use oar_ocr::prelude::*;
use std::path::Path;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Build OCR pipeline with required models
    let ocr = OAROCRBuilder::new(
        "detection_model.onnx".to_string(),
        "recognition_model.onnx".to_string(),
        "char_dict.txt".to_string(),
    ).build()?;

    // Process a single image
    let image = oar_ocr::utils::load_image(Path::new("document.jpg"))?;
    let results = ocr.predict(&[image])?;
    let result = &results[0];

    // Print extracted text with confidence scores using the modern TextRegion API
    for text_region in &result.text_regions {
        if let (Some(text), Some(confidence)) = (&text_region.text, text_region.confidence) {
            println!("Text: {} (confidence: {:.2})", text, confidence);
        }
    }

    // Process multiple images at once
    let images = oar_ocr::utils::load_images(&[
        Path::new("document1.jpg"),
        Path::new("document2.jpg"),
        Path::new("document3.jpg"),
    ])?;
    let results = ocr.predict(&images)?;

    for result in results {
        println!("Image {}: {} text regions found", result.index, result.text_regions.len());
        for text_region in &result.text_regions {
            if let (Some(text), Some(confidence)) = (&text_region.text, text_region.confidence) {
                println!("  Text: {} (confidence: {:.2})", text, confidence);
            }
        }
    }

    Ok(())
}

use oar_ocr::prelude::*;
use oar_ocr::core::config::onnx::{OrtSessionConfig, OrtExecutionProvider};
use std::path::Path;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Configure CUDA execution provider for GPU acceleration
    let ort_config = OrtSessionConfig::new()
        .with_execution_providers(vec![
            OrtExecutionProvider::CUDA {
                device_id: Some(0),  // Use GPU 0
                gpu_mem_limit: None,
                arena_extend_strategy: None,
                cudnn_conv_algo_search: None,
                do_copy_in_default_stream: None,
                cudnn_conv_use_max_workspace: None,
            },
            OrtExecutionProvider::CPU,  // Fallback to CPU if CUDA fails
        ]);

    // Build OCR pipeline with CUDA support
    let ocr = OAROCRBuilder::new(
        "detection_model.onnx".to_string(),
        "recognition_model.onnx".to_string(),
        "char_dict.txt".to_string(),
    )
    .global_ort_session(ort_config)  // Apply CUDA config to all components
    .build()?;

    // Process images (same as CPU example)
    let image = oar_ocr::utils::load_image(Path::new("document.jpg"))?;
    let results = ocr.predict(&[image])?;
    let result = &results[0];

    // Extract text from results
    for text_region in &result.text_regions {
        if let (Some(text), Some(confidence)) = (&text_region.text, text_region.confidence) {
            println!("Text: {} (confidence: {:.2})", text, confidence);
        }
    }

    Ok(())
}