/*
 * Fashion-MNIST Optical Evolution - Quick Inference Example
 *
 * This example demonstrates how to use the trained optical neural network
 * for inference on new Fashion-MNIST images.
 *
 * Author: Francisco Angulo de Lafuente
 * License: MIT
 */

#include <iostream>
#include <vector>
#include <string>
#include "../src/optical_model.hpp"
#include "../src/fungi.hpp"

// Fashion-MNIST class names
const std::vector<std::string> CLASS_NAMES = {
    "T-shirt/top", "Trouser", "Pullover", "Dress", "Coat",
    "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"
};

int main() {
    std::cout << "Fashion-MNIST Optical Evolution - Inference Example\n";
    std::cout << "==================================================\n";
    std::cout << "Enhanced FFT Kernel with 85.86% Accuracy\n\n";

    try {
        // Initialize model components
        OpticalParams params;
        DeviceBuffers db;
        FFTPlan fft;
        FungiSoA fungi;

        // Load pre-trained model weights
        std::cout << "Loading pre-trained model...\n";
        // Note: In actual implementation, load from trained_model.bin
        init_params(params, 42); // Use saved weights instead

        // Initialize GPU resources
        allocate_device_buffers(db, 1); // Batch size 1 for single image
        create_fft_plan(fft, 1);
        fungi.resize(128, 28, 28);
        fungi.init_random(42);

        // Upload parameters to GPU
        upload_params_to_gpu(params, db);

        // Example: Load a single Fashion-MNIST image
        std::vector<float> input_image(IMG_SIZE);

        // In real usage, load from file:
        // load_fashion_mnist_image("test_image.bin", input_image);

        // For demonstration, create a simple pattern
        for (int i = 0; i < IMG_SIZE; i++) {
            input_image[i] = 0.5f; // Placeholder
        }

        std::cout << "Processing image through optical network...\n";

        // Run inference
        std::vector<int> predictions;
        infer_batch(input_image.data(), 1, fungi, params, db, fft, predictions);

        // Display results
        int predicted_class = predictions[0];
        std::cout << "\nPrediction Results:\n";
        std::cout << "==================\n";
        std::cout << "Predicted Class: " << predicted_class << "\n";
        std::cout << "Class Name: " << CLASS_NAMES[predicted_class] << "\n";

        std::cout << "\nOptical Processing Details:\n";
        std::cout << "- Multi-Scale FFT: 6-scale mirror architecture\n";
        std::cout << "- Features Extracted: 2058 (Enhanced FFT)\n";
        std::cout << "- Hidden Neurons: 1800\n";
        std::cout << "- Fungi Population: 128 organisms\n";
        std::cout << "- Technology: 100% Optical + CUDA\n";

        // Cleanup
        free_device_buffers(db);
        destroy_fft_plan(fft);

        std::cout << "\nInference completed successfully!\n";

    } catch (const std::exception& e) {
        std::cerr << "Error during inference: " << e.what() << std::endl;
        return 1;
    }

    return 0;
}

/*
 * Compilation Instructions:
 *
 * nvcc -o quick_inference quick_inference.cpp ../src/optical_model.cu ../src/fungi.cu \
 *      -lcufft -lcurand -std=c++17 -O3
 *
 * Usage:
 * ./quick_inference
 *
 * For batch inference or custom images, modify the input loading section.
 */