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.
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Claude
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"""
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Inference Module für Modell-Evaluation
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Lädt Base- und Fine-tuned Models für Vergleiche
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"""
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from pathlib import Path
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from typing import Optional, Dict
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import threading
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class ModelInference:
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"""Handhabt Modell-Inferenz für Base und Fine-tuned Models"""
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def __init__(self, models_dir: str = "models", output_dir: str = "output"):
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self.models_dir = Path(models_dir)
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self.output_dir = Path(output_dir)
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self.base_model = None
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self.finetuned_model = None
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self.model_lock = threading.Lock()
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def load_base_model(self, model_name: str) -> bool:
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"""Lädt das Basis-Modell"""
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try:
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# Import MLX nur bei Bedarf
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from mlx_lm import load
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model_path = self.models_dir / model_name
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if not model_path.exists():
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return False
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with self.model_lock:
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self.base_model = load(str(model_path))
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return True
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except Exception as e:
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print(f"Error loading base model: {e}")
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return False
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def load_finetuned_model(self, model_name: str, adapter_path: str) -> bool:
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"""Lädt das Fine-tuned Modell (Base + LoRA Adapter)"""
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try:
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from mlx_lm import load
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model_path = self.models_dir / model_name
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adapter_file = Path(adapter_path)
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if not model_path.exists() or not adapter_file.exists():
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return False
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with self.model_lock:
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# Lade Base Model mit Adapter
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self.finetuned_model = load(
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str(model_path),
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adapter_path=str(adapter_file)
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)
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return True
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except Exception as e:
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print(f"Error loading finetuned model: {e}")
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return False
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def generate(self, prompt: str, model_type: str = 'base',
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max_tokens: int = 512, temperature: float = 0.7) -> str:
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"""
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Generiert Text mit dem gewählten Modell
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Args:
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prompt: Input prompt
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model_type: 'base' oder 'finetuned'
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max_tokens: Maximale Anzahl Tokens
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temperature: Sampling temperature
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Returns:
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Generierter Text
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"""
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try:
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from mlx_lm import generate as mlx_generate
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model = self.base_model if model_type == 'base' else self.finetuned_model
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if model is None:
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return f"Error: {model_type} model not loaded"
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with self.model_lock:
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# MLX-LM generate
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result = mlx_generate(
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model,
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prompt=prompt,
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max_tokens=max_tokens,
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temp=temperature
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)
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return result
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except Exception as e:
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return f"Error during generation: {str(e)}"
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def generate_comparison(self, prompt: str, max_tokens: int = 512,
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temperature: float = 0.7) -> Dict[str, str]:
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"""
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Generiert mit beiden Modellen für Vergleich
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Returns:
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Dict mit 'base' und 'finetuned' Outputs
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"""
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result = {
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'base': None,
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'finetuned': None
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}
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if self.base_model:
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result['base'] = self.generate(
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prompt, 'base', max_tokens, temperature
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)
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if self.finetuned_model:
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result['finetuned'] = self.generate(
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prompt, 'finetuned', max_tokens, temperature
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)
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return result
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def format_mail_prompt(self, task_type: str, mail_body: str) -> str:
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"""Formatiert einen Prompt basierend auf Task-Type"""
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task_prompts = {
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'Zusammenfassen': 'Fasse folgende E-Mail zusammen:',
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'Antwort schreiben': 'Schreibe eine Antwort auf folgende E-Mail:',
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'Kategorisieren': 'Kategorisiere folgende E-Mail:',
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'Action Items': 'Extrahiere die Action Items aus folgender E-Mail:',
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'Custom': 'Bearbeite folgende E-Mail:'
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}
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instruction = task_prompts.get(task_type, task_prompts['Custom'])
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return f"{instruction}\n\n{mail_body}"
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def get_test_prompts(self) -> Dict[str, str]:
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"""Vordefinierte Test-Prompts"""
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return {
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'Zusammenfassen': self.format_mail_prompt(
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'Zusammenfassen',
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"""Betreff: Q4 Projektupdate
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Hallo Team,
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ich wollte euch ein kurzes Update zum aktuellen Projektstand geben.
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Wir haben letzte Woche die neue API-Integration abgeschlossen und erfolgreich getestet.
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Die Performance-Tests zeigen eine Verbesserung von 40% gegenüber der alten Implementierung.
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Nächste Woche starten wir mit der Frontend-Anpassung. Maria und Tom werden das Design
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überarbeiten, während ich mich um die Backend-Anbindung kümmere.
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Der Go-Live ist weiterhin für Ende des Monats geplant.
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Beste Grüße
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Alex"""
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),
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'Antwort schreiben': self.format_mail_prompt(
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'Antwort schreiben',
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"""Betreff: Frage zu Invoice #2847
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Hallo,
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ich habe eine Frage zur Rechnung #2847 vom 15. März.
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Der Betrag scheint nicht mit unserem Angebot übereinzustimmen.
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Könnten Sie das bitte prüfen?
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Danke
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Michael"""
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),
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'Action Items': self.format_mail_prompt(
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'Action Items',
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"""Betreff: Meeting Notes - Produktlaunch
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Hi alle,
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hier die wichtigsten Punkte vom heutigen Meeting:
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- Sarah bereitet die Pressemitteilung vor (Deadline: Freitag)
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- Marketing-Team erstellt Social Media Content (nächste Woche)
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- Ich kümmere mich um die Influencer-Kontakte
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- Wir brauchen noch finale Produktfotos vom Design-Team
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- Launch-Event ist am 1. April - Location muss noch gebucht werden
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Bitte gebt bis Mittwoch Bescheid ob ihr eure Aufgaben schaffen könnt.
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Lisa"""
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)
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}
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def unload_models(self):
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"""Entlädt Modelle aus dem Speicher"""
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with self.model_lock:
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self.base_model = None
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self.finetuned_model = None
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def get_loaded_models(self) -> Dict[str, bool]:
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"""Gibt zurück welche Modelle geladen sind"""
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return {
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'base': self.base_model is not None,
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'finetuned': self.finetuned_model is not None
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}
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