Fixed image size and bullet lists

app.py

@@ -14,17 +14,23 @@ Welcome to our Huggingface space, where you can explore the capabilities of TURN
 - **Powerful Architecture:** TURNA contains 1.1B parameters, and was pre-trained with an encoder-decoder architecture following the UL2 framework on 43B tokens from various domains.
 - **Diverse Training Data:** Our model is trained on a varied dataset of 43 billion tokens, covering a wide array of domains.
 - **Broad Applications:** TURNA is fine-tuned for a variety of generation and understanding tasks, including:
-    
     - Summarization
-	- Paraphrasing
-	- News title generation
-	- Sentiment classification
-	- Text categorization
-	- Named entity recognition
-	- Part-of-speech tagging
-	- Semantic textual similarity
-	- Natural language inference
+    - Paraphrasing
+    - News title generation
+    - Sentiment classification
+    - Text categorization
+    - Named entity recognition
+    - Part-of-speech tagging
+    - Semantic textual similarity
+    - Natural language inference
+
+**Note:** First inference might take time as the models are downloaded on-the-go.
+
+*TURNA can generate toxic content or provide erroneous information. Double-check before usage.*
 
+"""
+
+CITATION = """
 Refer to our [paper](https://arxiv.org/abs/2401.14373) for more details.
 
 ### Citation
@@ -38,11 +44,6 @@ Refer to our [paper](https://arxiv.org/abs/2401.14373) for more details.
 	primaryClass={cs.CL}
 }
 ```
-
-**Note:** First inference might take time as the models are downloaded on-the-go.
-
-*TURNA can generate toxic content or provide erroneous information. Double-check before usage.*
-
 """
 
 
@@ -138,12 +139,10 @@ def turna(input, max_new_tokens, length_penalty,
 
 with gr.Blocks(theme="abidlabs/Lime") as demo:
     gr.Markdown("# TURNA")
-    gr.Image("images/turna-logo.png", width=100)
+    with gr.Row():
+        gr.Image("images/turna-logo.png", width=100)
 
     gr.Markdown(DESCRIPTION)
-
-    
-
     
     with gr.Tab("Sentiment Analysis"):
         gr.Markdown("TURNA fine-tuned on sentiment analysis. Enter text to analyse sentiment and pick the model (tweets or product reviews).")
@@ -265,4 +264,7 @@ with gr.Blocks(theme="abidlabs/Lime") as demo:
                 
                 sum_submit.click(summarize, inputs=[sum_input, sum_choice], outputs=sum_output)
             sum_examples = gr.Examples(examples = long_text, inputs = [sum_input, sum_choice], outputs=sum_output,  fn=summarize)
+    
+    gr.Markdown(CITATION)
+
 demo.launch()