Add complete Mail Fine-Tuning Web-App for macOS Apple Silicon

Implemented a full-stack web application for fine-tuning LLMs on email data, optimized for Apple Silicon (M4 Pro with 24GB RAM).

Features:
- Mail import with drag & drop support (.mbox, .eml, .txt)
- Automated mail cleaning and preprocessing
- Interactive labeling interface with keyboard shortcuts
- Training data export to JSONL format
- MLX-based LoRA fine-tuning with live updates
- Model evaluation and comparison interface
- Server-Sent Events for real-time training progress
- Dark theme UI optimized for extended use

Technical Stack:
- Backend: FastAPI with SQLite database
- Frontend: Vanilla HTML/CSS/JavaScript (no external dependencies)
- ML Framework: MLX for Apple Silicon optimization
- Models: Support for Mistral 7B and Llama 3 8B via MLX

Components:
- data_manager.py: SQLite operations for mail storage and labeling
- mail_parser.py: Parser for multiple mail formats with cleaning
- training.py: MLX training wrapper with LoRA support
- inference.py: Model loading and inference for evaluation
- main.py: FastAPI backend with REST API and SSE
- Frontend: Complete UI with all features

Documentation:
- Comprehensive README with installation and usage guide
- Quick-start guide for rapid setup
- Example mails for testing
- Troubleshooting and best practices

Ready for local deployment and fine-tuning workflows.

training/start.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+
+# Mail Fine-Tuning App Startup Script
+
+echo "🚀 Starting Mail Fine-Tuning App..."
+echo ""
+
+# Check if venv exists
+if [ ! -d "venv" ]; then
+    echo "❌ Virtual environment not found!"
+    echo "Please run: python3 -m venv venv && source venv/bin/activate && pip install -r requirements.txt"
+    exit 1
+fi
+
+# Activate venv
+source venv/bin/activate
+
+# Check if dependencies are installed
+if ! python -c "import fastapi" 2>/dev/null; then
+    echo "❌ Dependencies not installed!"
+    echo "Please run: pip install -r requirements.txt"
+    exit 1
+fi
+
+# Create necessary directories
+mkdir -p data models output
+
+# Start server
+echo "✅ Starting server on http://localhost:8000"
+echo ""
+echo "Press Ctrl+C to stop"
+echo ""
+
+cd backend
+python main.py