Update app.py

app.py

@@ -97,19 +97,8 @@ if (runModel=='1'):
     test_dataset = IntentDataset(test_encodings, list(test_df['label']))
 
  
-    # Your repository name 
-    repo_name = "Reyad-Ahmmed/hf-data-timeframe" 
     
     
-    api_token = os.getenv("HF_API_TOKEN")  # Retrieve the API token from environment variable
- 
-    if not api_token:
-        raise ValueError("API token not found. Please set the HF_API_TOKEN environment variable.")
-
-    # Create repository (if not already created)
-    api = HfApi()
-    create_repo(repo_id=repo_name, token=api_token, exist_ok=True)
-    
     
     # Create an instance of the custom loss function
     training_args = TrainingArguments(
@@ -192,35 +181,12 @@ if (runModel=='1'):
     evaluate_and_report_errors(model,train_dataloader, tokenizer)
 
     # Save the model and tokenizer
-    #model.save_pretrained('./'  + modelNameToUse + '_model')
-    #tokenizer.save_pretrained('./' + modelNameToUse + '_tokenizer')
+    model.save_pretrained('./'  + modelNameToUse + '_model')
+    tokenizer.save_pretrained('./' + modelNameToUse + '_tokenizer')
 
-    # Save the model and tokenizer locally
-    local_model_path = "./data-timeframe_model"
-    local_tokenizer_path = "./data-timeframe_tokenizer"
-    
-    # Ensure the directories exist 
-    os.makedirs(local_model_path, exist_ok=True) 
-    os.makedirs(local_tokenizer_path, exist_ok=True)
     
-    model.save_pretrained(local_model_path)
-    tokenizer.save_pretrained(local_tokenizer_path)
-
-    # Upload the model and tokenizer to the Hugging Face repository
-    upload_folder(
-        folder_path=local_model_path,
-        path_in_repo="data-timeframe_model",
-        repo_id=repo_name,
-        token=api_token,
-        commit_message="Update fine-tuned model"
-    )
-    upload_folder(
-        folder_path=local_tokenizer_path,
-        path_in_repo="data-timeframe_tokenizer",
-        repo_id=repo_name,
-        token=api_token,
-        commit_message="Update fine-tuned tokenizer"
-    )
+
+
 
     
 else: