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| import gradio as gr | |
| import requests | |
| import os | |
| import numpy as np | |
| import pandas as pd | |
| import json | |
| import socket | |
| import huggingface_hub | |
| from huggingface_hub import Repository | |
| # from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification | |
| from questiongenerator import QuestionGenerator | |
| import csv | |
| from urllib.request import urlopen | |
| import re as r | |
| qg = QuestionGenerator() | |
| HF_TOKEN = os.environ.get("HF_TOKEN") | |
| DATASET_NAME = "Text2Question" | |
| DATASET_REPO_URL = f"https://huggingface.co/spaces/bhaskartripathi/{DATASET_NAME}" | |
| DATA_FILENAME = "que_gen_logs.csv" | |
| DATA_FILE = os.path.join("que_gen_logs", DATA_FILENAME) | |
| DATASET_REPO_ID = "bhaskartripathi/Text2Question" | |
| print("is none?", HF_TOKEN is None) | |
| article_value = """Affecting computing is an artificial intelligence area of study that recognizes, interprets, processes, and simulates human affects. The user’s emotional states can be sensed through electroencephalography (EEG)-based Brain Computer Interfaces (BCI) devices. Research in emotion recognition using these tools is a rapidly growing field with multiple inter-disciplinary applications. This article performs a survey of the pertinent scientific literature from 2015 to 2020. It presents trends and a comparative analysis of algorithm applications in new implementations from a computer science perspective. Our survey gives an overview of datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation. Lastly, we provide insights for future developments.""" | |
| # 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 getIP(): | |
| ip_address = '' | |
| try: | |
| d = str(urlopen('http://checkip.dyndns.com/') | |
| .read()) | |
| return r.compile(r'Address: (\d+\.\d+\.\d+\.\d+)').search(d).group(1) | |
| except Exception as e: | |
| print("Error while getting IP address -->",e) | |
| return ip_address | |
| def get_location(ip_addr): | |
| location = {} | |
| try: | |
| ip=ip_addr | |
| req_data={ | |
| "ip":ip, | |
| "token":"pkml123" | |
| } | |
| url = "https://bhaskartripathi.com/get-ip-location" | |
| # req_data=json.dumps(req_data) | |
| # print("req_data",req_data) | |
| headers = {'Content-Type': 'application/json'} | |
| response = requests.request("POST", url, headers=headers, data=json.dumps(req_data)) | |
| response = response.json() | |
| print("response======>>",response) | |
| return response | |
| except Exception as e: | |
| print("Error while getting location -->",e) | |
| return location | |
| def generate_questions(article,num_que): | |
| result = '' | |
| if article.strip(): | |
| if num_que == None or num_que == '': | |
| num_que = 3 | |
| 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",'') | |
| for q in generated_questions: | |
| print(q) | |
| result = result + q + '\n' | |
| #save_data_and_sendmail(article,generated_questions,num_que) | |
| print("sending result***!!!!!!", result) | |
| return result | |
| else: | |
| raise gr.Error("Please enter text in inputbox!!!!") | |
| """ | |
| Save generated details | |
| """ | |
| def save_data_and_sendmail(article,generated_questions,num_que): | |
| try: | |
| ip_address= getIP() | |
| print(ip_address) | |
| location = get_location(ip_address) | |
| print(location) | |
| add_csv = [article, generated_questions, num_que, ip_address,location] | |
| print("data^^^^^",add_csv) | |
| with open(DATA_FILE, "a") as f: | |
| writer = csv.writer(f) | |
| # write the data | |
| writer.writerow(add_csv) | |
| commit_url = repo.push_to_hub() | |
| print("commit data :",commit_url) | |
| url = 'https://bhaskartripathi.com/HF_space_que_gen' | |
| myobj = {'article': article,'total_que': num_que,'gen_que':generated_questions,'ip_addr':ip_address,'loc':location} | |
| x = requests.post(url, json = myobj) | |
| print("myobj^^^^^",myobj) | |
| except Exception as e: | |
| return "Error while sending mail" + str(e) | |
| return "Successfully save data" | |
| ## design 1 | |
| inputs=gr.Textbox(value=article_value, lines=5, label="Input Text/Article",elem_id="inp_div") | |
| total_que = gr.Textbox(value=3, label="Enter the number of questions to generate",elem_id="inp_div") | |
| outputs=gr.Textbox(label="Generated Questions",lines=6,elem_id="inp_div") | |
| demo = gr.Interface( | |
| generate_questions, | |
| [inputs,total_que], | |
| outputs, | |
| title="Text2Question Generation with Text-to-Text-Transfer-Transformer", | |
| css=".gradio-container {background-color: lightgray} #inp_div {background-color: #7FB3D5;}", | |
| article="""<p style='text-align: center;'><a href="https://github.com/bhaskatripathi/QuestAnsGenerator/issues" target="_blank">Raise Issues</a></p> | |
| <p style='text-align: center;'>MultiCloud4U Sandbox Env <a href="https://www.multicloud4u.com" target="_blank">Multicloud4U Technologies Pvt. Ltd.</a></p>""" | |
| ) | |
| demo.launch() |