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| import gradio as gr | |
| from datetime import date | |
| import json | |
| import csv | |
| import datetime | |
| import smtplib | |
| 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 | |
| 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 generate_emotion(article): | |
| 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) | |
| def save_data_and_sendmail(article,output): | |
| try: | |
| print("welcome") | |
| # ip_address = '' | |
| # ip_address = get_device_ip_address() | |
| ip_address=str(request.remote_addr) | |
| print(ip_address) | |
| location = get_location(ip_address) | |
| # location = get_location(ip_address) | |
| # print(ip_address) | |
| add_csv = [article,output,ip_address] | |
| 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) | |
| # location = get_location(ip_addr) | |
| print(location) | |
| url = 'https://pragnakalpdev33.pythonanywhere.com/HF_space_bert_base_ner' | |
| myobj = {'article': article,'gen_text':output,'ip_addr':ip_address} | |
| 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")] | |
| # with gr.Blocks(css=".gradio-container {background-color: lightgray}") as demo: | |
| # gr.Markdown("<h1 style='text-align: center;'>"+ "Named Entity Recognition Using BERT" + "</h1><br/><br/>") | |
| # 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( | |
| # """ | |
| # <p style='text-align: center;'>Feel free to give us your <a href="https://www.pragnakalp.com/contact/"> feedback </a> on this NER demo. | |
| # For all your Named Entity Recognition related requirements, we are here to help you.<br /> | |
| # Email us your requirement at <a href="mailto:letstalk@pragnakalp.com"> letstalk@pragnakalp.com </a>. | |
| # And don't forget to check out more interesting <a href="https://www.pragnakalp.com/services/natural-language-processing-services/">NLP services</a> we are offering.<br/> | |
| # <b>Developed by</b> : <a href="https://www.pragnakalp.com" target="_blank">Pragnakalp Techlabs </a></p> | |
| # """) | |
| # event = sub_btn.click(generate_emotion, inputs=input, outputs=output) | |
| # demo.launch() | |
| demo = gr.Interface( | |
| generate_emotion, | |
| 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](https://www.pragnakalp.com/contact/) 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]("mailto:letstalk@pragnakalp.com"). | |
| And don't forget to check out more interesting [NLP services](https://www.pragnakalp.com/services/natural-language-processing-services/) we are offering. | |
| <p style='text-align: center;'>Developed by :[ Pragnakalp Techlabs](https://www.pragnakalp.com)</p>""" | |
| ) | |
| demo.launch() |