Update app.py

app.py

@@ -3,11 +3,37 @@ import gradio as gr
 import numpy as np
 from transformers import pipeline
 
-pipe = pipeline("text2text-generation", model="google/flan-t5-large")
+pipe_flan = pipeline("text2text-generation", model="google/flan-t5-large", device="cuda")
+pipe_vanilla = pipeline("text2text-generation", model="t5-large", device="cuda")
+
+examples = [
+  ["Translation"],
+  ["Please answer to the following question. Who is going to be the next Ballon d'or?"],
+  ["Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering."],
+  ["Please answer the following question. What is the boiling point of Nitrogen?"],
+  ["Answer the following yes/no question. Can you write a whole Haiku in a single tweet?"],
+  ["Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?"],
+  ["Q: ( False or not False or False ) is? A: Let's think step by step"],
+  ["The square root of x is the cube root of y. What is y to the power of 2, if x = 4?"],
+  ["Premise:  At my age you will probably have learnt one lesson. Hypothesis:  It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?"]
+]
+
+title = "Flan T5 and Vanilla T5"
+description = "Demo that compares [T5-large](https://huggingface.co/t5-large) and [Flan-T5-large](https://huggingface.co/ybelkada/flan-t5-large)"
 
 def inference(text):
-  output = pipe(text)
-  return output[0]["generated_text"]
+  output_flan = pipe_flan(text)[0]["generated_text"]
+  output_vanilla = pipe_vanilla(text)[0]["generated_text"]
+  return [output_flan, output_vanilla_
 
-io = gr.Interface(inference, gr.Textbox(lines=3), gr.Textbox(lines=3))
+io = gr.Interface(
+  inference,
+  gr.Textbox(lines=3),
+  outputs=[
+    gr.Textbox(lines=3, label="Flan T5"),
+    gr.Textbox(lines=3, label="T5")
+  ],
+  title=title,
+  description=description
+)
 io.launch()