import gradio as gr from datetime import date import json import datetime import smtplib import csv from email.mime.text import MIMEText import requests from transformers import AutoTokenizer, AutoModelWithLMHead import os import numpy as np import json from tqdm import trange import gc import torch import torch.nn.functional as F from bert_ner_model_loader import Ner import pandas as pd from huggingface_hub import Repository import huggingface_hub import socket from urllib.request import urlopen import re as r HF_TOKEN = os.environ.get("HF_TOKEN") DATASET_NAME = "bert_based_ner_dataset" DATASET_REPO_URL = f"https://huggingface.co/datasets/pragnakalp/{DATASET_NAME}" DATA_FILENAME = "bert_base_ner_logs.csv" DATA_FILE = os.path.join("bert_base_ner_logs", DATA_FILENAME) DATASET_REPO_ID = "pragnakalp/bert_based_ner_dataset" print("is none?", HF_TOKEN is None) input_value = "The U.S. President Donald Trump came to visit Ahmedabad first time at Motera Stadium with our Prime Minister Narendra Modi in February 2020" 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="bert_base_ner_logs", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN ) cwd = os.getcwd() bert_ner_model = os.path.join(cwd) Entities_Found =[] Entity_Types = [] k = 0 # def get_device_ip_address(): # if os.name == "nt": # result = "Running on Windows" # hostname = socket.gethostname() # ip_address = socket.gethostbyname(hostname) # return ip_address # elif os.name == "posix": # gw = os.popen("ip -4 route show default").read().split() # s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # s.connect((gw[2], 0)) # ip_address = s.getsockname()[0] # gateway = gw[2] # host = socket.gethostname() # return ip_address # else: # result['id'] = os.name + " not supported yet." # print(result) # return result # def get_location(ip_address): # ip=ip_address # # ip=str(request.remote_addr) # req_data={ # "ip":ip, # "token":"pkml123" # } # url = "https://demos.pragnakalp.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 # def get_ip(): # response = requests.get('https://api64.ipify.org?format=json').json() # return response["ip"] # def get_location(ip_addr): # ip_address = ip_addr # response = requests.get(f'https://ipapi.co/{ip_address}/json/').json() # location_data = { # "ip": ip_address, # "city": response.get("city"), # "region": response.get("region"), # "country": response.get("country_name") # } # return location_data def getIP(): d = str(urlopen('http://checkip.dyndns.com/') .read()) return r.compile(r'Address: (\d+\.\d+\.\d+\.\d+)').search(d).group(1) def get_location(ip_addr): ip=ip_addr req_data={ "ip":ip, "token":"pkml123" } url = "https://demos.pragnakalp.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 def generate_ner(article): result = {'Entities Found':[], 'Entity Types':[]} if article.strip(): text = "Input sentence: " text += article model_ner = Ner(bert_ner_model) output = model_ner.predict(text) print(output) k = 0 Entities_Found.clear() Entity_Types.clear() save_data_and_sendmail(article,output) for i in output: for j in i: if k == 0: Entities_Found.append(j) k += 1 else: Entity_Types.append(j) k = 0 result = {'Entities Found':Entities_Found, 'Entity Types':Entity_Types} return pd.DataFrame(result) else: raise gr.Error("Please enter text in inputbox!!!!") def save_data_and_sendmail(article,output): try: print("welcome") ip_address = '' ip_address= getIP() print(ip_address) location = get_location(ip_address) print(location) add_csv = [article,output,ip_address,location] 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://pragnakalpdev35.pythonanywhere.com/HF_space_que_gen' # # url = 'http://pragnakalpdev33.pythonanywhere.com/HF_space_question_generator' # myobj = {'article': article,'total_que': num_que,'gen_que':result,'ip_addr':hostname.get("ip_addr",""),'host':hostname.get("host","")} # x = requests.post(url, json = myobj) url = 'https://pragnakalpdev33.pythonanywhere.com/HF_space_bert_base_ner' myobj = {'article': article,'gen_text':output,'ip_addr':ip_address,"location":location} x = requests.post(url, json = myobj) return "Successfully save data" except Exception as e: print("error") return "Error while sending mail" + str(e) input=gr.Textbox(lines=3, value=input_value, label="Input Text") output = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(2, "fixed"), headers=["Entities Found","Entity Types"], lable="Here is the result",wrap=True)] # with gr.Blocks(css=".gradio-container {background-color: lightgray}") as demo: # gr.Markdown("

"+ "Named Entity Recognition Using BERT" + "



") # with gr.Row(): # with gr.Column(): # input=gr.Textbox(lines=5, value=input_value, label="Input Text") # sub_btn = gr.Button("Submit") # output = gr.Dataframe(row_count = (3, "dynamic"), col_count=(2, "fixed"), headers=["Entities Found","Entity Types"]) # gr.Markdown( # """ #

Feel free to give us your feedback on this NER demo. # For all your Named Entity Recognition related requirements, we are here to help you.
# Email us your requirement at letstalk@pragnakalp.com . # And don't forget to check out more interesting NLP services we are offering.
# Developed by : Pragnakalp Techlabs

# """) # event = sub_btn.click(generate_emotion, inputs=input, outputs=output) # demo.launch() demo = gr.Interface( generate_ner, input, output, title="Named Entity Recognition Using BERT", css=".gradio-container {background-color: lightgray} #inp_div {background-color: #7FB3D5;", article="""

Feel free to give us your feedback on this NER demo. For all your Named Entity Recognition related requirements, we are here to help you. Email us your requirement at letstalk@pragnakalp.com. And don't forget to check out more interesting NLP services we are offering.

Developed by : Pragnakalp Techlabs

""" ) demo.launch()