app.py

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_base_ner"
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)
        print(result)
        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()
        print(result)
        return ip_address
    else:
        result['id'] = os.name + " not supported yet."
        print(result)
        return result

def get_location(ip_addr):
    ip=ip_addr
    # 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 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:
        ip_address = ''
        ip_address = get_device_ip_address()
        location = get_location(ip_address)

        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,'loc':location}
        x = requests.post(url, json = myobj) 
        
        return "Successfully save data"
    
    except Exception as e:
        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:[email protected]"> [email protected] </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}",
    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 [[email protected]]("mailto:[email protected]").
            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()