engine/src/ml_backend.rs

use crate::error::Result;
use std::path::Path;

/// Support pour différents backends d'IA locale
pub enum MLBackend {
    Onnx,
    Python,
    TensorRT,
    Candle, // Nouveau moteur natif Rust
}

/// Interface pour la génération locale d'images (SDXL, etc.)
pub struct LocalImageGenerator {
    pub backend: MLBackend,
    pub model_path: String,
}

impl LocalImageGenerator {
    pub fn new(backend: MLBackend, model_path: String) -> Self {
        Self { backend, model_path }
    }

    pub async fn generate_image(&self, prompt: &str, output_path: &Path) -> Result<()> {
        match self.backend {
            MLBackend::Onnx => self.generate_onnx(prompt, output_path).await,
            MLBackend::Python => self.generate_python(prompt, output_path).await,
            MLBackend::TensorRT => self.generate_tensorrt(prompt, output_path).await,
            MLBackend::Candle => self.generate_candle(prompt, output_path).await,
        }
    }

    async fn generate_candle(&self, prompt: &str, output_path: &Path) -> Result<()> {
        use crate::generators::candle_native::CandleNativeGenerator;
        let gen = CandleNativeGenerator::new(&self.model_path)?;
        // Interface simplifiée pour le backend ML
        let _ = crate::generators::ImageGenerator::generate(&gen, prompt, output_path, 0, false).await?;
        Ok(())
    }

    async fn generate_onnx(&self, _prompt: &str, _output_path: &Path) -> Result<()> {
        eprintln!("⚠️  ONNX image generation not yet implemented");
        eprintln!("     Would require: ort crate (ONNX Runtime)");
        Ok(())
    }

    async fn generate_python(&self, _prompt: &str, _output_path: &Path) -> Result<()> {
        eprintln!("⚠️  Python image generation not yet implemented");
        eprintln!("     Would require: PyO3 for Python integration");
        Ok(())
    }

    async fn generate_tensorrt(&self, prompt: &str, output_path: &Path) -> Result<()> {
        use crate::generators::tensorrt_engine::TensorRTGenerator;
        let gen = TensorRTGenerator::new(&self.model_path)?;
        let _ = crate::generators::ImageGenerator::generate(&gen, prompt, output_path, 0, false).await?;
        Ok(())
    }
}

/// Interface pour la génération locale de depth maps
pub struct LocalDepthGenerator {
    pub backend: MLBackend,
    pub model_path: String,
}

impl LocalDepthGenerator {
    pub fn new(backend: MLBackend, model_path: String) -> Self {
        Self { backend, model_path }
    }

    pub async fn estimate_depth(&self, image_path: &Path, output_path: &Path) -> Result<()> {
        match self.backend {
            MLBackend::Onnx => self.estimate_onnx(image_path, output_path).await,
            MLBackend::Python => self.estimate_python(image_path, output_path).await,
            MLBackend::TensorRT => self.estimate_tensorrt(image_path, output_path).await,
            MLBackend::Candle => self.estimate_candle(image_path, output_path).await,
        }
    }

    async fn estimate_candle(&self, _image_path: &Path, _output_path: &Path) -> Result<()> {
        eprintln!("⚠️  Candle depth estimation not yet implemented");
        Ok(())
    }

    async fn estimate_onnx(&self, _image_path: &Path, _output_path: &Path) -> Result<()> {
        eprintln!("⚠️  ONNX depth estimation not yet implemented");
        Ok(())
    }

    async fn estimate_python(&self, _image_path: &Path, _output_path: &Path) -> Result<()> {
        eprintln!("⚠️  Python depth estimation not yet implemented");
        Ok(())
    }

    async fn estimate_tensorrt(&self, _image_path: &Path, _output_path: &Path) -> Result<()> {
        eprintln!("⚠️  TensorRT depth estimation not yet implemented");
        Ok(())
    }
}

/// Interface pour l'embedding de prompts (CLIP, etc.)
pub struct PromptEmbedder {
    pub backend: MLBackend,
}

impl PromptEmbedder {
    pub fn new(backend: MLBackend) -> Self {
        Self { backend }
    }

    pub async fn embed_prompt(&self, prompt: &str) -> Result<Vec<f32>> {
        match self.backend {
            MLBackend::Onnx => self.embed_onnx(prompt).await,
            MLBackend::Python => self.embed_python(prompt).await,
            MLBackend::TensorRT => self.embed_tensorrt(prompt).await,
            MLBackend::Candle => self.embed_candle(prompt).await,
        }
    }

    async fn embed_candle(&self, _prompt: &str) -> Result<Vec<f32>> {
        eprintln!("⚠️  Candle embedding not yet implemented");
        Ok(vec![])
    }

    async fn embed_onnx(&self, _prompt: &str) -> Result<Vec<f32>> {
        eprintln!("⚠️  ONNX embedding not yet implemented");
        Ok(vec![])
    }

    async fn embed_python(&self, _prompt: &str) -> Result<Vec<f32>> {
        eprintln!("⚠️  Python embedding not yet implemented");
        Ok(vec![])
    }

    async fn embed_tensorrt(&self, _prompt: &str) -> Result<Vec<f32>> {
        eprintln!("⚠️  TensorRT embedding not yet implemented");
        Ok(vec![])
    }
}

/// Feature flags pour déterminer quels backends sont disponibles
pub fn available_backends() -> Vec<&'static str> {
    let backends = vec![];
    #[cfg(feature = "onnx")]
    backends.push("onnx");
    #[cfg(feature = "python")]
    backends.push("python");
    #[cfg(feature = "tensorrt")]
    backends.push("tensorrt");
    backends
}