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750
model_converter.py
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750
model_converter.py
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#!/usr/bin/env python3
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"""
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Model Conversion and Editing Tools for DarkHal 2.0
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Provides comprehensive model conversion between formats, quantization options,
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and model editing capabilities.
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"""
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import tkinter as tk
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from tkinter import ttk, messagebox, filedialog, scrolledtext
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import os
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import sys
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import json
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import subprocess
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import threading
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import queue
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import shutil
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from pathlib import Path
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from typing import Optional, Dict, Any, List, Tuple
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from datetime import datetime
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import tempfile
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class ModelConverter:
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"""Handles model conversion operations."""
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SUPPORTED_FORMATS = {
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'gguf': 'GGUF (llama.cpp)',
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'safetensors': 'SafeTensors (HuggingFace)',
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'bin': 'PyTorch Binary',
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'pt': 'PyTorch',
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'pth': 'PyTorch State Dict',
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'onnx': 'ONNX',
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'tflite': 'TensorFlow Lite',
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'h5': 'Keras/HDF5'
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}
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QUANTIZATION_TYPES = {
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'q4_0': 'Q4_0 - 4-bit (smallest, lower quality)',
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'q4_1': 'Q4_1 - 4-bit (small, better than q4_0)',
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'q4_k_m': 'Q4_K_M - 4-bit (medium, recommended)',
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'q4_k_s': 'Q4_K_S - 4-bit (small)',
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'q5_0': 'Q5_0 - 5-bit',
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'q5_1': 'Q5_1 - 5-bit (better than q5_0)',
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'q5_k_m': 'Q5_K_M - 5-bit (medium, recommended)',
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'q5_k_s': 'Q5_K_S - 5-bit (small)',
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'q6_k': 'Q6_K - 6-bit (good quality/size ratio)',
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'q8_0': 'Q8_0 - 8-bit (high quality)',
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'f16': 'FP16 - 16-bit float',
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'f32': 'FP32 - 32-bit float (original)'
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}
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def __init__(self):
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self.conversion_queue = queue.Queue()
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self.current_process = None
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def get_model_info(self, model_path: str) -> Dict[str, Any]:
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"""Get information about a model file."""
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path = Path(model_path)
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if not path.exists():
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return None
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info = {
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'name': path.stem,
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'path': str(path),
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'format': path.suffix.lower().lstrip('.'),
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'size': path.stat().st_size,
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'size_mb': path.stat().st_size / (1024 * 1024),
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'modified': datetime.fromtimestamp(path.stat().st_mtime)
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}
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# Try to extract additional metadata
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if info['format'] == 'gguf':
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info.update(self._get_gguf_info(path))
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elif info['format'] in ['safetensors', 'bin']:
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info.update(self._get_hf_info(path))
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return info
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def _get_gguf_info(self, path: Path) -> Dict[str, Any]:
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"""Extract GGUF model information."""
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info = {}
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try:
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# Try to use llama-cpp-python if available
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from llama_cpp import Llama
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# This would require actually loading the model which is expensive
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# For now, extract from filename
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name = path.stem.lower()
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# Detect quantization
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for q_type in self.QUANTIZATION_TYPES.keys():
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if q_type in name:
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info['quantization'] = q_type
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break
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# Detect model size
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import re
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size_match = re.search(r'(\d+)b', name, re.IGNORECASE)
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if size_match:
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info['parameters'] = f"{size_match.group(1)}B"
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except Exception:
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pass
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return info
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def _get_hf_info(self, path: Path) -> Dict[str, Any]:
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"""Extract HuggingFace model information."""
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info = {}
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try:
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# Look for config.json in the same directory
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config_path = path.parent / "config.json"
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if config_path.exists():
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with open(config_path, 'r') as f:
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config = json.load(f)
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info['model_type'] = config.get('model_type', 'unknown')
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info['architectures'] = config.get('architectures', [])
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info['vocab_size'] = config.get('vocab_size', 0)
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# Calculate parameters if possible
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if 'hidden_size' in config and 'num_hidden_layers' in config:
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hidden = config['hidden_size']
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layers = config['num_hidden_layers']
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vocab = config.get('vocab_size', 0)
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# Rough parameter estimation
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params = (hidden * hidden * 4 * layers + vocab * hidden) / 1e9
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info['parameters'] = f"{params:.1f}B"
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except Exception:
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pass
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return info
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def convert_to_gguf(self, input_path: str, output_path: str,
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quantization: str = 'q4_k_m',
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progress_callback: Optional[callable] = None) -> bool:
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"""Convert a model to GGUF format."""
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try:
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input_format = Path(input_path).suffix.lower().lstrip('.')
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if input_format == 'gguf':
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# Already GGUF, just quantize if needed
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return self.quantize_gguf(input_path, output_path, quantization, progress_callback)
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# For HuggingFace models, use convert.py from llama.cpp
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if input_format in ['safetensors', 'bin']:
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return self._convert_hf_to_gguf(input_path, output_path, quantization, progress_callback)
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# For other formats, try generic conversion
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return self._generic_convert(input_path, output_path, 'gguf', progress_callback)
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except Exception as e:
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if progress_callback:
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progress_callback(f"Error: {e}", 100, "error")
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return False
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def quantize_gguf(self, input_path: str, output_path: str,
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quantization: str = 'q4_k_m',
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progress_callback: Optional[callable] = None) -> bool:
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"""Quantize a GGUF model."""
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try:
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# Look for quantize executable
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quantize_exe = self._find_quantize_executable()
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if not quantize_exe:
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if progress_callback:
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progress_callback("Quantize executable not found", 100, "error")
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return False
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# Run quantization
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cmd = [str(quantize_exe), input_path, output_path, quantization]
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if progress_callback:
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progress_callback("Starting quantization...", 0, "info")
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process = subprocess.Popen(
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cmd,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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text=True,
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bufsize=1
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)
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self.current_process = process
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# Monitor progress
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while True:
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line = process.stderr.readline()
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if not line:
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break
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if progress_callback:
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# Parse progress from output
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if "%" in line:
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try:
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import re
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match = re.search(r'(\d+)%', line)
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if match:
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percent = int(match.group(1))
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progress_callback(line.strip(), percent, "info")
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except:
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progress_callback(line.strip(), -1, "info")
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else:
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progress_callback(line.strip(), -1, "info")
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process.wait()
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if process.returncode == 0:
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if progress_callback:
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progress_callback("Quantization complete!", 100, "success")
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return True
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else:
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if progress_callback:
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progress_callback("Quantization failed", 100, "error")
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return False
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except Exception as e:
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if progress_callback:
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progress_callback(f"Error: {e}", 100, "error")
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return False
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finally:
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self.current_process = None
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def _find_quantize_executable(self) -> Optional[Path]:
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"""Find the quantize executable."""
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# Common locations
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locations = [
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Path("llama-cpp-python/vendor/llama.cpp/quantize"),
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Path("llama.cpp/quantize"),
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Path("bin/quantize"),
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Path("quantize"),
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Path("quantize.exe")
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]
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for loc in locations:
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if loc.exists():
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return loc
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# Check in PATH
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import shutil
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exe = shutil.which("quantize")
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if exe:
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return Path(exe)
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return None
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def _convert_hf_to_gguf(self, input_path: str, output_path: str,
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quantization: str,
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progress_callback: Optional[callable] = None) -> bool:
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"""Convert HuggingFace model to GGUF."""
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try:
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# Look for convert.py script
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convert_script = self._find_convert_script()
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if not convert_script:
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if progress_callback:
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progress_callback("Convert script not found", 100, "error")
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return False
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# First convert to FP16 GGUF
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temp_gguf = output_path.replace('.gguf', '_fp16.gguf')
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cmd = [
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sys.executable,
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str(convert_script),
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str(Path(input_path).parent), # Model directory
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"--outfile", temp_gguf,
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"--outtype", "f16"
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]
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if progress_callback:
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progress_callback("Converting to GGUF format...", 0, "info")
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process = subprocess.Popen(
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cmd,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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text=True
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)
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stdout, stderr = process.communicate()
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if process.returncode != 0:
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if progress_callback:
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progress_callback(f"Conversion failed: {stderr}", 100, "error")
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return False
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if progress_callback:
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progress_callback("Conversion complete, quantizing...", 50, "info")
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# Then quantize if needed
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if quantization != 'f16':
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result = self.quantize_gguf(temp_gguf, output_path, quantization, progress_callback)
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# Clean up temp file
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try:
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os.remove(temp_gguf)
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except:
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pass
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return result
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else:
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# Just rename
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shutil.move(temp_gguf, output_path)
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if progress_callback:
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progress_callback("Conversion complete!", 100, "success")
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return True
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except Exception as e:
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if progress_callback:
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progress_callback(f"Error: {e}", 100, "error")
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return False
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def _find_convert_script(self) -> Optional[Path]:
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"""Find the convert.py script."""
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locations = [
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Path("llama-cpp-python/vendor/llama.cpp/convert.py"),
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Path("llama.cpp/convert.py"),
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Path("scripts/convert.py"),
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Path("convert.py")
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]
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for loc in locations:
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if loc.exists():
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return loc
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return None
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def _generic_convert(self, input_path: str, output_path: str,
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target_format: str,
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progress_callback: Optional[callable] = None) -> bool:
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"""Generic conversion using available tools."""
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# This would use tools like ONNX converters, TensorFlow converters, etc.
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# For now, return False as not implemented
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if progress_callback:
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progress_callback(f"Conversion to {target_format} not yet implemented", 100, "error")
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return False
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def merge_lora(self, base_model: str, lora_path: str, output_path: str,
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progress_callback: Optional[callable] = None) -> bool:
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"""Merge a LoRA adapter into a base model."""
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try:
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# This would use a LoRA merging tool
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# For now, simplified implementation
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if progress_callback:
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progress_callback("LoRA merging not yet implemented", 100, "error")
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return False
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except Exception as e:
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if progress_callback:
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progress_callback(f"Error: {e}", 100, "error")
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return False
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def split_model(self, input_path: str, output_dir: str,
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num_shards: int = 2,
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progress_callback: Optional[callable] = None) -> bool:
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"""Split a model into multiple shards."""
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try:
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# This would split large models for easier distribution
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if progress_callback:
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progress_callback("Model splitting not yet implemented", 100, "error")
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return False
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except Exception as e:
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if progress_callback:
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progress_callback(f"Error: {e}", 100, "error")
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return False
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class ModelConverterTab:
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"""Model conversion and editing tab for DarkHal 2.0."""
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def __init__(self, parent: ttk.Frame, settings_manager):
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self.parent = parent
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self.settings = settings_manager
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self.converter = ModelConverter()
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self.current_model = None
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self._build_ui()
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def _build_ui(self):
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"""Build the converter tab UI."""
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# Create paned window for split view
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paned = ttk.PanedWindow(self.parent, orient=tk.HORIZONTAL)
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paned.pack(fill=tk.BOTH, expand=True, padx=10, pady=10)
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# Left panel - Model Selection and Info
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left_frame = ttk.Frame(paned)
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paned.add(left_frame, weight=1)
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# Model Selection Frame
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select_frame = ttk.LabelFrame(left_frame, text="Model Selection", padding=10)
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select_frame.pack(fill=tk.X, pady=(0, 10))
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# Input model
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ttk.Label(select_frame, text="Input Model:").pack(anchor=tk.W)
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input_frame = ttk.Frame(select_frame)
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input_frame.pack(fill=tk.X, pady=(5, 10))
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self.input_path_var = tk.StringVar()
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self.input_entry = ttk.Entry(input_frame, textvariable=self.input_path_var)
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self.input_entry.pack(side=tk.LEFT, fill=tk.X, expand=True)
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ttk.Button(input_frame, text="Browse", command=self._browse_input).pack(side=tk.LEFT, padx=(5, 0))
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ttk.Button(input_frame, text="Analyze", command=self._analyze_model).pack(side=tk.LEFT, padx=(5, 0))
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# Model Information Frame
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info_frame = ttk.LabelFrame(left_frame, text="Model Information", padding=10)
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info_frame.pack(fill=tk.BOTH, expand=True, pady=(0, 10))
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self.info_text = scrolledtext.ScrolledText(info_frame, height=10, wrap=tk.WORD)
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self.info_text.pack(fill=tk.BOTH, expand=True)
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# Conversion Options Frame
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options_frame = ttk.LabelFrame(left_frame, text="Conversion Options", padding=10)
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options_frame.pack(fill=tk.X)
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# Output format
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ttk.Label(options_frame, text="Output Format:").grid(row=0, column=0, sticky=tk.W, pady=5)
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self.output_format_var = tk.StringVar(value="gguf")
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format_combo = ttk.Combobox(options_frame, textvariable=self.output_format_var,
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values=list(ModelConverter.SUPPORTED_FORMATS.keys()),
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state="readonly", width=20)
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format_combo.grid(row=0, column=1, sticky=tk.W, pady=5)
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format_combo.bind('<<ComboboxSelected>>', self._on_format_change)
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# Quantization (for GGUF)
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ttk.Label(options_frame, text="Quantization:").grid(row=1, column=0, sticky=tk.W, pady=5)
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self.quantization_var = tk.StringVar(value="q4_k_m")
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self.quant_combo = ttk.Combobox(options_frame, textvariable=self.quantization_var,
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values=list(ModelConverter.QUANTIZATION_TYPES.keys()),
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state="readonly", width=20)
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self.quant_combo.grid(row=1, column=1, sticky=tk.W, pady=5)
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# Output path
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ttk.Label(options_frame, text="Output Path:").grid(row=2, column=0, sticky=tk.W, pady=5)
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output_frame = ttk.Frame(options_frame)
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output_frame.grid(row=2, column=1, sticky=tk.W+tk.E, pady=5)
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self.output_path_var = tk.StringVar()
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self.output_entry = ttk.Entry(output_frame, textvariable=self.output_path_var, width=30)
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self.output_entry.pack(side=tk.LEFT, fill=tk.X, expand=True)
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ttk.Button(output_frame, text="Browse", command=self._browse_output).pack(side=tk.LEFT, padx=(5, 0))
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# Conversion button
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self.convert_btn = ttk.Button(options_frame, text="Start Conversion",
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command=self._start_conversion)
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self.convert_btn.grid(row=3, column=0, columnspan=2, pady=(10, 0))
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# Right panel - Advanced Tools and Progress
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right_frame = ttk.Frame(paned)
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paned.add(right_frame, weight=1)
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# Advanced Tools Frame
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tools_frame = ttk.LabelFrame(right_frame, text="Advanced Tools", padding=10)
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tools_frame.pack(fill=tk.X, pady=(0, 10))
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# Tool buttons
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tool_grid = ttk.Frame(tools_frame)
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tool_grid.pack(fill=tk.X)
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ttk.Button(tool_grid, text="Merge LoRA", command=self._open_lora_merger).grid(row=0, column=0, padx=2, pady=2)
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ttk.Button(tool_grid, text="Split Model", command=self._open_model_splitter).grid(row=0, column=1, padx=2, pady=2)
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ttk.Button(tool_grid, text="Optimize", command=self._open_optimizer).grid(row=0, column=2, padx=2, pady=2)
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ttk.Button(tool_grid, text="Batch Convert", command=self._open_batch_converter).grid(row=1, column=0, padx=2, pady=2)
|
||||
ttk.Button(tool_grid, text="Compare Models", command=self._open_model_compare).grid(row=1, column=1, padx=2, pady=2)
|
||||
ttk.Button(tool_grid, text="Edit Metadata", command=self._open_metadata_editor).grid(row=1, column=2, padx=2, pady=2)
|
||||
|
||||
# Quantization Comparison
|
||||
compare_frame = ttk.LabelFrame(right_frame, text="Quantization Comparison", padding=10)
|
||||
compare_frame.pack(fill=tk.X, pady=(0, 10))
|
||||
|
||||
# Comparison table
|
||||
columns = ("Type", "Size", "Quality", "Speed")
|
||||
self.compare_tree = ttk.Treeview(compare_frame, columns=columns, show="headings", height=6)
|
||||
|
||||
self.compare_tree.heading("Type", text="Type")
|
||||
self.compare_tree.heading("Size", text="Size")
|
||||
self.compare_tree.heading("Quality", text="Quality")
|
||||
self.compare_tree.heading("Speed", text="Speed")
|
||||
|
||||
self.compare_tree.column("Type", width=80)
|
||||
self.compare_tree.column("Size", width=80)
|
||||
self.compare_tree.column("Quality", width=80)
|
||||
self.compare_tree.column("Speed", width=80)
|
||||
|
||||
self.compare_tree.pack(fill=tk.X)
|
||||
|
||||
# Populate comparison table
|
||||
self._populate_comparison()
|
||||
|
||||
# Progress Frame
|
||||
progress_frame = ttk.LabelFrame(right_frame, text="Conversion Progress", padding=10)
|
||||
progress_frame.pack(fill=tk.BOTH, expand=True)
|
||||
|
||||
# Progress bar
|
||||
self.progress_var = tk.DoubleVar()
|
||||
self.progress_bar = ttk.Progressbar(progress_frame, variable=self.progress_var,
|
||||
maximum=100, length=400)
|
||||
self.progress_bar.pack(fill=tk.X, pady=(0, 10))
|
||||
|
||||
# Progress log
|
||||
self.progress_text = scrolledtext.ScrolledText(progress_frame, height=10, wrap=tk.WORD,
|
||||
bg="#1a1a1a", fg="#00ff88",
|
||||
font=("Consolas", 9))
|
||||
self.progress_text.pack(fill=tk.BOTH, expand=True)
|
||||
|
||||
# Control buttons
|
||||
control_frame = ttk.Frame(progress_frame)
|
||||
control_frame.pack(fill=tk.X, pady=(5, 0))
|
||||
|
||||
self.cancel_btn = ttk.Button(control_frame, text="Cancel", command=self._cancel_conversion,
|
||||
state=tk.DISABLED)
|
||||
self.cancel_btn.pack(side=tk.LEFT)
|
||||
|
||||
ttk.Button(control_frame, text="Clear Log", command=self._clear_log).pack(side=tk.LEFT, padx=5)
|
||||
|
||||
def _populate_comparison(self):
|
||||
"""Populate the quantization comparison table."""
|
||||
comparisons = [
|
||||
("Q4_0", "~3.5GB", "★★☆☆☆", "★★★★★"),
|
||||
("Q4_K_M", "~3.8GB", "★★★☆☆", "★★★★★"),
|
||||
("Q5_K_M", "~4.5GB", "★★★★☆", "★★★★☆"),
|
||||
("Q6_K", "~5.5GB", "★★★★☆", "★★★☆☆"),
|
||||
("Q8_0", "~7GB", "★★★★★", "★★☆☆☆"),
|
||||
("FP16", "~14GB", "★★★★★", "★☆☆☆☆"),
|
||||
]
|
||||
|
||||
for comp in comparisons:
|
||||
self.compare_tree.insert("", tk.END, values=comp)
|
||||
|
||||
def _browse_input(self):
|
||||
"""Browse for input model."""
|
||||
filename = filedialog.askopenfilename(
|
||||
title="Select Model File",
|
||||
filetypes=[
|
||||
("All Models", "*.gguf;*.safetensors;*.bin;*.pt;*.pth;*.onnx"),
|
||||
("GGUF", "*.gguf"),
|
||||
("SafeTensors", "*.safetensors"),
|
||||
("PyTorch", "*.bin;*.pt;*.pth"),
|
||||
("ONNX", "*.onnx"),
|
||||
("All Files", "*.*")
|
||||
]
|
||||
)
|
||||
|
||||
if filename:
|
||||
self.input_path_var.set(filename)
|
||||
self._analyze_model()
|
||||
|
||||
# Auto-generate output path
|
||||
input_path = Path(filename)
|
||||
output_format = self.output_format_var.get()
|
||||
quantization = self.quantization_var.get()
|
||||
|
||||
output_name = f"{input_path.stem}_{quantization}.{output_format}"
|
||||
output_path = input_path.parent / output_name
|
||||
self.output_path_var.set(str(output_path))
|
||||
|
||||
def _browse_output(self):
|
||||
"""Browse for output location."""
|
||||
filename = filedialog.asksaveasfilename(
|
||||
title="Save Converted Model As",
|
||||
defaultextension=f".{self.output_format_var.get()}",
|
||||
filetypes=[
|
||||
(f"{self.output_format_var.get().upper()}", f"*.{self.output_format_var.get()}"),
|
||||
("All Files", "*.*")
|
||||
]
|
||||
)
|
||||
|
||||
if filename:
|
||||
self.output_path_var.set(filename)
|
||||
|
||||
def _analyze_model(self):
|
||||
"""Analyze the selected model."""
|
||||
model_path = self.input_path_var.get()
|
||||
if not model_path or not os.path.exists(model_path):
|
||||
messagebox.showerror("Error", "Please select a valid model file")
|
||||
return
|
||||
|
||||
self.info_text.delete(1.0, tk.END)
|
||||
self.info_text.insert(tk.END, "Analyzing model...\n\n")
|
||||
|
||||
# Get model info
|
||||
info = self.converter.get_model_info(model_path)
|
||||
if info:
|
||||
self.current_model = info
|
||||
|
||||
# Display info
|
||||
self.info_text.insert(tk.END, f"Name: {info['name']}\n")
|
||||
self.info_text.insert(tk.END, f"Format: {info['format'].upper()}\n")
|
||||
self.info_text.insert(tk.END, f"Size: {info['size_mb']:.1f} MB\n")
|
||||
self.info_text.insert(tk.END, f"Modified: {info['modified'].strftime('%Y-%m-%d %H:%M')}\n")
|
||||
|
||||
if 'quantization' in info:
|
||||
self.info_text.insert(tk.END, f"Quantization: {info['quantization']}\n")
|
||||
if 'parameters' in info:
|
||||
self.info_text.insert(tk.END, f"Parameters: {info['parameters']}\n")
|
||||
if 'model_type' in info:
|
||||
self.info_text.insert(tk.END, f"Model Type: {info['model_type']}\n")
|
||||
if 'architectures' in info:
|
||||
self.info_text.insert(tk.END, f"Architecture: {', '.join(info['architectures'])}\n")
|
||||
if 'vocab_size' in info:
|
||||
self.info_text.insert(tk.END, f"Vocab Size: {info['vocab_size']:,}\n")
|
||||
|
||||
def _on_format_change(self, event=None):
|
||||
"""Handle output format change."""
|
||||
format_type = self.output_format_var.get()
|
||||
|
||||
# Enable/disable quantization based on format
|
||||
if format_type == 'gguf':
|
||||
self.quant_combo.config(state="readonly")
|
||||
else:
|
||||
self.quant_combo.config(state="disabled")
|
||||
|
||||
# Update output path
|
||||
if self.input_path_var.get():
|
||||
input_path = Path(self.input_path_var.get())
|
||||
quantization = self.quantization_var.get() if format_type == 'gguf' else ''
|
||||
|
||||
if quantization:
|
||||
output_name = f"{input_path.stem}_{quantization}.{format_type}"
|
||||
else:
|
||||
output_name = f"{input_path.stem}.{format_type}"
|
||||
|
||||
output_path = input_path.parent / output_name
|
||||
self.output_path_var.set(str(output_path))
|
||||
|
||||
def _start_conversion(self):
|
||||
"""Start the conversion process."""
|
||||
input_path = self.input_path_var.get()
|
||||
output_path = self.output_path_var.get()
|
||||
|
||||
if not input_path or not os.path.exists(input_path):
|
||||
messagebox.showerror("Error", "Please select a valid input model")
|
||||
return
|
||||
|
||||
if not output_path:
|
||||
messagebox.showerror("Error", "Please specify an output path")
|
||||
return
|
||||
|
||||
# Confirm overwrite if exists
|
||||
if os.path.exists(output_path):
|
||||
if not messagebox.askyesno("Confirm", f"Output file exists. Overwrite?\n{output_path}"):
|
||||
return
|
||||
|
||||
# Disable UI
|
||||
self.convert_btn.config(state=tk.DISABLED)
|
||||
self.cancel_btn.config(state=tk.NORMAL)
|
||||
|
||||
# Clear progress
|
||||
self.progress_var.set(0)
|
||||
self.progress_text.delete(1.0, tk.END)
|
||||
|
||||
# Start conversion in thread
|
||||
threading.Thread(target=self._conversion_thread,
|
||||
args=(input_path, output_path),
|
||||
daemon=True).start()
|
||||
|
||||
def _conversion_thread(self, input_path: str, output_path: str):
|
||||
"""Run conversion in background thread."""
|
||||
def progress_callback(message: str, percent: int, level: str = "info"):
|
||||
# Update UI in main thread
|
||||
self.parent.after(0, self._update_progress, message, percent, level)
|
||||
|
||||
try:
|
||||
output_format = self.output_format_var.get()
|
||||
|
||||
if output_format == 'gguf':
|
||||
quantization = self.quantization_var.get()
|
||||
success = self.converter.convert_to_gguf(
|
||||
input_path, output_path, quantization, progress_callback
|
||||
)
|
||||
else:
|
||||
# Other format conversions
|
||||
success = False
|
||||
progress_callback(f"Conversion to {output_format} not yet implemented", 100, "error")
|
||||
|
||||
if success:
|
||||
self.parent.after(0, self._conversion_complete, True)
|
||||
else:
|
||||
self.parent.after(0, self._conversion_complete, False)
|
||||
|
||||
except Exception as e:
|
||||
progress_callback(f"Error: {e}", 100, "error")
|
||||
self.parent.after(0, self._conversion_complete, False)
|
||||
|
||||
def _update_progress(self, message: str, percent: int, level: str):
|
||||
"""Update progress display."""
|
||||
# Update progress bar
|
||||
if percent >= 0:
|
||||
self.progress_var.set(percent)
|
||||
|
||||
# Add to log
|
||||
timestamp = datetime.now().strftime("%H:%M:%S")
|
||||
|
||||
# Color based on level
|
||||
if level == "error":
|
||||
self.progress_text.insert(tk.END, f"[{timestamp}] ERROR: {message}\n", "error")
|
||||
elif level == "success":
|
||||
self.progress_text.insert(tk.END, f"[{timestamp}] SUCCESS: {message}\n", "success")
|
||||
else:
|
||||
self.progress_text.insert(tk.END, f"[{timestamp}] {message}\n")
|
||||
|
||||
self.progress_text.see(tk.END)
|
||||
|
||||
def _conversion_complete(self, success: bool):
|
||||
"""Handle conversion completion."""
|
||||
self.convert_btn.config(state=tk.NORMAL)
|
||||
self.cancel_btn.config(state=tk.DISABLED)
|
||||
|
||||
if success:
|
||||
messagebox.showinfo("Success", "Model conversion completed successfully!")
|
||||
else:
|
||||
messagebox.showerror("Error", "Model conversion failed. Check the log for details.")
|
||||
|
||||
def _cancel_conversion(self):
|
||||
"""Cancel the current conversion."""
|
||||
if self.converter.current_process:
|
||||
self.converter.current_process.terminate()
|
||||
self._update_progress("Conversion cancelled", 0, "error")
|
||||
self.convert_btn.config(state=tk.NORMAL)
|
||||
self.cancel_btn.config(state=tk.DISABLED)
|
||||
|
||||
def _clear_log(self):
|
||||
"""Clear the progress log."""
|
||||
self.progress_text.delete(1.0, tk.END)
|
||||
self.progress_var.set(0)
|
||||
|
||||
def _open_lora_merger(self):
|
||||
"""Open LoRA merger dialog."""
|
||||
messagebox.showinfo("LoRA Merger", "LoRA merging tool coming soon!")
|
||||
|
||||
def _open_model_splitter(self):
|
||||
"""Open model splitter dialog."""
|
||||
messagebox.showinfo("Model Splitter", "Model splitting tool coming soon!")
|
||||
|
||||
def _open_optimizer(self):
|
||||
"""Open model optimizer dialog."""
|
||||
messagebox.showinfo("Model Optimizer", "Model optimization tool coming soon!")
|
||||
|
||||
def _open_batch_converter(self):
|
||||
"""Open batch converter dialog."""
|
||||
messagebox.showinfo("Batch Converter", "Batch conversion tool coming soon!")
|
||||
|
||||
def _open_model_compare(self):
|
||||
"""Open model comparison dialog."""
|
||||
messagebox.showinfo("Model Compare", "Model comparison tool coming soon!")
|
||||
|
||||
def _open_metadata_editor(self):
|
||||
"""Open metadata editor dialog."""
|
||||
messagebox.showinfo("Metadata Editor", "Metadata editing tool coming soon!")
|
||||
Reference in New Issue
Block a user