Update app.py

app.py

@@ -296,8 +296,8 @@ if (should_train_model=='1'): #train model
     model.config.label_mapping = label_mapping
 
     # Save the model and tokenizer
-    model.save_pretrained(f"./{model_save_path}_model")
-    tokenizer.save_pretrained(f"./{model_save_path}_tokenizer")
+    model.save_pretrained(f"./{model_save_path}")
+    tokenizer.save_pretrained(f"./{model_save_path}")
     
     #for push repository
     repo_name = "Reyad-Ahmmed/hf-data-timeframe" 
@@ -315,35 +315,19 @@ if (should_train_model=='1'): #train model
     # Upload the model and tokenizer to the Hugging Face repository
     
     upload_folder(
-        folder_path=f"{model_save_path}_model",
-        path_in_repo=f"{model_save_path}_model",
+        folder_path=f"{model_save_path}",
+        path_in_repo=f"{model_save_path}",
         repo_id=repo_name,
         token=api_token,
         commit_message="Push model",
         #overwrite=True  # Force overwrite existing files
     )
-
-    upload_folder(
-        folder_path=f"{model_save_path}_tokenizer",
-        path_in_repo=f"{model_save_path}_tokenizer",
-        repo_id=repo_name,
-        token=api_token,
-        commit_message="Push tokenizer",
-        #overwrite=True  # Force overwrite existing files
-    )
-
-    url = "http://210.1.253.35:200/api/hello"  # Example API
-    response = requests.get(url, timeout=120)
-    
-    if response.status_code == 200:
-        jsonify({"message": "Hello, World!"})
-    else:
-        print(f"Error: {response.status_code}")
         
 else:
     print('Load Pre-trained')
-    model_save_path = f"./{model_save_path}_model"
-    tokenizer_save_path = f"./{model_save_path}_tokenizer"
+    model_save_path = f"./{model_save_path}"
+    tokenizer_save_path = f"./{model_save_path}"
+    
     # RobertaTokenizer.from_pretrained(model_save_path)
     model = AutoModelForSequenceClassification.from_pretrained(model_save_path).to('cpu')
     tokenizer = AutoTokenizer.from_pretrained(tokenizer_save_path)