:lady_beetle: Trying to repair git related issues

.gitignore

@@ -1,2 +1,3 @@
 lightning_logs/
-*.pth
+*.pth
+my-awesome-model/

app.py

@@ -13,6 +13,7 @@ from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAtte
 import torch
 from torch import nn
 from datasets import load_dataset
+from huggingface_hub import PyTorchModelHubMixin
 
 timber = logging.getLogger()
 # logging.basicConfig(level=logging.DEBUG)
@@ -296,7 +297,9 @@ def create_conv_sequence(in_channel_num_of_nucleotides, num_filters, kernel_size
   return nn.Sequential(conv1d, activation, pooling)
 
 
-class Cnn1dClassifier(nn.Module):
+class Cnn1dClassifier(nn.Module,
+                      PyTorchModelHubMixin
+                      ):
   def __init__(self,
                seq_len,
                in_channel_num_of_nucleotides=4,
@@ -382,6 +385,7 @@ def start(classifier_model, model_save_path, is_attention_model=False, m_optimiz
   data_module = MqtlDataModule(train_ds=train_dataset, val_ds=val_dataset, test_ds=test_dataset)
 
   classifier_model = classifier_model  #.to(DEVICE)
+  classifier_model = classifier_model.from_pretrained("my-awesome-model")
 
   classifier_module = MQtlClassifierLightningModule(classifier=classifier_model, regularization=2,
                                                     m_optimizer=m_optimizer)
@@ -398,7 +402,21 @@ def start(classifier_model, model_save_path, is_attention_model=False, m_optimiz
   timber.info("\n\n")
   torch.save(classifier_module.state_dict(), model_save_path)
 
-  trainer.push_to_hub("fahimfarhan/mqtl-classifier-model")
+  #  save locally
+  classifier_model.save_pretrained("my-awesome-model")
+
+  # push to the hub
+  classifier_model.push_to_hub(repo_id="fahimfarhan/mqtl-classifier-model", commit_message=":tada: Push model using huggingface_hub")
+
+  # reload
+  model = classifier_model.from_pretrained("my-awesome-model")
+  # repo_url = "https://huggingface.co/fahimfarhan/mqtl-classifier-model"
+  #
+  # push_to_hub(
+  #   model_file=classifier_model.file_name,  # Replace with your model file path
+  #   repo_url=repo_url,
+  #   # config_file="config.json"  # Optional, if you have a config file
+  # )
 
   # start_interpreting_ig_and_dl(classifier_model, WINDOW, dataset_folder_prefix=dataset_folder_prefix)
   # start_interpreting_with_dlshap(classifier_model, WINDOW, dataset_folder_prefix=dataset_folder_prefix)
@@ -416,3 +434,10 @@ if __name__ == '__main__':
         dataset_folder_prefix="inputdata/", is_debug=True, max_epochs=3)
 
   pass
+
+
+"""
+lightning_logs/
+*.pth
+my-awesome-model
+"""