tektra 0.2.0

A voice-interactive AI assistant with multimodal capabilities
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use anyhow::Result;
use std::path::Path;
use tracing::{info, error, warn};
use super::inference_backend::{InferenceBackend, InferenceConfig, InferenceMetrics};
use std::time::Instant;
use candle_core::Device;
use candle_transformers::models::gemma2::Model as GemmaModel;
use tokenizers::Tokenizer;
use std::sync::Arc;

pub struct GGUFInference {
    model: Option<GemmaModel>,
    tokenizer: Option<Arc<Tokenizer>>,
    device: Device,
    model_loaded: bool,
}

impl GGUFInference {
    pub fn new() -> Self {
        // Use Metal on macOS, CUDA if available, otherwise CPU
        let device = if cfg!(target_os = "macos") {
            Device::new_metal(0).unwrap_or(Device::Cpu)
        } else if candle_core::utils::cuda_is_available() {
            Device::new_cuda(0).unwrap_or(Device::Cpu)
        } else {
            Device::Cpu
        };
        
        info!("GGUF inference using device: {:?}", device);
        
        Self {
            model: None,
            tokenizer: None,
            device,
            model_loaded: false,
        }
    }

    fn load_gguf_model(&mut self, model_path: &Path) -> Result<()> {
        info!("Loading GGUF model from: {:?}", model_path);
        
        if !model_path.exists() {
            error!("Model file not found at: {:?}", model_path);
            return Err(anyhow::anyhow!("Model file not found"));
        }
        
        info!("GGUF model file found at: {:?}", model_path);
        
        // For now, we'll implement a basic loading mechanism
        // In a full implementation, we would:
        // 1. Parse the GGUF file format
        // 2. Load the quantized weights
        // 3. Set up the model architecture based on config
        
        // Load tokenizer if available
        let tokenizer_path = model_path.parent()
            .unwrap_or(Path::new("."))
            .join("tokenizer.json");
            
        if tokenizer_path.exists() {
            info!("Loading tokenizer from: {:?}", tokenizer_path);
            match Tokenizer::from_file(&tokenizer_path) {
                Ok(tokenizer) => {
                    self.tokenizer = Some(Arc::new(tokenizer));
                    info!("Tokenizer loaded successfully");
                }
                Err(e) => {
                    warn!("Failed to load tokenizer: {}", e);
                }
            }
        } else {
            // Try to download tokenizer from a public repository
            info!("Tokenizer not found locally, downloading tokenizer...");
            
            // Try the unsloth repository which has the tokenizer publicly available
            let tokenizer_url = "https://huggingface.co/unsloth/gemma-2-2b-it-bnb-4bit/resolve/main/tokenizer.json";
            let tokenizer_path_clone = tokenizer_path.clone();
            
            // Download using blocking I/O to avoid runtime issues
            let download_result = std::thread::spawn(move || {
                let client = reqwest::blocking::Client::builder()
                    .build()
                    .map_err(|e| std::io::Error::new(std::io::ErrorKind::Other, format!("Failed to create client: {}", e)))?;
                
                match client.get(tokenizer_url).send() {
                    Ok(response) => {
                        if response.status().is_success() {
                            match response.bytes() {
                                Ok(bytes) => {
                                    std::fs::write(&tokenizer_path_clone, bytes)
                                }
                                Err(e) => {
                                    Err(std::io::Error::new(std::io::ErrorKind::Other, format!("Failed to read response: {}", e)))
                                }
                            }
                        } else {
                            Err(std::io::Error::new(std::io::ErrorKind::Other, format!("HTTP error: {}", response.status())))
                        }
                    }
                    Err(e) => {
                        Err(std::io::Error::new(std::io::ErrorKind::Other, format!("Request failed: {}", e)))
                    }
                }
            }).join().unwrap();
            
            match download_result {
                Ok(_) => {
                    info!("Tokenizer downloaded successfully");
                    // Try to load it
                    match Tokenizer::from_file(&tokenizer_path) {
                        Ok(tokenizer) => {
                            self.tokenizer = Some(Arc::new(tokenizer));
                            info!("Tokenizer loaded successfully");
                        }
                        Err(e) => {
                            error!("Failed to load downloaded tokenizer: {}", e);
                        }
                    }
                }
                Err(e) => {
                    error!("Failed to download/save tokenizer: {}", e);
                }
            }
        }
        
        // TODO: Implement actual GGUF loading with Candle
        // For now, we'll mark it as loaded but warn about incomplete implementation
        warn!("GGUF model loading with Candle is not fully implemented yet");
        warn!("To use actual inference, we need to:");
        warn!("1. Implement GGUF format parsing");
        warn!("2. Load quantized weights into Candle tensors");
        warn!("3. Set up the Gemma model architecture");
        
        self.model_loaded = true;
        Ok(())
    }
    
}

impl InferenceBackend for GGUFInference {
    fn load_model(&mut self, model_path: &Path) -> Result<()> {
        self.load_gguf_model(model_path)
    }
    
    fn is_loaded(&self) -> bool {
        self.model_loaded
    }
    
    fn generate(&self, prompt: &str, config: &InferenceConfig) -> Result<String> {
        if !self.model_loaded {
            return Err(anyhow::anyhow!("Model not loaded"));
        }
        
        info!("GGUF Backend - Generating response for prompt: {}", prompt);
        info!("Config: max_tokens={}, temp={}, top_p={}", 
              config.max_tokens, config.temperature, config.top_p);
        
        // Check if we have a tokenizer
        if let Some(tokenizer) = &self.tokenizer {
            info!("Tokenizer available, attempting to tokenize prompt");
            
            // Tokenize the prompt
            match tokenizer.encode(prompt, true) {
                Ok(encoding) => {
                    let tokens = encoding.get_ids();
                    info!("Prompt tokenized to {} tokens", tokens.len());
                    
                    // TODO: Implement actual Candle inference here
                    error!("GGUF inference with Candle is not yet implemented");
                    return Err(anyhow::anyhow!("GGUF inference with Candle is not yet implemented. Please use a different backend or wait for implementation."));
                }
                Err(e) => {
                    error!("Failed to tokenize prompt: {}", e);
                    return Err(anyhow::anyhow!("Tokenization failed: {}", e));
                }
            }
        } else {
            error!("No tokenizer available for GGUF model");
            return Err(anyhow::anyhow!("No tokenizer available. GGUF models require a tokenizer.json file."));
        }
    }
    
    fn generate_with_metrics(&self, prompt: &str, config: &InferenceConfig) -> Result<(String, InferenceMetrics)> {
        let start = Instant::now();
        let initial_memory = self.get_memory_usage_mb();
        
        // Generate response
        let result = self.generate(prompt, config)?;
        
        let total_time = start.elapsed();
        let tokens = result.split_whitespace().count();
        
        let metrics = InferenceMetrics {
            tokens_generated: tokens,
            time_to_first_token_ms: 50.0, // Estimate for fallback
            tokens_per_second: tokens as f64 / total_time.as_secs_f64(),
            total_time_ms: total_time.as_millis() as f64,
            peak_memory_mb: self.get_memory_usage_mb() - initial_memory,
        };
        
        Ok((result, metrics))
    }
    
    fn name(&self) -> &str {
        "GGUF (Candle)"
    }
    
    fn is_available() -> bool {
        // GGUF with Candle is available on all platforms
        true
    }
    
    fn get_memory_usage_mb(&self) -> f64 {
        // TODO: Implement actual memory tracking
        0.0
    }
}