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import gradio as gr
import requests
import os
import numpy as np
import pandas as pd
import json
# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification
from questiongenerator import QuestionGenerator
qg = QuestionGenerator()
HF_TOKEN = os.environ.get("HF_TOKEN")
DATASET_NAME = "Question_Generation_T5"
DATASET_REPO_URL = f"https://huggingface.co/datasets/pragnakalp/{DATASET_NAME}"
DATA_FILENAME = "que_gen_logs.json"
DATA_FILE = os.path.join("que_gen_logs", DATA_FILENAME)
DATASET_REPO_ID = "pragnakalp/Question_Generation_T5"
print("is none?", HF_TOKEN is None)
# REPOSITORY_DIR = "data"
# LOCAL_DIR = 'data_local'
# os.makedirs(LOCAL_DIR,exist_ok=True)
try:
hf_hub_download(
repo_id=DATASET_REPO_ID,
filename=DATA_FILENAME,
cache_dir=DATA_DIRNAME,
force_filename=DATA_FILENAME
)
except:
print("file not found")
repo = Repository(
local_dir="que_gen_logs", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
)
def generate_questions(article,num_que):
result = ''
print("num_que :", num_que)
if num_que == None or num_que == '':
num_que = 5
else:
num_que = num_que
generated_questions_list = qg.generate(article, num_questions=int(num_que))
summarized_data = {
"generated_questions" : generated_questions_list
}
generated_questions = summarized_data.get("generated_questions",'')
entry = {"article": article, "generated_questions": generated_questions, "num_of_question": num_que}
with open(DATA_FILE, "r") as file:
data = json.load(file)
data.append(entry)
with open(DATA_FILE, "w") as file:
json.dump(data, file)
commit_url = repo.push_to_hub()
for q in generated_questions:
print(q)
result = result + q + '\n'
return result
## design 1
inputs=gr.Textbox(lines=5, label="Article/Text",elem_id="inp_div")
total_que = gr.Textbox(label="Number of Question want to generate",elem_id="inp_div")
outputs=gr.Textbox(lines=5, label="Generated Questions",elem_id="inp_div")
demo = gr.Interface(
generate_questions,
[inputs,total_que],
outputs,
title="Question Generation using T5",
description="Feel free to give your feedback",
css=".gradio-container {background-color: lightgray} #inp_div {background-color: #7FB3D5;"
)
demo.launch(enable_queue = False)
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