scirs2_text/huggingface_compat/
hub.rs

1//! Hugging Face Hub integration for model discovery and download
2//!
3//! This module provides functionality for interacting with the Hugging Face
4//! model hub to discover, download, and manage models.
5
6use crate::error::{Result, TextError};
7use std::collections::HashMap;
8use std::path::{Path, PathBuf};
9
10#[cfg(feature = "serde-support")]
11use serde::{Deserialize, Serialize};
12
13/// Hugging Face Hub interface
14#[derive(Debug)]
15pub struct HfHub {
16    /// Cache directory for downloaded models
17    cache_dir: PathBuf,
18    /// API token for authenticated requests
19    token: Option<String>,
20    /// Model repository cache
21    model_cache: HashMap<String, HfModelInfo>,
22}
23
24impl HfHub {
25    /// Create new HF Hub interface
26    pub fn new() -> Self {
27        let cache_dir = std::env::var("HF_HOME")
28            .or_else(|_| std::env::var("HUGGINGFACE_HUB_CACHE"))
29            .map(PathBuf::from)
30            .unwrap_or_else(|_| {
31                let mut home = std::env::var("HOME")
32                    .map(PathBuf::from)
33                    .unwrap_or_else(|_| PathBuf::from("."));
34                home.push(".cache");
35                home.push("huggingface");
36                home.push("hub");
37                home
38            });
39
40        Self {
41            cache_dir,
42            token: None,
43            model_cache: HashMap::new(),
44        }
45    }
46
47    /// Set authentication token
48    pub fn with_token(mut self, token: String) -> Self {
49        self.token = Some(token);
50        self
51    }
52
53    /// Set cache directory
54    pub fn with_cache_dir<P: AsRef<Path>>(mut self, cache_dir: P) -> Self {
55        self.cache_dir = cache_dir.as_ref().to_path_buf();
56        self
57    }
58
59    /// List available models
60    pub fn list_models(&self, filter: Option<&str>) -> Result<Vec<String>> {
61        // Simulated model list (in practice, this would make HTTP requests)
62        let models = vec![
63            "bert-base-uncased",
64            "bert-large-uncased",
65            "distilbert-base-uncased",
66            "roberta-base",
67            "roberta-large",
68            "gpt2",
69            "gpt2-medium",
70            "gpt2-large",
71            "t5-small",
72            "t5-base",
73            "t5-large",
74            "facebook/bart-base",
75            "facebook/bart-large",
76            "microsoft/DialoGPT-medium",
77            "microsoft/DialoGPT-large",
78        ];
79
80        let filtered_models: Vec<String> = models
81            .into_iter()
82            .filter(|model| filter.is_none_or(|f| model.to_lowercase().contains(&f.to_lowercase())))
83            .map(|s| s.to_string())
84            .collect();
85
86        Ok(filtered_models)
87    }
88
89    /// Get model information
90    pub fn model_info(&mut self, model_id: &str) -> Result<HfModelInfo> {
91        if let Some(info) = self.model_cache.get(model_id) {
92            return Ok(info.clone());
93        }
94
95        // Create mock model info (in practice, this would fetch from API)
96        let info = HfModelInfo {
97            model_id: model_id.to_string(),
98            tags: vec!["pytorch".to_string(), "transformers".to_string()],
99            pipeline_tag: Some(self.infer_pipeline_tag(model_id)),
100            downloads: 1000000,
101            likes: 500,
102            library_name: Some("transformers".to_string()),
103        };
104
105        self.model_cache.insert(model_id.to_string(), info.clone());
106        Ok(info)
107    }
108
109    /// Download model files
110    pub fn download_model<P: AsRef<Path>>(
111        &self,
112        model_id: &str,
113        cache_dir: Option<P>,
114    ) -> Result<PathBuf> {
115        let download_dir = cache_dir
116            .map(|p| p.as_ref().to_path_buf())
117            .unwrap_or_else(|| self.cache_dir.join(model_id));
118
119        // Create download directory
120        std::fs::create_dir_all(&download_dir)
121            .map_err(|e| TextError::IoError(format!("Failed to create download directory: {e}")))?;
122
123        // In a real implementation, this would download files from the hub
124        // For now, create placeholder files
125        let files = [
126            "config.json",
127            "pytorch_model.bin",
128            "tokenizer.json",
129            "vocab.txt",
130        ];
131
132        for file in &files {
133            let file_path = download_dir.join(file);
134            if !file_path.exists() {
135                let content = if file == &"config.json" {
136                    // Create a valid JSON config for testing
137                    r#"{
138  "architectures": ["BertModel"],
139  "model_type": "bert",
140  "num_attention_heads": 12,
141  "hidden_size": 768,
142  "intermediate_size": 3072,
143  "num_hidden_layers": 12,
144  "vocab_size": 30522,
145  "max_position_embeddings": 512,
146  "extraconfig": {}
147}"#
148                    .to_string()
149                } else {
150                    format!("# Placeholder {file} for {model_id}")
151                };
152                std::fs::write(&file_path, content)
153                    .map_err(|e| TextError::IoError(format!("Failed to create {file}: {e}")))?;
154            }
155        }
156
157        Ok(download_dir)
158    }
159
160    /// Upload model to hub
161    pub fn upload_model<P: AsRef<Path>>(
162        &self,
163        model_path: P,
164        repo_id: &str,
165        commit_message: Option<&str>,
166    ) -> Result<()> {
167        let model_path = model_path.as_ref();
168
169        if !model_path.exists() {
170            return Err(TextError::InvalidInput(
171                "Model path does not exist".to_string(),
172            ));
173        }
174
175        // Validate required files
176        let required_files = ["config.json"];
177        for file in &required_files {
178            if !model_path.join(file).exists() {
179                return Err(TextError::InvalidInput(format!(
180                    "Required file {file} not found"
181                )));
182            }
183        }
184
185        println!(
186            "Would upload model from {} to {} with message: {}",
187            model_path.display(),
188            repo_id,
189            commit_message.unwrap_or("Upload model")
190        );
191
192        Ok(())
193    }
194
195    /// Create model repository
196    pub fn create_repo(&self, repo_id: &str, private: bool) -> Result<()> {
197        if self.token.is_none() {
198            return Err(TextError::InvalidInput(
199                "Authentication token required".to_string(),
200            ));
201        }
202
203        println!("Would create repository {} (private: {})", repo_id, private);
204
205        Ok(())
206    }
207
208    /// Get cached model path
209    pub fn get_cached_model_path(&self, model_id: &str) -> PathBuf {
210        self.cache_dir.join(model_id)
211    }
212
213    fn infer_pipeline_tag(&self, model_id: &str) -> String {
214        if model_id.contains("bert") || model_id.contains("roberta") {
215            "text-classification".to_string()
216        } else if model_id.contains("gpt") || model_id.contains("t5") {
217            "text-generation".to_string()
218        } else if model_id.contains("bart") {
219            "summarization".to_string()
220        } else {
221            "feature-extraction".to_string()
222        }
223    }
224}
225
226impl Default for HfHub {
227    fn default() -> Self {
228        Self::new()
229    }
230}
231
232/// Model information from Hugging Face Hub
233#[derive(Debug, Clone)]
234#[cfg_attr(feature = "serde-support", derive(Serialize, Deserialize))]
235pub struct HfModelInfo {
236    /// Model identifier
237    pub model_id: String,
238    /// Model tags
239    pub tags: Vec<String>,
240    /// Pipeline task type
241    pub pipeline_tag: Option<String>,
242    /// Download count
243    pub downloads: u64,
244    /// Like count
245    pub likes: u64,
246    /// Library name (e.g., "transformers")
247    pub library_name: Option<String>,
248}
scirs2_text/huggingface_compat/hub.rs

scirs2_text/huggingface_compat/
hub.rs
