app.py

import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base")


def get_examples ():
    return [
    ["Peter goes to the store to buy a soda. The soda costs $.25 an ounce. \
He brought $2 with him and leaves with $.50. How many ounces of soda did he buy?",
    "How much did Peter spend on soda? ** He spend $1.5 on soda because 2 - .5 = <<2-.5=1.5>>1.5 \
How many ounces of soda did Peter buy? ** He bought 6 ounces of soda because 1.5 / .25 = <<6=6>>6 #### 6"
    ],
    ["Krystian works in the library. He borrows an average of 40 books every day. \
Every Friday, his number of borrowed books is about 40% higher than the daily average. How many books does he borrow in a week if the library is open from Monday to Friday?"
    ,"How many books does Krystian borrow on Friday? ** The number of books borrowed \
on Friday is higher by 40 * 40/100 = <<40*40/100=16>>16 books. How many books does Krystian borrow in a week? ** There are 5 days from Monday to Friday inclusive, so Krystian borrows an average of 5 * 40 = <<5*40=200>>200 books during that time. How many books does Krystian borrow in a week? ** With Friday's increase in borrowings, during one week Krystian borrows 200 + 16 = <<200+16=216>>216 books."]
    ]
    
    
def text2text(input_text):
    input_ids = tokenizer(input_text, return_tensors="pt").input_ids

    outputs = model.generate(input_ids, max_length=40)
    return tokenizer.decode(outputs[0])

textin = gr.Textbox()
examples = gr.Examples(examples=get_examples(), inputs=[textin])
iface = gr.Interface(fn=text2text, inputs=[textin, examples], outputs="text")
iface.launch()