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 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://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()