Update app.py

app.py

@@ -2,7 +2,9 @@ import jax
 import jax.numpy as jnp
 from transformers import FlaxBigBirdForQuestionAnswering, BigBirdTokenizerFast
 import gradio as gr
+
 FLAX_MODEL_ID = "vasudevgupta/flax-bigbird-natural-questions"
+
 if __name__ == "__main__":  
   model = FlaxBigBirdForQuestionAnswering.from_pretrained(FLAX_MODEL_ID, block_size=64, num_random_blocks=3)
   tokenizer = BigBirdTokenizerFast.from_pretrained(FLAX_MODEL_ID)
@@ -27,6 +29,8 @@ if __name__ == "__main__":
   default_context = "Models like BERT, RoBERTa have a token limit of 512. But BigBird supports up to 4096 tokens! How does it do that? How can transformers be applied to longer sequences? In Abhishek Thakur's next Talks, I will discuss BigBird!! Attend this Friday, 9:30 PM IST Live link: https://www.youtube.com/watch?v=G22vNvHmHQ0.\nBigBird is a transformer based model which can process long sequences (upto 4096) very efficiently. RoBERTa variant of BigBird has shown outstanding results on long document question answering."
   question = gr.inputs.Textbox(lines=2, default="When is talk happening?", label="Question")
   context = gr.inputs.Textbox(lines=10, default=default_context, label="Context")
+
   title = "BigBird-RoBERTa"
   desc = "BigBird is a transformer based model which can process long sequences (upto 4096) very efficiently. RoBERTa variant of BigBird has shown outstanding results on long document question answering."
-  gr.Interface(fn=get_answer, inputs=[question, context], outputs="text", title=title, description=desc).launch()
+
+  gr.Interface(fn=get_answer, inputs=[question, context], outputs="text", title=title, description=desc).launch()