//! 快速开始示例：演示 ddddocr-core 与引擎 crate 的解耦用法。
//!
//! 运行：`cargo run -p ddddocr-core --example quick_start`

use ddddocr_core::error::{Result, TensorError};
use ddddocr_core::traits::{InferenceEngine, Info, OcrEngine};
use ddddocr_core::types::{ModelInfo, TensorInfo};
use ddddocr_core::{ModelMetadata, Normalization, OcrBuilder, OcrOutput, Resize};

/// 演示引擎：只实现接口，不接入真实 ONNX 运行时。
struct DemoEngine {
    meta: ModelMetadata,
}

impl Info for DemoEngine {
    fn input_info(&self) -> Result<Vec<TensorInfo>> {
        Ok(vec![])
    }
    fn output_info(&self) -> Result<Vec<TensorInfo>> {
        Ok(vec![])
    }
    fn model_info(&self) -> Result<ModelInfo> {
        Ok(ModelInfo {
            inputs: vec![],
            outputs: vec![],
            providers: None,
        })
    }
}

impl InferenceEngine for DemoEngine {
    type Output = OcrOutput;

    fn inference(&self, input: ndarray::Array4<f32>) -> Result<Self::Output, TensorError> {
        // 用全零 logits 模拟推理输出：[Steps, Classes]
        let steps = input.shape()[2];
        let classes = self.meta.charset.size();
        Ok(OcrOutput::Logits(ndarray::Array2::zeros((steps, classes))))
    }
}

impl OcrEngine for DemoEngine {
    fn metadata(&self) -> &ModelMetadata {
        &self.meta
    }
}

fn main() {
    let engine = DemoEngine {
        meta: ModelMetadata::from_static_slice(
            &["", "a", "b"],
            false,
            Resize::Fixed(64, 64),
            1,
            Normalization::ZeroToOne,
        ),
    };

    let ocr = OcrBuilder::new().probability(true).build_with(&engine);
    let image = image::DynamicImage::new_luma8(64, 64);
    let result = ocr.predict(&image).expect("识别失败");
    println!("识别结果: {result}");
}