Update app.py

app.py

@@ -1,12 +1,12 @@
 import os
 import subprocess
 from typing import Union
+from huggingface_hub import whoami
 is_spaces = True if os.environ.get("SPACE_ID") else False
 
 if is_spaces:
     subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
     import spaces
-    from huggingface_hub import whoami
     
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 import sys
@@ -37,6 +37,7 @@ if not is_spaces:
     
 MAX_IMAGES = 150
 
+
 def load_captioning(uploaded_images, concept_sentence):
     gr.Info("Images uploaded!")
     updates = []
@@ -149,6 +150,16 @@ def start_training(
 ):
     if not lora_name:
         raise gr.Error("You forgot to insert your LoRA name! This name has to be unique.")
+    
+    if not is_spaces:
+        try:
+            if whoami()["auth"]["accessToken"]["role"] == "write" or "repo.write" in whoami()["auth"]["accessToken"]["fineGrained"]["scoped"][0]["permissions"]:
+                gr.Info(f"Starting training locally {whoami()['name']}. Your LoRA will be available locally and in Hugging Face after it finishes.")
+            else:
+                raise gr.Error(f"You logged in to Hugging Face with not enough permissions, you need a token that allows writing to {whoami()['name']} profile.")
+        except: 
+            raise gr.Error(f"You logged in to Hugging Face with not enough permissions, you need a token that allows writing to {whoami()['name']} profile.")
+            
     print("Started training")
     slugged_lora_name = slugify(lora_name)
 
@@ -166,7 +177,11 @@ def start_training(
     config["config"]["process"][0]["network"]["linear_alpha"] = int(rank)
     config["config"]["process"][0]["datasets"][0]["folder_path"] = dataset_folder
     config["config"]["process"][0]["save"]["push_to_hub"] = True
-    config["config"]["process"][0]["save"]["hf_repo_id"] = f"{profile.username}/{slugged_lora_name}"
+    try:
+        username = whoami()["name"] if not is_spaces else profile.username
+    except:
+        raise gr.Error("Error trying to retrieve your username. Are you sure you are logged in with Hugging Face?")
+    config["config"]["process"][0]["save"]["hf_repo_id"] = f"{username}/{slugged_lora_name}"
     config["config"]["process"][0]["save"]["hf_private"] = True
     if concept_sentence:
         config["config"]["process"][0]["trigger_word"] = concept_sentence
@@ -189,7 +204,7 @@ def start_training(
     with open(config_path, "w") as f:
         yaml.dump(config, f)
     if is_spaces:
-        print("Started training with spacerunner...")
+        gr.Info("Instantiating Spacerunner...")
         # copy config to dataset_folder as config.yaml
         shutil.copy(config_path, dataset_folder + "/config.yaml")
         # get location of this script
@@ -343,16 +358,14 @@ with gr.Blocks(theme=theme, css=css) as demo:
         progress_area = gr.Markdown("")
 
     with gr.Tab("Train on your device" if is_spaces else "Instructions"):
-        gr.Markdown(
-            f"""To use FLUX LoRA Ease locally with this UI, you can clone this repository (yes, HF Spaces are git repos!). You'll need ~23GB of VRAM
+        gr.Markdown(f"""To use FLUX LoRA Ease locally with this UI, you can clone this repository (yes, HF Spaces are git repos!). You'll need ~23GB of VRAM
         ```bash
-        git clone https://huggingface.co/spaces/autotrain-projects/flux-lora-ease
+        git clone https://huggingface.co/spaces/flux-lora-ease/flux-lora-ease
         cd flux-lora-ease
-        ## Optional, start a venv environment ## 
+        ## Optional, start a venv environment (install torch first) ## 
         python3 -m venv venv
         source venv/bin/activate
         # .\venv\Scripts\activate on windows
-        # install torch first
         ## End of optional ##
         pip install -r requirements_local.txt
         ```