Update app.py

app.py

@@ -1144,7 +1144,7 @@ def handle_large_dataset(df, create_document,isDataFrame):
     #print(df)
     print(f"Total rows: {total_rows}")
     
-    docstatus = f"Download the complete dataset <a href='https://huggingface.co/spaces/Redmind/NewageNXTGPT/blob/main/output/output.xlsx' download> here.</a>.There are total of {total_rows} rows."
+    docstatus = f"Download the complete dataset <a href='https://huggingface.co/spaces/Redmind/NewageNXTGPT/blob/main/assets/output.xlsx' download> here.</a>.There are total of {total_rows} rows."
     #docstatus = f"Download the complete dataset <a href='https://redmindtechnologies.com/RedMindGPT/output.xlsx' download> here.</a>.There are total of {total_rows} rows."
     if total_rows < 4000:
 
@@ -1207,12 +1207,12 @@ def handle_large_dataset(df, create_document,isDataFrame):
         login(token=os.getenv("HF_TOKEN"))
         from huggingface_hub import HfApi
         api = HfApi()
-        api.upload_file(path_or_fileobj=file_path, repo_id=repo_id, repo_type= "space", path_in_repo="output/output.xlsx")
+        api.upload_file(path_or_fileobj=file_path, repo_id=repo_id, repo_type= "space", path_in_repo="assets/output.xlsx")
         
         from huggingface_hub import hf_hub_url
 
         print(hf_hub_url(
-            repo_id="Redmind/NewageNXTGPT", filename="output/output.xlsx"
+            repo_id="Redmind/NewageNXTGPT", filename="assets/output.xlsx"
             ))
 
         #print(f"Download the file here: {download_url}")
@@ -1255,7 +1255,7 @@ def create_pdf(name,id):
         output.write(filled.read())
 
     repo_id = "Redmind/NewageNXTGPT"
-    file_output=f"output/{output_file_name}"
+    file_output=f"assets/{output_file_name}"
     api.upload_file(path_or_fileobj=output_file_name, repo_id=repo_id, repo_type= "space", path_in_repo=file_output)
     return f"{output_file_name} is created successfully."