Update script.py

script.py

@@ -4,6 +4,7 @@ from safetensors.torch import load_file
 from diffusers import AutoPipelineForText2Image
 from datasets import load_dataset
 from huggingface_hub.repocard import RepoCard
+from huggingface_hub import HfApi
 import torch
 import re
 import argparse
@@ -23,6 +24,7 @@ def do_train(script_args):
     subprocess.run(['python', 'trainer.py'] + script_args)
 
 def do_inference(dataset_name, output_dir, num_tokens):
+    widget_content = []
     try:
         print("Starting inference to generate example images...")
         dataset = load_dataset(dataset_name)
@@ -33,7 +35,6 @@ def do_inference(dataset_name, output_dir, num_tokens):
         pipe.load_lora_weights(f'{output_dir}/pytorch_lora_weights.safetensors')
         
         prompts = dataset["train"]["prompt"]
-        widget_content = []
         if(num_tokens > 0):
             tokens_sequence = ''.join(f'<s{i}>' for i in range(num_tokens))
             tokens_list = [f'<s{i}>' for i in range(num_tokens)]
@@ -55,7 +56,12 @@ def do_inference(dataset_name, output_dir, num_tokens):
                 }
             }
             widget_content.append(card_dict)
-        
+    except Exception as e:
+        print("Something went wrong with generating images, specifically: ", e)
+    
+    try:
+        api = HfApi()
+        username = api.whoami()["name"]
         repo_id = api.create_repo(f"{username}/{output_dir}", exist_ok=True).repo_id
         
         with open(f'{output_dir}/README.md', 'r') as file:
@@ -63,21 +69,20 @@ def do_inference(dataset_name, output_dir, num_tokens):
         readme_content = readme_content.replace(f'{output_dir}', f'{username}/{output_dir}')
         
         card = RepoCard(readme_content)
-        card.data["widget"] = widget_content
-        card.save(f'{output_dir}/README.md')
-    except Exception as e:
-        print("Something went wrong with generating images, specifically: ", e)
+        if widget_content: 
+            card.data["widget"] = widget_content
+            card.save(f'{output_dir}/README.md')
     
-    from huggingface_hub import HfApi
-    api = HfApi()
-    username = api.whoami()["name"]
-    print("Starting upload...")
-    api.upload_folder(
-        folder_path=output_dir,
-        repo_id=f"{username}/{output_dir}",
-        repo_type="model",
-    )
-    print("Upload finished!")
+        print("Starting upload...")
+        api.upload_folder(
+            folder_path=output_dir,
+            repo_id=f"{username}/{output_dir}",
+            repo_type="model",
+        )
+    except Exception as e:
+        print("Something went wrong with uploading your model, specificaly: ", e)
+    else:
+        print("Upload finished!")
 
 import sys
 import argparse