Error in model loading when called predict method

I am trying to integrate SAM.The model is getting loaded but when I try to annotate. I am getting this error here. I will really appreciate any help on this

(mask_decoder): MaskDecoder(
(transformer): TwoWayTransformer(
(layers): ModuleList(
(0): TwoWayAttentionBlock(
(self_attn): Attention(
(q_proj): Linear(in_features=256, out_features=256, bias=True)
(k_proj): Linear(in_features=256, out_features=256, bias=True)
(v_proj): Linear(in_features=256, out_features=256, bias=True)
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(cross_attn_token_to_image): Attention(
(q_proj): Linear(in_features=256, out_features=128, bias=True)
(k_proj): Linear(in_features=256, out_features=128, bias=True)
(v_proj): Linear(in_features=256, out_features=128, bias=True)
(out_proj): Linear(in_features=128, out_features=256, bias=True)
)
(norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(mlp): MLPBlock(
(lin1): Linear(in_features=256, out_features=2048, bias=True)
(lin2): Linear(in_features=2048, out_features=256, bias=True)
(act): ReLU()
)
(norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(norm4): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(cross_attn_image_to_token): Attention(
(q_proj): Linear(in_features=256, out_features=128, bias=True)
(k_proj): Linear(in_features=256, out_features=128, bias=True)
(v_proj): Linear(in_features=256, out_features=128, bias=True)
(out_proj): Linear(in_features=128, out_features=256, bias=True)
)
)
(1): TwoWayAttentionBlock(
(self_attn): Attention(
(q_proj): Linear(in_features=256, out_features=256, bias=True)
(k_proj): Linear(in_features=256, out_features=256, bias=True)
(v_proj): Linear(in_features=256, out_features=256, bias=True)
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(cross_attn_token_to_image): Attention(
(q_proj): Linear(in_features=256, out_features=128, bias=True)
(k_proj): Linear(in_features=256, out_features=128, bias=True)
(v_proj): Linear(in_features=256, out_features=128, bias=True)
(out_proj): Linear(in_features=128, out_features=256, bias=True)
)
(norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(mlp): MLPBlock(
(lin1): Linear(in_features=256, out_features=2048, bias=True)
(lin2): Linear(in_features=2048, out_features=256, bias=True)
(act): ReLU()
)
(norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(norm4): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(cross_attn_image_to_token): Attention(
(q_proj): Linear(in_features=256, out_features=128, bias=True)
(k_proj): Linear(in_features=256, out_features=128, bias=True)
(v_proj): Linear(in_features=256, out_features=128, bias=True)
(out_proj): Linear(in_features=128, out_features=256, bias=True)
)
)
)
(final_attn_token_to_image): Attention(
(q_proj): Linear(in_features=256, out_features=128, bias=True)
(k_proj): Linear(in_features=256, out_features=128, bias=True)
(v_proj): Linear(in_features=256, out_features=128, bias=True)
(out_proj): Linear(in_features=128, out_features=256, bias=True)
)
(norm_final_attn): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
(iou_token): Embedding(1, 256)
(mask_tokens): Embedding(4, 256)
(output_upscaling): Sequential(
(0): ConvTranspose2d(256, 64, kernel_size=(2, 2), stride=(2, 2))
(1): LayerNorm2d()
(2): GELU(approximate='none')
(3): ConvTranspose2d(64, 32, kernel_size=(2, 2), stride=(2, 2))
(4): GELU(approximate='none')
)
(output_hypernetworks_mlps): ModuleList(
(0): MLP(
(layers): ModuleList(
(0): Linear(in_features=256, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=256, bias=True)
(2): Linear(in_features=256, out_features=32, bias=True)
)
)
(1): MLP(
(layers): ModuleList(
(0): Linear(in_features=256, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=256, bias=True)
(2): Linear(in_features=256, out_features=32, bias=True)
)
)
(2): MLP(
(layers): ModuleList(
(0): Linear(in_features=256, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=256, bias=True)
(2): Linear(in_features=256, out_features=32, bias=True)
)
)
(3): MLP(
(layers): ModuleList(
(0): Linear(in_features=256, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=256, bias=True)
(2): Linear(in_features=256, out_features=32, bias=True)
)
)
)
(iou_prediction_head): MLP(
(layers): ModuleList(
(0): Linear(in_features=256, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=256, bias=True)
(2): Linear(in_features=256, out_features=4, bias=True)
)
)
)
)
[2023-08-06 16:43:21,132] [ERROR] [label_studio_ml.exceptions::exception_f::53] Traceback (most recent call last):
File "/usr/local/lib/python3.8/site-packages/label_studio_ml/exceptions.py", line 39, in exception_f
return f(*args, **kwargs)
File "/usr/local/lib/python3.8/site-packages/label_studio_ml/api.py", line 51, in _predict
predictions, model = _manager.predict(tasks, project, label_config, force_reload, try_fetch, **params)
File "/usr/local/lib/python3.8/site-packages/label_studio_ml/model.py", line 615, in predict
raise ValueError(f'Model is not loaded for {cls.__class__.__name__}: run setup() before using predict()')
ValueError: Model is not loaded for type: run setup() before using predict()
Traceback (most recent call last):
File "/usr/local/lib/python3.8/site-packages/label_studio_ml/exceptions.py", line 39, in exception_f
return f(*args, **kwargs)
File "/usr/local/lib/python3.8/site-packages/label_studio_ml/api.py", line 51, in _predict
predictions, model = _manager.predict(tasks, project, label_config, force_reload, try_fetch, **params)
File "/usr/local/lib/python3.8/site-packages/label_studio_ml/model.py", line 615, in predict
raise ValueError(f'Model is not loaded for {cls.__class__.__name__}: run setup() before using predict()')
ValueError: Model is not loaded for type: run setup() before using predict()