Update app.py

app.py

@@ -115,12 +115,28 @@ def gr_predict(text):
 
   return out_str
 
+descr = """
+
+This app uses [Covid-twitter-BERT-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2)
+fine tuned on a custom subset of [Caves dataset](https://arxiv.org/abs/2204.13746) sent by [FIRE 2023](http://fire.irsi.res.in/fire/2023/home)
+conference to do multi-label classification of tweets expressing concerns towards vaccines. The different concerns/classes are
+('side-effect', 'ineffective', 'rushed', 'pharma', 'mandatory', 'unnecessary', 'political', 'ingredients', 'conspiracy', 'country', 'religious'). 
+Each tweet can be expressing multiple of these concerns. If a tweet is not expressing any concern falling into any of these categories 
+it will be classified as 'None'.\n
+[Source files](https://github.com/Ranjit246/AISoME_FIRE_2023)\n
+Try it out with some ridiculous statements about vaccines. You can use the examples below as a start.
+
+
+"""
 # Gradio Interface
 iface = gr.Interface(
     fn=gr_predict,
     inputs=gr.Textbox(),
-    outputs=gr.Label()  # Use Label widget for output
+    outputs=gr.Label(),  # Use Label widget for output
+    examples=["This vaccine gave me mumps", "Chinese vaccine will infect our brain",
+              "Trump is gonna use these vaccines to control us and become the president"],
+    title="Vaccine Concerns ML",
+    description=descr
 )
-
 # Launch the Gradio app
 iface.launch(debug=True)