formatting

app.py

@@ -130,13 +130,15 @@ def predict_subset(model_id, token):
 
 
 with gr.Blocks() as demo:
-    gr.Markdown("""# ARCH Image Dataset Explorer 
+    gr.Markdown(
+        """# ARCH Image Dataset Explorer 
     This [Gradio](https://gradio.app/) [Space](https://huggingface.co/spaces/launch) allows you to explore an image dataset exported from ARCH: Archive Research Compute Hub from the [Internet Archive](https://archive.org/).
     Each tab allows you to explore the dataset in a slightly different way by making use of Machine Learning models and tools from the Hugging Face ecosystem.
-    """)
+    """
+    )
     with gr.Tab("Random Image Gallery"):
         gr.Markdown(
-        """## Random image gallery
+            """## Random image gallery
         This tab allows you to explore images in your ARCH collection. You can refresh the images by clicking the refresh button.
         **Please note** not all images will be displayed as some images may not available via the original URLS anymore."""
         )
@@ -145,7 +147,7 @@ with gr.Blocks() as demo:
         button.click(return_random_sample, [], [gallery])
     with gr.Tab("Image Search"):
         gr.Markdown(
-        """## Image search 
+            """## Image search 
         You can search for images by entering a search term and clicking the search button.
         You can also change the number of images to be returned.
         This model uses the [clip-ViT-B-16](https://huggingface.co/sentence-transformers/clip-ViT-B-16) model to embed your images and search term"""
@@ -162,7 +164,7 @@ with gr.Blocks() as demo:
 
     with gr.Tab("Image Classification Model Tester"):
         gr.Markdown(
-        """## Image classification model tester
+            """## Image classification model tester
         You can use this to test out [image classification models](https://huggingface.co/models?pipeline_tag=image-classification) on the Hugging Face Hub:
         - To use this tab you will need to have a Hugging Face account and a valid token. 
         - You can get a token from your [Hugging Face account page](https://huggingface.co/settings/token). 
@@ -173,21 +175,25 @@ with gr.Blocks() as demo:
         )
         token = gr.Textbox(label="token", type="password")
 
-        model_id = gr.Textbox(label="model_id", value="davanstrien/autotrain-wikiart-sample2-42615108993")
+        model_id = gr.Textbox(
+            label="model_id", value="davanstrien/autotrain-wikiart-sample2-42615108993"
+        )
         button = gr.Button("predict")
         gr.Markdown("## Results")
         plot = gr.BarPlot(x="labels", y="freqs", width=600, height=400, vertical=False)
         gallery = gr.Gallery()
         button.click(predict_subset, [model_id, token], [gallery, plot])
     with gr.Tab("Export to Label Studio format"):
-        gr.Markdown("""
+        gr.Markdown(
+            """
         ## Export to Label Studio format
         <img align=left src="https://warehouse-camo.ingress.cmh1.psfhosted.org/ba8de1e22c982bbfc28201dcc953ca15e92a399c/68747470733a2f2f7261772e67697468756275736572636f6e74656e742e636f6d2f686561727465786c6162732f6c6162656c2d73747564696f2f6d61737465722f696d616765732f6c735f6769746875625f6865616465722e706e67">
         This will export the current dataset to a csv file which can be imported into [Label Studio](https://labelstud.io/). You can then import this into Label Studio to label your images by hand.
-        You can run Label Studio using Hugging Face Spaces using this [Spaces template](https://huggingface.co/new-space?template=LabelStudio/LabelStudio)""")
+        You can run Label Studio using Hugging Face Spaces using this [Spaces template](https://huggingface.co/new-space?template=LabelStudio/LabelStudio)"""
+        )
 
         dataset2 = copy.deepcopy(dataset)
-        dataset2 = dataset2.remove_columns('image')
+        dataset2 = dataset2.remove_columns("image")
         dataset2 = dataset2.rename_column("url", "image")
         csv = dataset2.to_csv("label_studio.csv")
         csv_file = gr.File("label_studio.csv")