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app.py

@@ -1,12 +1,5 @@
 import gradio as gr
 import torch
-
-# Check if ZeroGPU is available
-try:
-    import spaces
-    ZEROGPU = True
-except:
-    ZEROGPU = False
 import json
 import time
 import os
@@ -158,15 +151,6 @@ def load_custom_dataset(file_path):
 
     return data
 
-# Conditional GPU decorator
-def gpu_decorator(duration=300):
-    def decorator(func):
-        if ZEROGPU:
-            return spaces.GPU(duration=duration)(func)
-        return func
-    return decorator
-
-@gpu_decorator(duration=300)
 def train_model(model_name, datasets, max_samples, lora_r, lora_alpha, epochs, output_name, custom_file, continue_training, continue_from, progress=gr.Progress()):
     """Train model with LoRA"""
 
@@ -403,7 +387,6 @@ def train_model(model_name, datasets, max_samples, lora_r, lora_alpha, epochs, o
         import traceback
         yield log(f"ERROR: {e}\n{traceback.format_exc()}")
 
-@gpu_decorator(duration=60)
 def test_model(prompt, output_name):
     """Test a trained model"""
     try: