app.py

import gradio as gr
import numpy as np
import random
import re
import torch
import transformers

from keybert import KeyBERT
from transformers import (T5ForConditionalGeneration, T5Tokenizer)

DEVICE = torch.device('cpu')
MAX_LEN = 512

tokenizer = T5Tokenizer.from_pretrained('t5-base')
model = T5ForConditionalGeneration.from_pretrained('ZhangCheng/T5-Base-Fine-Tuned-for-Question-Generation')
mod = KeyBERT('distilbert-base-nli-mean-tokens')
model.to(DEVICE)

context = "The Transgender Persons Bill, 2016 was hurriedly passed in the Lok Sabha, amid much outcry from the very community it claims to protect."

def func(context, slide):
    slide = int(slide)
    randomness = 0.4
    orig = int(np.ceil(randomness * slide))
    temp = slide - orig
    ap = filter_keyword(context, ran=slide*2)
    outputs = []
    print(slide)
    print(orig)
    print(ap)
    for i in range(orig):
        inputs = "context: " + context + " keyword: " + ap[i][0]
        source_tokenizer = tokenizer.encode_plus(inputs, max_length=512, pad_to_max_length=True, return_tensors="pt")
        outs = model.generate(input_ids=source_tokenizer['input_ids'].to(DEVICE), attention_mask=source_tokenizer['attention_mask'].to(DEVICE), max_length=50)
        dec = [tokenizer.decode(ids) for ids in outs][0]
        st = dec.replace("<pad> ", "")
        st = st.replace("</s>", "")
        if ap[i][1] > 0.0:
            outputs.append((st, "Good"))
        else:
            outputs.append((st, "Bad"))

    del ap[: orig]
    print("first",outputs)
    print(temp)

    if temp > 0:
        for i in range(temp):
            keyword = random.choice(ap)
            inputs = "context: " + context + " keyword: " + keyword[0]
            source_tokenizer = tokenizer.encode_plus(inputs, max_length=512, pad_to_max_length=True, return_tensors="pt")
            outs = model.generate(input_ids=source_tokenizer['input_ids'].to(DEVICE), attention_mask=source_tokenizer['attention_mask'].to(DEVICE), max_length=50)
            dec = [tokenizer.decode(ids) for ids in outs][0]
            st = dec.replace("<pad> ", "")
            st = st.replace("</s>", "")
            if keyword[1] > 0.0:
                outputs.append((st, "Good"))
            else:
                outputs.append((st, "Bad"))
    print("second",outputs)

    return outputs

gr.Interface(func, [gr.inputs.Textbox(lines=10, label="context"), gr.inputs.Slider(minimum=1, maximum=5, default=1, label="No of Question"),], gr.outputs.KeyValues()).launch()