test

app.py

@@ -1,7 +1,7 @@
 from pathlib import Path
 from typing import List, Dict, Tuple
 import matplotlib.colors as mpl_colors
-
+import os
 import pandas as pd
 import seaborn as sns
 import shinyswatch
@@ -47,9 +47,15 @@ def server(input: Inputs, output: Outputs, session: Session):
             src = input.image_input()[0]['datapath']
             img = {"src": src, "width": "500px"} 
 
+            # Specify the cache directory
+            transformers_cache_dir = "/usr/local/lib/python3.9/site-packages/transformers"
+
+            # Set the TRANSFORMERS_CACHE environment variable
+            os.environ["TRANSFORMERS_CACHE"] = transformers_cache_dir
+
             # Load the model configuration
-            model_config = SamConfig.from_pretrained("facebook/sam-vit-base", use_cache=False)
-            processor = SamProcessor.from_pretrained("facebook/sam-vit-base", use_cache=False)
+            model_config = SamConfig.from_pretrained("facebook/sam-vit-base")
+            processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
 
             # Create an instance of the model architecture with the loaded configuration
             my_sam_model = SamModel(config=model_config)

run.py

@@ -1,19 +1,26 @@
-from transformers import SamModel, SamConfig, SamProcessor
-import torch
-import numpy as np
-import matplotlib.pyplot as plt
-import app
+# from transformers import SamModel, SamConfig, SamProcessor
+# import torch
+# import numpy as np
+# import matplotlib.pyplot as plt
+# import app
 
-# Load the model configuration
-model_config = SamConfig.from_pretrained("facebook/sam-vit-base")
-processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
+# # Load the model configuration
+# model_config = SamConfig.from_pretrained("facebook/sam-vit-base")
+# processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
 
-# Create an instance of the model architecture with the loaded configuration
-my_sam_model = SamModel(config=model_config)
-#Update the model by loading the weights from saved file.
-my_sam_model.load_state_dict(torch.load("sam_model.pth", map_location=torch.device('cpu')))
+# # Create an instance of the model architecture with the loaded configuration
+# my_sam_model = SamModel(config=model_config)
+# #Update the model by loading the weights from saved file.
+# my_sam_model.load_state_dict(torch.load("sam_model.pth", map_location=torch.device('cpu')))
 
 
 # load image
 # test_image = {"src": src, "width": "500px"} 
 
+import os
+
+# Specify the cache directory
+transformers_cache_dir = "/usr/local/lib/python3.9/site-packages/transformers"
+
+# Set the TRANSFORMERS_CACHE environment variable
+os.environ["TRANSFORMERS_CACHE"] = transformers_cache_dir