app.py

import gradio as gr

import torch
from transformers import (
    AutoTokenizer,
    AutoModelForSeq2SeqLM,
    AutoModelForSequenceClassification,
)

QG_PRETRAINED = "ehsanul007/IAmA-question-generator"
SEQ_LENGTH = 512

device = torch.device(
    "cuda" if torch.cuda.is_available() else "cpu")

qg_tokenizer = AutoTokenizer.from_pretrained(
    QG_PRETRAINED, use_fast=False)
qg_model = AutoModelForSeq2SeqLM.from_pretrained(QG_PRETRAINED)
qg_model.to(device)
qg_model.eval()


def evaluate(topic, context):
    text = f'<topic> {topic} <context> {context}'
    encoded_text = qg_tokenizer(
            text,
            padding='max_length',
            max_length=SEQ_LENGTH,
            truncation=True,
            return_tensors="pt",
        ).to(device)
    output = qg_model.generate(
            input_ids=encoded_text["input_ids"],
            max_length=100
        )
    question = qg_tokenizer.decode(
            output[0],
            skip_special_tokens=True
        )
    return question





g = gr.Interface(
    fn=evaluate,
    inputs=[
        gr.components.Textbox(
            lines=2, label="Topic", placeholder="Topic Name."
        ),
        gr.components.Textbox(lines=2, label="Your Details", placeholder="I am ..."),
    ],
    outputs=[
        gr.inputs.Textbox(
            lines=5,
            label="What AI wants to ask you",
        )
    ],
    title="IAmA Question Generator",
    description="Write down who you are (add details) and which topic you want to be asked a question on",
)
g.queue(concurrency_count=1)
g.launch()