use anyhow::Result;
use burncloud_common::{BurnCloudError, ModelInfo};
use std::collections::HashMap;
pub struct ModelManager {
models: HashMap<String, ModelInfo>,
models_dir: String,
}
impl ModelManager {
pub fn new(models_dir: String) -> Self {
Self {
models: HashMap::new(),
models_dir,
}
}
pub async fn pull_model(&mut self, name: &str) -> Result<()> {
println!("正在下载模型: {}", name);
tokio::time::sleep(tokio::time::Duration::from_secs(1)).await;
let model_info = ModelInfo {
name: name.to_string(),
size: 1024 * 1024 * 100, downloaded: true,
path: Some(format!("{}/{}", self.models_dir, name)),
};
self.models.insert(name.to_string(), model_info);
println!("模型 {} 下载完成", name);
Ok(())
}
pub async fn run_model(&self, name: &str, prompt: Option<&str>) -> Result<String> {
let model = self
.models
.get(name)
.ok_or_else(|| BurnCloudError::ModelNotFound(name.to_string()))?;
if !model.downloaded {
return Err(BurnCloudError::ModelNotFound(format!("模型 {} 未下载", name)).into());
}
println!("正在运行模型: {}", name);
if let Some(p) = prompt {
println!("输入: {}", p);
}
tokio::time::sleep(tokio::time::Duration::from_millis(500)).await;
let response = match name {
"llama3.2" => "Hello! I'm Llama 3.2. How can I help you today?",
"gemma3" => "Hi there! I'm Gemma 3. What would you like to know?",
_ => "I'm a language model. How can I assist you?",
};
Ok(response.to_string())
}
pub fn list_models(&self) -> Vec<&ModelInfo> {
self.models.values().collect()
}
pub fn get_model(&self, name: &str) -> Option<&ModelInfo> {
self.models.get(name)
}
}