Update app.py

app.py

@@ -19,6 +19,8 @@ import torch.nn.functional as F
 import matplotlib.pyplot as plt
 import json
 import gradio as gr
+from huggingface_hub import HfApi, upload_folder, create_repo
+import os
 
 # Load configuration file 
 with open('config.json', 'r') as config_file:
@@ -171,8 +173,31 @@ if (runModel=='1'):
     #model.save_pretrained('./'  + modelNameToUse + '_model')
     #tokenizer.save_pretrained('./' + modelNameToUse + '_tokenizer')
 
-    model.save_pretrained("hf-data-timeframe/data-timeframe_model")
-    tokenizer.save_pretrained("hf-data-timeframe/data-timeframe_tokenizer")
+    repo_name = "Reyad-Ahmmed/hf-data-timeframe"  # Replace with your repository name
+    api_token = os.getenv("HF_API_TOKEN")  # Replace with your actual API token
+
+    print("app token: ", api_token)
+    
+    api = HfApi() 
+    create_repo(repo_id=repo_name, token=api_token, exist_ok=True)
+    
+    model.save_pretrained("/data-timeframe_model")
+    tokenizer.save_pretrained("/data-timeframe_tokenizer")
+
+    # Upload the model and tokenizer to the Hugging Face repository
+    upload_folder( 
+        folder_path="/data-timeframe_model", 
+        repo_id=repo_name, 
+        token=api_token, 
+        commit_message="Add fine-tuned model" 
+    ) 
+    
+    upload_folder( 
+        folder_path="/data-timeframe_tokenizer", 
+        repo_id=repo_name, 
+        token=api_token, 
+        commit_message="Add fine-tuned tokenizer" 
+    )
     
 else:
     print('Load Pre-trained')