.tv_sec - start.tv_sec) * 1000000 + 
                                                   (end.tv_nsec - start.tv_nsec) / 1000);

    return 0;
}

void mvgal_ai_model_unload(mvgal_ai_model_t model) {
    if (model) {
        mvgal_log_info("AI model unloaded: %s", model->path);
        free(model);
    }
}
