Update dscription

app.py

@@ -5,6 +5,28 @@ from translation_direction_detection import TranslationDirectionDetector
 
 detector = TranslationDirectionDetector(NMTScorer("m2m100_418M"))
 
+buttons_html = """
+<div style="display: flex; justify-content: start; align-items: center; gap: 10px;">
+    <a href="https://github.com/ZurichNLP/translation-direction-detection" target="_blank">
+        <img src="https://img.shields.io/badge/GitHub-View%20Source-blue?logo=github" alt="GitHub Repo"/>
+    </a>
+    <a href="https://arxiv.org/abs/PAPER_ID" target="_blank">
+        <img src="https://img.shields.io/badge/arXiv-Read%20Paper-brightgreen?logo=arxiv" alt="arXiv Paper"/>
+    </a>
+</div>
+""" # TODO: Update the arxiv link
+
+description_text = """
+
+This demo exemplifies a massively multilingual machine translation model's zero-shot capability to detect translation direction given two parallel texts as described in the paper "Machine Translation Models are Zero-Shot Detectors of Translation Direction" (linked above).
+
+Instructions: 
+Simply input the parallel text in the respective languages and select the corresponding language for each text. The model will predict the direction of translation using the M2M100 418M translation model.
+
+"""
+
+description = buttons_html + description_text
+
 def translate_direction(text1, lang1, text2, lang2):
     lang_to_code = {"English": 'en',
         "German": 'de',
@@ -37,6 +59,6 @@ iface = gr.Interface(
     ],
     outputs=gr.Textbox(label="Result"),
     title="Translation Direction Detector",
-    description="Detects the translation direction between two parallel sentences using the M2M100 418M translation model.",)
+    description=description)
 
 iface.launch()