Implement sidebar navigation and model management features. Refactor App component to utilize a Sidebar for tab navigation, integrating ProfileList, GenerationForm, HistoryTable, and ServerStatus components. Introduce ModelManagement and ModelProgress components for handling AI model downloads and status updates. Enhance CSS for sidebar styling and add progress tracking functionality in the backend for model downloads.

backend/transcribe.py

@@ -6,6 +6,8 @@ from typing import Optional, List, Dict
 import torch
 import numpy as np
 from pathlib import Path
+from .utils.progress import get_progress_manager
+from .utils.hf_progress import HFProgressTracker, create_hf_progress_callback
 
 
 class WhisperModel:
@@ -48,18 +50,33 @@ class WhisperModel:
             
             model_name = f"openai/whisper-{model_size}"
             
+            # Set up progress tracking
+            progress_manager = get_progress_manager()
+            progress_model_name = f"whisper-{model_size}"
+            
             print(f"Loading Whisper model {model_size} on {self.device}...")
             
-            self.processor = WhisperProcessor.from_pretrained(model_name)
-            self.model = WhisperForConditionalGeneration.from_pretrained(model_name)
-            self.model.to(self.device)
+            # Set up progress callback
+            progress_callback = create_hf_progress_callback(progress_model_name, progress_manager)
+            tracker = HFProgressTracker(progress_callback)
             
+            # Use progress tracker during download
+            with tracker.patch_download():
+                self.processor = WhisperProcessor.from_pretrained(model_name)
+                self.model = WhisperForConditionalGeneration.from_pretrained(model_name)
+            
+            self.model.to(self.device)
             self.model_size = model_size
             
+            # Mark as complete
+            progress_manager.mark_complete(progress_model_name)
+            
             print(f"Whisper model {model_size} loaded successfully")
             
         except Exception as e:
             print(f"Error loading Whisper model: {e}")
+            progress_manager = get_progress_manager()
+            progress_manager.mark_error(f"whisper-{model_size}", str(e))
             raise
     
     def unload_model(self):