login fix

app.py

@@ -121,15 +121,6 @@ def process_pdfs(
             ),
         )
 
-    if oauth_token is None:
-        return (
-            None,
-            None,
-            gr.Markdown(
-                "⚠️ Not logged in to Hugging Face. Please log in to upload to a Hugging Face dataset."
-            ),
-        )
-
     try:
         temp_dir = tempfile.mkdtemp()
         images_dir = os.path.join(temp_dir, "images")
@@ -166,61 +157,67 @@ def process_pdfs(
             message += f"\nCreated zip file with {len(images)} images"
 
         if hf_repo:
-            try:
-                hf_api = HfApi(token=oauth_token.token)
-                hf_api.create_repo(
-                    hf_repo,
-                    repo_type="dataset",
-                    private=private_repo,
-                )
-                # Upload only the sampled images directory
-                hf_api.upload_folder(
-                    folder_path=sampled_images_dir,
-                    repo_id=hf_repo,
-                    repo_type="dataset",
-                    path_in_repo="images",
-                )
-
-                # Determine size category
-                size_category = get_size_category(len(images))
-
-                # Create DatasetCardData instance
-                card_data = DatasetCardData(
-                    tags=["created-with-pdfs-to-page-images-converter", "pdf-to-image"],
-                    size_categories=[size_category],
-                )
-
-                # Create and populate the dataset card
-                card = DatasetCard.from_template(
-                    card_data,
-                    template_path=None,  # Use default template
-                    hf_repo=hf_repo,
-                    num_images=len(images),
-                    num_pdfs=len(pdf_files),
-                    sample_size=sample_percentage
-                    if sample_percentage > 0
-                    else "All pages",
-                    creation_date=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
-                )
-
-                # Add our custom content to the card
-                card.text = DATASET_CARD_TEMPLATE.format(
-                    hf_repo=hf_repo,
-                    num_images=len(images),
-                    num_pdfs=len(pdf_files),
-                    sample_size=sample_percentage
-                    if sample_percentage > 0
-                    else "All pages",
-                    creation_date=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
-                    size_category=size_category,
-                )
-
-                repo_url = f"https://huggingface.co/datasets/{hf_repo}"
-                message += f"\nUploaded dataset card to Hugging Face repo: [{hf_repo}]({repo_url})"
-
-                card.push_to_hub(hf_repo, token=oauth_token.token)
-            except Exception as e:
-                message += f"\nFailed to upload to Hugging Face: {str(e)}"
+            if oauth_token is None:
+                message += "\n⚠️ Not logged in to Hugging Face. Please log in to upload to a Hugging Face dataset."
+            else:
+                try:
+                    hf_api = HfApi(token=oauth_token.token)
+                    hf_api.create_repo(
+                        hf_repo,
+                        repo_type="dataset",
+                        private=private_repo,
+                    )
+                    # Upload only the sampled images directory
+                    hf_api.upload_folder(
+                        folder_path=sampled_images_dir,
+                        repo_id=hf_repo,
+                        repo_type="dataset",
+                        path_in_repo="images",
+                    )
+
+                    # Determine size category
+                    size_category = get_size_category(len(images))
+
+                    # Create DatasetCardData instance
+                    card_data = DatasetCardData(
+                        tags=[
+                            "created-with-pdfs-to-page-images-converter",
+                            "pdf-to-image",
+                        ],
+                        size_categories=[size_category],
+                    )
+
+                    # Create and populate the dataset card
+                    card = DatasetCard.from_template(
+                        card_data,
+                        template_path=None,  # Use default template
+                        hf_repo=hf_repo,
+                        num_images=len(images),
+                        num_pdfs=len(pdf_files),
+                        sample_size=sample_percentage
+                        if sample_percentage > 0
+                        else "All pages",
+                        creation_date=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+                    )
+
+                    # Add our custom content to the card
+                    card.text = DATASET_CARD_TEMPLATE.format(
+                        hf_repo=hf_repo,
+                        num_images=len(images),
+                        num_pdfs=len(pdf_files),
+                        sample_size=sample_percentage
+                        if sample_percentage > 0
+                        else "All pages",
+                        creation_date=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+                        size_category=size_category,
+                    )
+
+                    repo_url = f"https://huggingface.co/datasets/{hf_repo}"
+                    message += f"\nUploaded dataset card to Hugging Face repo: [{hf_repo}]({repo_url})"
+
+                    card.push_to_hub(hf_repo, token=oauth_token.token)
+                except Exception as e:
+                    message += f"\nFailed to upload to Hugging Face: {str(e)}"
 
         return images, zip_path, message
     except Exception as e:
@@ -292,7 +289,14 @@ with gr.Blocks() as demo:
     submit_button = gr.Button("Convert PDFs to page images")
     submit_button.click(
         process_pdfs,
-        inputs=[pdf_files, sample_percentage, hf_repo, create_zip, private_repo],
+        inputs=[
+            pdf_files,
+            sample_percentage,
+            hf_repo,
+            create_zip,
+            private_repo,
+            gr.OAuthToken(),
+        ],
         outputs=[output_gallery, download_button, status_text],
     )