Added cuda_is_available (#4725)

### What problem does this PR solve?

Added cuda_is_available

### Type of change

- [x] Refactoring

deepdoc/vision/ocr.py

 import math
 import numpy as np
 import cv2
-import torch
 import onnxruntime as ort
 
 from .postprocess import build_post_process
         raise ValueError("not find model file path {}".format(
             model_file_path))
 
+    def cuda_is_available():
+        try:
+            import torch
+            if torch.cuda.is_available():
+                return True
+        except Exception:
+            return False
+        return False
+
     options = ort.SessionOptions()
     options.enable_cpu_mem_arena = False
     options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
     # https://github.com/microsoft/onnxruntime/issues/9509#issuecomment-951546580
     # Shrink GPU memory after execution
     run_options = ort.RunOptions()
-    if torch.cuda.is_available():
+    if cuda_is_available():
         cuda_provider_options = {
             "device_id": 0, # Use specific GPU
             "gpu_mem_limit": 512 * 1024 * 1024, # Limit gpu memory

deepdoc/vision/recognizer.py

 import cv2
 from copy import deepcopy
 
-import torch
 import onnxruntime as ort
 from huggingface_hub import snapshot_download
 
         if not os.path.exists(model_file_path):
             raise ValueError("not find model file path {}".format(
                 model_file_path))
+
+        def cuda_is_available():
+            try:
+                import torch
+                if torch.cuda.is_available():
+                    return True
+            except Exception:
+                return False
+            return False
+
         # https://github.com/microsoft/onnxruntime/issues/9509#issuecomment-951546580
         # Shrink GPU memory after execution
         self.run_options = ort.RunOptions()
 
-        if torch.cuda.is_available():
+        if cuda_is_available():
             options = ort.SessionOptions()
             options.enable_cpu_mem_arena = False
             cuda_provider_options = {

pyproject.toml

     "tencentcloud-sdk-python==3.0.1215",
     "tika==2.6.0",
     "tiktoken==0.7.0",
-    "torch>=2.5.0,<3.0.0",
     "umap_learn==0.5.6",
     "vertexai==1.64.0",
     "volcengine==1.0.146",
     "fastembed>=0.3.6,<0.4.0; sys_platform == 'darwin' or platform_machine != 'x86_64'",
     "fastembed-gpu>=0.3.6,<0.4.0; sys_platform != 'darwin' and platform_machine == 'x86_64'",
     "flagembedding==1.2.10",
+    "torch>=2.5.0,<3.0.0",
     "transformers>=4.35.0,<5.0.0"
 ]

uv.lock

     { name = "tencentcloud-sdk-python" },
     { name = "tika" },
     { name = "tiktoken" },
-    { name = "torch" },
     { name = "umap-learn" },
     { name = "valkey" },
     { name = "vertexai" },
     { name = "fastembed", marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'" },
     { name = "fastembed-gpu", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
     { name = "flagembedding" },
+    { name = "torch" },
     { name = "transformers" },
 ]
 
     { name = "tencentcloud-sdk-python", specifier = "==3.0.1215" },
     { name = "tika", specifier = "==2.6.0" },
     { name = "tiktoken", specifier = "==0.7.0" },
-    { name = "torch", specifier = ">=2.5.0,<3.0.0" },
+    { name = "torch", marker = "extra == 'full'", specifier = ">=2.5.0,<3.0.0" },
     { name = "transformers", marker = "extra == 'full'", specifier = ">=4.35.0,<5.0.0" },
     { name = "umap-learn", specifier = "==0.5.6" },
     { name = "valkey", specifier = "==6.0.2" },