style

app.py

@@ -51,17 +51,17 @@ def onnx_export(token: str, model_id: str, task: str) -> str:
             commit_url = repo.push_to_hub()
             print("[dataset]", commit_url)
 
-        return f"### Success 🔥 Yay! This model was successfully converted and a PR was open using your token, here: {commit_info.pr_url}]({commit_info.pr_url})"
+        return f"#### Success 🔥 Yay! This model was successfully converted and a PR was open using your token, here: {commit_info.pr_url}]({commit_info.pr_url})"
     except Exception as e:
-        return f"### Error: {e}"
+        return f"#### Error: {e}"
 
 DESCRIPTION = """
 <p align="center">
     <img src="https://huggingface.co/spaces/optimum/exporters/resolve/main/clean_hf_onnx.png"/>
 </p>
 
-<p align="center">
-    # Convert any PyTorch model to ONNX with 🤗 Optimum exporters 🏎️
+<p align="center"; style="font-weight: 900; margin-bottom: 10px; margin-top: 10px>
+Convert any PyTorch model to ONNX with 🤗 Optimum exporters 🏎️
 </p>
 
 This Space allows to automatically convert to ONNX 🤗 transformers models hosted on the Hugging Face Hub. It opens a PR on the target model, and it is up to the owner of the original model
@@ -71,7 +71,7 @@ Once converted, the model can for example be used in the [🤗 Optimum](https://
 Check out [this guide](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/models) to see how!
 
 The steps are the following:
-- Paste a read-access token from hf.co/settings/tokens. Read access is enough given that we will open a PR against the source repo.
+- Paste a read-access token from https://huggingface.co/settings/tokens . Read access is enough given that we will open a PR against the source repo.
 - Input a model id from the Hub (for example:)
 - If necessary, input the task for this model.
 - Click "Convert to ONNX"
@@ -83,7 +83,7 @@ Note: in case the model to convert is larger than 2 GB, it will be saved in a su
 with gr.Blocks() as demo:
     gr.Markdown(DESCRIPTION)
 
-    with gr.Row():
+    with gr.Column():
         input_token = gr.Textbox(max_lines=1, label="Hugging Face token")
         input_model = gr.Textbox(max_lines=1, label="Model name", placeholder="textattack/distilbert-base-cased-CoLA")
         input_task = gr.Textbox(value="auto", max_lines=1, label="Task (can be left blank, will be automatically inferred)")