shimmy 2.0.0

// Auto-discovery system for GGUF and SafeTensors models
use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::env;
use std::fs;
use std::path::Path;
use std::path::PathBuf;

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DiscoveredModel {
    pub name: String,
    pub path: PathBuf,
    pub format: ModelFormat,
    pub size_bytes: Option<u64>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ModelFormat {
    Gguf,
    SafeTensors,
}

#[derive(Debug)]
pub struct ModelDiscovery {
    search_paths: Vec<PathBuf>,
}

impl Default for ModelDiscovery {
    fn default() -> Self {
        Self::new()
    }
}

impl ModelDiscovery {
    pub fn new() -> Self {
        Self {
            search_paths: Vec::new(),
        }
    }

    pub fn from_env() -> Self {
        let mut discovery = Self::new();

        // Add SHIMMY_BASE_GGUF parent directory
        if let Ok(base_path) = env::var("SHIMMY_BASE_GGUF") {
            if let Some(parent) = Path::new(&base_path).parent() {
                discovery.add_search_path(parent.to_path_buf());
            }
        }

        // Add custom model directories from environment variable
        if let Ok(custom_dirs) = env::var("SHIMMY_MODEL_PATHS") {
            for dir in custom_dirs.split(';').filter(|s| !s.is_empty()) {
                discovery.add_search_path(PathBuf::from(dir));
            }
        }

        // Add OLLAMA_MODELS environment variable if set
        if let Ok(ollama_models) = env::var("OLLAMA_MODELS") {
            discovery.add_search_path(PathBuf::from(ollama_models));
        }

        // Add common model directories
        if let Ok(home) = env::var("HOME").or_else(|_| env::var("USERPROFILE")) {
            let home_path = PathBuf::from(home);
            discovery.add_search_path(home_path.join(".cache/huggingface"));
            discovery.add_search_path(home_path.join(".ollama/models"));
            // LM Studio model cache (issue #184)
            discovery.add_search_path(home_path.join(".cache/lm-studio/models"));
            discovery.add_search_path(home_path.join("models"));
        }

        // Search common Ollama installation paths on different drives
        #[cfg(windows)]
        {
            for drive in &["C:", "D:", "E:", "F:"] {
                let ollama_path = PathBuf::from(format!(
                    "{}\\Users\\{}\\AppData\\Local\\Ollama\\models",
                    drive,
                    env::var("USERNAME").unwrap_or_default()
                ));
                discovery.add_search_path(ollama_path);

                // Also check alternate Ollama paths
                let alt_ollama = PathBuf::from(format!("{}\\Ollama\\models", drive));
                discovery.add_search_path(alt_ollama);
            }
        }

        discovery
    }

    pub fn add_search_path(&mut self, path: PathBuf) {
        self.search_paths.push(path);
    }

    pub fn search_paths(&self) -> &[PathBuf] {
        &self.search_paths
    }

    pub fn discover_models(&self) -> Result<Vec<DiscoveredModel>> {
        println!(
            "DEBUG: discover_models called, search_paths: {:?}",
            self.search_paths
        );
        let mut models = Vec::new();

        for path in &self.search_paths {
            if path.exists() {
                self.scan_directory(path, &mut models)?;
            }
        }

        Ok(models)
    }

    fn scan_directory(&self, dir: &Path, models: &mut Vec<DiscoveredModel>) -> Result<()> {
        println!("DEBUG: Scanning directory: {:?}", dir);
        // First pass: collect all model files in this directory
        let mut model_files = Vec::new();
        let mut subdirs = Vec::new();

        for entry in fs::read_dir(dir)? {
            let entry = entry?;
            let path = entry.path();

            if path.is_dir() {
                subdirs.push(path);
            } else if self.is_model_file(&path) {
                model_files.push(path);
            }
        }

        // Group sharded models together
        let grouped_models = self.group_sharded_models(dir, &model_files)?;

        // Add grouped models to the results
        for model in grouped_models {
            models.push(model);
        }

        // Recursively scan subdirectories
        for subdir in subdirs {
            self.scan_directory(&subdir, models)?;
        }

        Ok(())
    }

    /// Group sharded model files together (Issue #147)
    /// Detects patterns like model-00001-of-00004.safetensors and groups them as single models
    fn group_sharded_models(
        &self,
        dir: &Path,
        model_files: &[PathBuf],
    ) -> Result<Vec<DiscoveredModel>> {
        println!(
            "DEBUG: group_sharded_models called for dir: {:?}, files: {}",
            dir,
            model_files.len()
        );
        use regex::Regex;
        use std::collections::HashMap;

        let mut grouped_models = Vec::new();
        let mut processed_files = std::collections::HashSet::new();

        // Regex to match sharded model patterns: model-XXXX-of-YYYY.ext
        let shard_pattern = Regex::new(r"^(.+)-\d{5}-of-\d{5}(\..+)$").unwrap();

        // Group files by their base name (without shard numbers)
        let mut shard_groups: HashMap<String, Vec<PathBuf>> = HashMap::new();

        for file_path in model_files {
            if let Some(filename) = file_path.file_name().and_then(|f| f.to_str()) {
                println!("DEBUG: Checking file: {}", filename);
                if let Some(captures) = shard_pattern.captures(filename) {
                    // This is a sharded file
                    let base_name = captures.get(1).unwrap().as_str();
                    let extension = captures.get(2).unwrap().as_str();
                    let group_key = format!("{}{}", base_name, extension);
                    println!(
                        "DEBUG: Matched sharded file - base: {}, ext: {}, key: {}",
                        base_name, extension, group_key
                    );
                    shard_groups
                        .entry(group_key)
                        .or_default()
                        .push(file_path.clone());
                    processed_files.insert(file_path.clone());
                } else {
                    println!("DEBUG: No match for: {}", filename);
                }
            }
        }

        // Create grouped model entries for sharded models
        for (group_key, files) in shard_groups {
            if files.len() > 1 {
                // Calculate total size
                let total_size: u64 = files
                    .iter()
                    .filter_map(|path| fs::metadata(path).ok().map(|m| m.len()))
                    .sum();

                // Use directory name as model name for sharded models
                let model_name = dir
                    .file_name()
                    .and_then(|n| n.to_str())
                    .unwrap_or(&group_key)
                    .to_string();

                // Use the first file as the primary path (for compatibility)
                let primary_path = files[0].clone();

                let format = if group_key.ends_with(".safetensors") {
                    ModelFormat::SafeTensors
                } else {
                    ModelFormat::Gguf
                };

                grouped_models.push(DiscoveredModel {
                    name: model_name,
                    path: primary_path,
                    format,
                    size_bytes: Some(total_size),
                });
            }
        }

        // Add non-sharded models as individual entries
        for file_path in model_files {
            if !processed_files.contains(file_path) {
                if let Ok(model) = self.analyze_model_file(file_path) {
                    grouped_models.push(model);
                }
            }
        }

        Ok(grouped_models)
    }

    fn is_model_file(&self, path: &Path) -> bool {
        if let Some(ext) = path.extension() {
            if matches!(ext.to_str(), Some("gguf") | Some("safetensors")) {
                // Filter out non-LLM models based on filename patterns
                return self.is_llm_model(path);
            }
        }
        false
    }

    /// Filter to detect LLM models vs other GGUF types (Issue #80)
    fn is_llm_model(&self, path: &Path) -> bool {
        let filename = path
            .file_name()
            .and_then(|f| f.to_str())
            .unwrap_or("")
            .to_lowercase();

        // Exclude known non-LLM model types
        let non_llm_patterns = [
            // Text-to-image models
            "flux",
            "sd",
            "stable-diffusion",
            "sdxl",
            "dalle",
            "midjourney",
            // Video models
            "video",
            "vid",
            "animate",
            "motion",
            // Audio models
            "whisper",
            "audio",
            "speech",
            "tts",
            "voice",
            // CLIP and embedding models
            "clip",
            "embed",
            "encoder",
            "vision",
            // Specific model architectures that aren't text generation
            "vae",
            "unet",
            "controlnet",
            "lora",
            "adapter",
        ];

        // If filename contains any non-LLM patterns, exclude it
        if non_llm_patterns
            .iter()
            .any(|pattern| filename.contains(pattern))
        {
            return false;
        }

        // For SafeTensors, be more permissive as they're usually LLMs
        if path.extension().and_then(|s| s.to_str()) == Some("safetensors") {
            return true;
        }

        // For GGUF files, include if they look like LLM models
        // Common LLM model patterns
        let llm_patterns = [
            "llama",
            "mistral",
            "qwen",
            "phi",
            "gemma",
            "codellama",
            "vicuna",
            "alpaca",
            "orca",
            "falcon",
            "mpt",
            "gpt",
            "claude",
            "chatglm",
            "baichuan",
            "yi",
            "deepseek",
            "mixtral",
            "solar",
            "openchat",
            "starling",
            "wizardlm",
            "dolphin",
            "nous",
            "hermes",
            "airoboros",
        ];

        // If filename contains LLM patterns, include it
        if llm_patterns
            .iter()
            .any(|pattern| filename.contains(pattern))
        {
            return true;
        }

        // Default: include GGUF files unless they match exclusion patterns
        // This errs on the side of inclusion for unknown models
        true
    }

    fn analyze_model_file(&self, path: &Path) -> Result<DiscoveredModel> {
        let format = match path.extension().and_then(|s| s.to_str()) {
            Some("gguf") => ModelFormat::Gguf,
            Some("safetensors") => ModelFormat::SafeTensors,
            _ => return Err(anyhow::anyhow!("Unknown model format")),
        };

        let size_bytes = fs::metadata(path).ok().map(|m| m.len());

        let name = path
            .file_stem()
            .and_then(|s| s.to_str())
            .unwrap_or("unknown")
            .to_string();

        Ok(DiscoveredModel {
            name,
            path: path.to_path_buf(),
            format,
            size_bytes,
        })
    }
}

/// Simple wrapper function for benchmarking - discovers models in a single directory
pub fn discover_models_from_directory(path: &Path) -> Result<Vec<DiscoveredModel>> {
    let mut discovery = ModelDiscovery::new();
    discovery.add_search_path(path.to_path_buf());
    discovery.discover_models()
}

#[cfg(test)]
mod tests {
    use super::*;
    use std::env;
    use std::fs;
    use tempfile::TempDir;

    #[test]
    fn test_model_discovery_new() {
        let discovery = ModelDiscovery::new();
        assert_eq!(discovery.search_paths.len(), 0);
    }

    #[test]
    fn test_add_search_path() {
        let mut discovery = ModelDiscovery::new();
        let test_path = PathBuf::from("/test/path");

        discovery.add_search_path(test_path.clone());
        assert_eq!(discovery.search_paths.len(), 1);
        assert_eq!(discovery.search_paths[0], test_path);
    }

    #[test]
    fn test_from_env_with_shimmy_base_gguf() {
        // Set environment variable
        env::set_var("SHIMMY_BASE_GGUF", "/models/test.gguf");

        let discovery = ModelDiscovery::from_env();

        // Should have at least the parent directory of SHIMMY_BASE_GGUF
        assert!(!discovery.search_paths.is_empty());
        assert!(discovery
            .search_paths
            .iter()
            .any(|p| p.to_string_lossy().contains("models")));

        // Clean up
        env::remove_var("SHIMMY_BASE_GGUF");
    }

    #[test]
    fn test_from_env_with_home_directories() {
        // Temporarily set HOME/USERPROFILE
        let original_home = env::var("HOME").or_else(|_| env::var("USERPROFILE"));
        env::set_var("HOME", "/test/home");

        let discovery = ModelDiscovery::from_env();

        // Should include home-based paths
        assert!(discovery
            .search_paths
            .iter()
            .any(|p| p.to_string_lossy().contains(".cache/huggingface")));
        assert!(discovery
            .search_paths
            .iter()
            .any(|p| p.to_string_lossy().contains("models")));

        // Restore original environment
        env::remove_var("HOME");
        if let Ok(home) = original_home {
            env::set_var("HOME", home);
        }
    }

    #[test]
    fn test_is_model_file() {
        let discovery = ModelDiscovery::new();

        // Test GGUF files
        assert!(discovery.is_model_file(&PathBuf::from("test.gguf")));
        assert!(discovery.is_model_file(&PathBuf::from("/path/to/model.gguf")));

        // Test SafeTensors files
        assert!(discovery.is_model_file(&PathBuf::from("test.safetensors")));
        assert!(discovery.is_model_file(&PathBuf::from("/path/to/model.safetensors")));

        // Test non-model files
        assert!(!discovery.is_model_file(&PathBuf::from("test.txt")));
        assert!(!discovery.is_model_file(&PathBuf::from("test.bin")));
        assert!(!discovery.is_model_file(&PathBuf::from("test")));
    }

    #[test]
    fn test_analyze_model_file_gguf() -> Result<()> {
        let temp_dir = TempDir::new()?;
        let model_path = temp_dir.path().join("test-model.gguf");

        // Create a dummy file
        fs::write(&model_path, "dummy gguf content")?;

        let discovery = ModelDiscovery::new();
        let model = discovery.analyze_model_file(&model_path)?;

        assert_eq!(model.name, "test-model");
        assert_eq!(model.path, model_path);
        assert!(matches!(model.format, ModelFormat::Gguf));
        assert!(model.size_bytes.is_some());
        assert_eq!(model.size_bytes.unwrap(), "dummy gguf content".len() as u64);

        Ok(())
    }

    #[test]
    fn test_analyze_model_file_safetensors() -> Result<()> {
        let temp_dir = TempDir::new()?;
        let model_path = temp_dir.path().join("test-model.safetensors");

        // Create a dummy file
        fs::write(&model_path, "dummy safetensors content")?;

        let discovery = ModelDiscovery::new();
        let model = discovery.analyze_model_file(&model_path)?;

        assert_eq!(model.name, "test-model");
        assert_eq!(model.path, model_path);
        assert!(matches!(model.format, ModelFormat::SafeTensors));
        assert!(model.size_bytes.is_some());
        assert_eq!(
            model.size_bytes.unwrap(),
            "dummy safetensors content".len() as u64
        );

        Ok(())
    }

    #[test]
    fn test_analyze_model_file_unknown_format() {
        let temp_dir = TempDir::new().unwrap();
        let model_path = temp_dir.path().join("test-model.unknown");

        fs::write(&model_path, "dummy content").unwrap();

        let discovery = ModelDiscovery::new();
        let result = discovery.analyze_model_file(&model_path);

        assert!(result.is_err());
        assert!(result
            .unwrap_err()
            .to_string()
            .contains("Unknown model format"));
    }

    #[test]
    fn test_analyze_model_file_no_metadata() {
        let discovery = ModelDiscovery::new();
        let nonexistent_path = PathBuf::from("/nonexistent/model.gguf");

        let result = discovery.analyze_model_file(&nonexistent_path);

        // Should still work but with None for size_bytes
        if let Ok(model) = result {
            assert_eq!(model.name, "model");
            assert!(matches!(model.format, ModelFormat::Gguf));
            assert!(model.size_bytes.is_none());
        }
        // If it errors, that's also acceptable for nonexistent files
    }

    #[test]
    fn test_discover_models_empty_paths() {
        let discovery = ModelDiscovery::new();
        let models = discovery.discover_models().unwrap();
        assert_eq!(models.len(), 0);
    }

    #[test]
    fn test_discover_models_nonexistent_paths() {
        let mut discovery = ModelDiscovery::new();
        discovery.add_search_path(PathBuf::from("/nonexistent/path"));

        let models = discovery.discover_models().unwrap();
        assert_eq!(models.len(), 0);
    }

    #[test]
    fn test_discover_models_with_files() -> Result<()> {
        let temp_dir = TempDir::new()?;

        // Create some test model files
        fs::write(temp_dir.path().join("model1.gguf"), "content1")?;
        fs::write(temp_dir.path().join("model2.safetensors"), "content2")?;
        fs::write(temp_dir.path().join("not_model.txt"), "not a model")?;

        // Create subdirectory with another model
        let subdir = temp_dir.path().join("subdir");
        fs::create_dir(&subdir)?;
        fs::write(subdir.join("model3.gguf"), "content3")?;

        let mut discovery = ModelDiscovery::new();
        discovery.add_search_path(temp_dir.path().to_path_buf());

        let models = discovery.discover_models()?;

        // Should find 3 model files (2 in root, 1 in subdir)
        assert_eq!(models.len(), 3);

        let names: Vec<String> = models.iter().map(|m| m.name.clone()).collect();
        assert!(names.contains(&"model1".to_string()));
        assert!(names.contains(&"model2".to_string()));
        assert!(names.contains(&"model3".to_string()));

        Ok(())
    }

    #[test]
    fn test_scan_directory_recursive() -> Result<()> {
        let temp_dir = TempDir::new()?;

        // Create nested directory structure
        let level1 = temp_dir.path().join("level1");
        let level2 = level1.join("level2");
        fs::create_dir_all(&level2)?;

        // Create model files at different levels
        fs::write(temp_dir.path().join("root.gguf"), "root content")?;
        fs::write(level1.join("level1.gguf"), "level1 content")?;
        fs::write(level2.join("level2.safetensors"), "level2 content")?;

        let discovery = ModelDiscovery::new();
        let mut models = Vec::new();
        discovery.scan_directory(temp_dir.path(), &mut models)?;

        assert_eq!(models.len(), 3);
        let names: Vec<String> = models.iter().map(|m| m.name.clone()).collect();
        assert!(names.contains(&"root".to_string()));
        assert!(names.contains(&"level1".to_string()));
        assert!(names.contains(&"level2".to_string()));

        Ok(())
    }

    #[test]
    fn test_scan_directory_error_handling() {
        let discovery = ModelDiscovery::new();
        let mut models = Vec::new();

        // Try to scan a file (not a directory) - should fail
        let temp_dir = TempDir::new().unwrap();
        let file_path = temp_dir.path().join("not_a_dir.txt");
        fs::write(&file_path, "content").unwrap();

        let result = discovery.scan_directory(&file_path, &mut models);
        assert!(result.is_err());
    }

    #[test]
    fn test_model_format_serialization() {
        // Test that ModelFormat can be serialized/deserialized
        let gguf = ModelFormat::Gguf;
        let safetensors = ModelFormat::SafeTensors;

        let gguf_json = serde_json::to_string(&gguf).unwrap();
        let safetensors_json = serde_json::to_string(&safetensors).unwrap();

        assert!(gguf_json.contains("Gguf"));
        assert!(safetensors_json.contains("SafeTensors"));

        let gguf_parsed: ModelFormat = serde_json::from_str(&gguf_json).unwrap();
        let safetensors_parsed: ModelFormat = serde_json::from_str(&safetensors_json).unwrap();

        assert!(matches!(gguf_parsed, ModelFormat::Gguf));
        assert!(matches!(safetensors_parsed, ModelFormat::SafeTensors));
    }

    #[test]
    fn test_discovered_model_serialization() {
        let model = DiscoveredModel {
            name: "test-model".to_string(),
            path: PathBuf::from("/path/to/model.gguf"),
            format: ModelFormat::Gguf,
            size_bytes: Some(1024),
        };

        let json = serde_json::to_string(&model).unwrap();
        let parsed: DiscoveredModel = serde_json::from_str(&json).unwrap();

        assert_eq!(parsed.name, "test-model");
        assert_eq!(parsed.path, PathBuf::from("/path/to/model.gguf"));
        assert!(matches!(parsed.format, ModelFormat::Gguf));
        assert_eq!(parsed.size_bytes, Some(1024));
    }

    #[test]
    fn test_discovered_model_debug_format() {
        let model = DiscoveredModel {
            name: "test".to_string(),
            path: PathBuf::from("/test.gguf"),
            format: ModelFormat::Gguf,
            size_bytes: Some(512),
        };

        let debug_str = format!("{:?}", model);
        assert!(debug_str.contains("test"));
        assert!(debug_str.contains("test.gguf"));
        assert!(debug_str.contains("Gguf"));
        assert!(debug_str.contains("512"));
    }

    #[test]
    fn test_model_discovery_debug_format() {
        let mut discovery = ModelDiscovery::new();
        discovery.add_search_path(PathBuf::from("/test"));

        let debug_str = format!("{:?}", discovery);
        assert!(debug_str.contains("ModelDiscovery"));
        assert!(debug_str.contains("/test"));
    }

    #[test]
    fn test_file_stem_edge_cases() {
        let discovery = ModelDiscovery::new();

        // Test files with dots in name
        let temp_dir = TempDir::new().unwrap();
        let complex_name = temp_dir.path().join("model.v1.0.final.gguf");
        fs::write(&complex_name, "content").unwrap();

        let model = discovery.analyze_model_file(&complex_name).unwrap();
        assert_eq!(model.name, "model.v1.0.final");

        // Test file with no stem (shouldn't happen with our extension check, but test anyway)
        let no_stem = PathBuf::from(".gguf");
        if let Ok(model) = discovery.analyze_model_file(&no_stem) {
            assert_eq!(model.name, "unknown");
        }
    }

    #[test]
    fn test_environment_variable_edge_cases() {
        // Test from_env when SHIMMY_BASE_GGUF has no parent
        env::set_var("SHIMMY_BASE_GGUF", "model.gguf"); // No directory separator

        let discovery = ModelDiscovery::from_env();

        // Should still create discovery object, just won't add parent path
        // Verify discovery object was created successfully
        assert!(!discovery.search_paths.is_empty()); // Validates that search paths were added

        env::remove_var("SHIMMY_BASE_GGUF");
    }

    #[test]
    fn test_from_env_no_environment_variables() {
        // Clear all relevant environment variables
        env::remove_var("SHIMMY_BASE_GGUF");
        env::remove_var("SHIMMY_MODEL_PATHS");
        env::remove_var("OLLAMA_MODELS");
        env::remove_var("HOME");
        env::remove_var("USERPROFILE");

        // Should not panic regardless of env state
        let discovery = ModelDiscovery::from_env();

        // Platform-default paths are still added (e.g. Windows Ollama drive scan).
        // Just verify the result is a valid, non-empty collection.
        assert!(
            discovery.search_paths.len() < 100,
            "Unexpected explosion in default search paths: {}",
            discovery.search_paths.len()
        );
    }

    #[test]
    fn test_multiple_search_paths() -> Result<()> {
        let temp_dir1 = TempDir::new()?;
        let temp_dir2 = TempDir::new()?;

        // Create models in different directories
        fs::write(temp_dir1.path().join("model1.gguf"), "content1")?;
        fs::write(temp_dir2.path().join("model2.safetensors"), "content2")?;

        let mut discovery = ModelDiscovery::new();
        discovery.add_search_path(temp_dir1.path().to_path_buf());
        discovery.add_search_path(temp_dir2.path().to_path_buf());

        let models = discovery.discover_models()?;

        assert_eq!(models.len(), 2);
        let names: Vec<String> = models.iter().map(|m| m.name.clone()).collect();
        assert!(names.contains(&"model1".to_string()));
        assert!(names.contains(&"model2".to_string()));

        Ok(())
    }

    /// Test cases for Issue #80 - LLM model filtering to exclude non-LLM models
    #[test]
    fn test_is_llm_model_excludes_non_llm_models() {
        let discovery = ModelDiscovery::new();

        // Test exclusion of image generation models
        assert!(!discovery.is_llm_model(&PathBuf::from("flux-dev.gguf")));
        assert!(!discovery.is_llm_model(&PathBuf::from("stable-diffusion-xl.safetensors")));
        assert!(!discovery.is_llm_model(&PathBuf::from("sdxl-base.gguf")));
        assert!(!discovery.is_llm_model(&PathBuf::from("dalle-mini.gguf")));

        // Test exclusion of video models
        assert!(!discovery.is_llm_model(&PathBuf::from("video-generator.gguf")));
        assert!(!discovery.is_llm_model(&PathBuf::from("animate-diff.safetensors")));
        assert!(!discovery.is_llm_model(&PathBuf::from("motion-model.gguf")));

        // Test exclusion of audio models
        assert!(!discovery.is_llm_model(&PathBuf::from("whisper-large.gguf")));
        assert!(!discovery.is_llm_model(&PathBuf::from("speech-t5.safetensors")));
        assert!(!discovery.is_llm_model(&PathBuf::from("tts-model.gguf")));
        assert!(!discovery.is_llm_model(&PathBuf::from("voice-clone.gguf")));

        // Test exclusion of CLIP and embedding models
        assert!(!discovery.is_llm_model(&PathBuf::from("clip-vit-base.gguf")));
        assert!(!discovery.is_llm_model(&PathBuf::from("text-embeddings.safetensors")));
        assert!(!discovery.is_llm_model(&PathBuf::from("vision-encoder.gguf")));

        // Test exclusion of specialized model components
        assert!(!discovery.is_llm_model(&PathBuf::from("vae-encoder.safetensors")));
        assert!(!discovery.is_llm_model(&PathBuf::from("unet-model.gguf")));
        assert!(!discovery.is_llm_model(&PathBuf::from("controlnet-canny.safetensors")));
        assert!(!discovery.is_llm_model(&PathBuf::from("lora-adapter.gguf")));
    }

    #[test]
    fn test_is_llm_model_includes_llm_models() {
        let discovery = ModelDiscovery::new();

        // Test inclusion of popular LLM models
        assert!(discovery.is_llm_model(&PathBuf::from("llama-2-7b.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("mistral-7b-instruct.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("qwen-14b.safetensors")));
        assert!(discovery.is_llm_model(&PathBuf::from("phi-3-mini.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("gemma-2b.safetensors")));
        assert!(discovery.is_llm_model(&PathBuf::from("codellama-34b.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("vicuna-13b.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("alpaca-7b.safetensors")));

        // Test inclusion of instruction-tuned models
        assert!(discovery.is_llm_model(&PathBuf::from("orca-2-7b.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("falcon-40b.safetensors")));
        assert!(discovery.is_llm_model(&PathBuf::from("mpt-7b-chat.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("gpt4all-falcon.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("chatglm-6b.safetensors")));

        // Test inclusion of newer models
        assert!(discovery.is_llm_model(&PathBuf::from("baichuan-13b.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("yi-34b.safetensors")));
        assert!(discovery.is_llm_model(&PathBuf::from("deepseek-coder.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("mixtral-8x7b.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("solar-10.7b.safetensors")));

        // Test inclusion of fine-tuned variants
        assert!(discovery.is_llm_model(&PathBuf::from("openchat-3.5.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("starling-lm-7b.safetensors")));
        assert!(discovery.is_llm_model(&PathBuf::from("wizardlm-13b.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("dolphin-mixtral.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("nous-hermes-2.safetensors")));
        assert!(discovery.is_llm_model(&PathBuf::from("airoboros-34b.gguf")));
    }

    #[test]
    fn test_is_llm_model_safetensors_permissive() {
        let discovery = ModelDiscovery::new();

        // SafeTensors files should be more permissive (unless they match exclusion patterns)
        assert!(discovery.is_llm_model(&PathBuf::from("unknown-model.safetensors")));
        assert!(discovery.is_llm_model(&PathBuf::from("custom-transformer.safetensors")));
        assert!(discovery.is_llm_model(&PathBuf::from("experimental-llm.safetensors")));

        // But still exclude obvious non-LLM SafeTensors
        assert!(!discovery.is_llm_model(&PathBuf::from("stable-diffusion.safetensors")));
        assert!(!discovery.is_llm_model(&PathBuf::from("whisper-base.safetensors")));
        assert!(!discovery.is_llm_model(&PathBuf::from("clip-model.safetensors")));
    }

    #[test]
    fn test_is_llm_model_gguf_default_inclusion() {
        let discovery = ModelDiscovery::new();

        // GGUF files without clear patterns should be included by default
        assert!(discovery.is_llm_model(&PathBuf::from("unknown-model.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("custom-7b.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("experimental.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("language-model.gguf")));

        // Unless they match exclusion patterns
        assert!(!discovery.is_llm_model(&PathBuf::from("flux-unknown.gguf")));
        assert!(!discovery.is_llm_model(&PathBuf::from("sd-custom.gguf")));
        assert!(!discovery.is_llm_model(&PathBuf::from("whisper-custom.gguf")));
    }

    #[test]
    fn test_is_llm_model_case_insensitive() {
        let discovery = ModelDiscovery::new();

        // Test case insensitivity for both inclusion and exclusion
        assert!(discovery.is_llm_model(&PathBuf::from("LLAMA-2-7B.GGUF")));
        assert!(discovery.is_llm_model(&PathBuf::from("Mistral-7B.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("PHI-3-mini.SAFETENSORS")));

        assert!(!discovery.is_llm_model(&PathBuf::from("FLUX-DEV.GGUF")));
        assert!(!discovery.is_llm_model(&PathBuf::from("Stable-Diffusion.safetensors")));
        assert!(!discovery.is_llm_model(&PathBuf::from("WHISPER-LARGE.gguf")));
    }

    #[test]
    fn test_is_llm_model_edge_cases() {
        let discovery = ModelDiscovery::new();

        // Test models with multiple matching patterns
        assert!(discovery.is_llm_model(&PathBuf::from("llama-mistral-hybrid.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("phi-gemma-merged.safetensors")));

        // Test models that could be ambiguous
        assert!(discovery.is_llm_model(&PathBuf::from("gpt-4-turbo.gguf"))); // GPT pattern clearly LLM
        assert!(!discovery.is_llm_model(&PathBuf::from("vision-gpt-clip.gguf"))); // CLIP pattern excludes

        // Test empty/minimal filenames
        assert!(discovery.is_llm_model(&PathBuf::from("model.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("test.safetensors")));

        // Test complex filenames with versions and metadata
        assert!(discovery.is_llm_model(&PathBuf::from("llama-2-7b-chat-hf-q4_0.gguf")));
        assert!(discovery.is_llm_model(&PathBuf::from("mistral-7b-instruct-v0.1-fp16.safetensors")));
        assert!(!discovery.is_llm_model(&PathBuf::from(
            "stable-diffusion-xl-base-1.0-fp16.safetensors"
        )));
    }

    #[test]
    fn test_model_filtering_integration() -> Result<()> {
        let temp_dir = TempDir::new()?;

        // Create a mix of LLM and non-LLM model files
        fs::write(temp_dir.path().join("llama-2-7b.gguf"), "llm content")?;
        fs::write(
            temp_dir.path().join("mistral-instruct.safetensors"),
            "llm content",
        )?;
        fs::write(temp_dir.path().join("flux-dev.gguf"), "image model content")?;
        fs::write(
            temp_dir.path().join("whisper-large.gguf"),
            "audio model content",
        )?;
        fs::write(
            temp_dir.path().join("clip-vit.safetensors"),
            "vision model content",
        )?;
        fs::write(
            temp_dir.path().join("unknown-model.gguf"),
            "unknown content",
        )?;

        let mut discovery = ModelDiscovery::new();
        discovery.add_search_path(temp_dir.path().to_path_buf());

        let models = discovery.discover_models()?;

        // Should only find the LLM models (llama, mistral, unknown)
        assert_eq!(models.len(), 3);

        let names: Vec<String> = models.iter().map(|m| m.name.clone()).collect();
        assert!(names.contains(&"llama-2-7b".to_string()));
        assert!(names.contains(&"mistral-instruct".to_string()));
        assert!(names.contains(&"unknown-model".to_string()));

        // Should NOT contain the excluded models
        assert!(!names.contains(&"flux-dev".to_string()));
        assert!(!names.contains(&"whisper-large".to_string()));
        assert!(!names.contains(&"clip-vit".to_string()));

        Ok(())
    }
}