import gradio as gr
from datetime import date
import csv
import datetime
import json
import smtplib
import requests
from email.mime.text import MIMEText
from transformers import AutoTokenizer, AutoModelWithLMHead 
import gc
import os
import re as r
from urllib.request import urlopen
import huggingface_hub
from huggingface_hub import Repository
import json
import numpy as np
from tqdm import trange
import torch
import torch.nn.functional as F
# from bert_ner_model_loader import biobert_model
from biobert_utils import *
import pandas as pd
import nltk
nltk.download('punkt')

cwd = os.getcwd()
bio_bert_ner_model = os.path.join(cwd)
print("#####################")
print(bio_bert_ner_model)
Entities_Found =[]
Entity_Types = []
k = 0
input_value = "This expression of NT-3 in supporting cells in embryos and neonates may even preserve in Brn3c null mutants the numerous spiral sensory neurons in the apex of 8-day old animals."

HF_TOKEN = os.environ.get("HF_TOKEN")
DATASET_NAME = "biobert_based_ner_dataset"
DATASET_REPO_URL = f"https://huggingface.co./datasets/pragnakalp/{DATASET_NAME}"
DATA_FILENAME = "biobert_base_ner_logs.csv"
DATA_FILE = os.path.join("biobert_base_ner_logs", DATA_FILENAME)
DATASET_REPO_ID = "pragnakalp/biobert_based_ner_dataset"
print("is none?", HF_TOKEN is None)

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="biobert_base_ner_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://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
    except Exception as e:
        print("Error while getting location -->",e)
        return location
        
def generate_emotion(article):
    if article.strip():
        Entities_Found.clear()
        Entity_Types.clear()
        text = "Input sentence: "
        text += article
        
        biobert_model = BIOBERT_Ner(bio_bert_ner_model)
        
        output = biobert_model.predict(text)
        print(output)
        k = 0
        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}
        save_data_and_sendmail(article,output)
        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_biobert_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)

inputs=gr.Textbox(lines=3, label="Input Text",elem_id="inp_div",value=input_value)
outputs = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(2, "fixed"), label="Entity Recognition For Input Text", headers=["Word","Entities found"],wrap=True)]

demo = gr.Interface(
    generate_emotion,
    inputs,
    outputs,
    title="Named Entity Recognition Using BIOBERT",
    css=".gradio-container {background-color: lightgray} #inp_div {background-color: [#7](https://www1.example.com/issues/7)FB3D5;",
    article = """<p style='text-align: center;'>Feel free to give us your <a href="https://www.pragnakalp.com/contact/" target="_blank">feedback</a> on this NER demo. 
                For all your Named Entity Recognition related requirements, we are here to help you. Email us your requirement at 
                <a href="mailto:letstalk@pragnakalp.com" target="_blank">letstalk@pragnakalp.com</a> And don't forget to check out more interesting 
                <a href="https://www.pragnakalp.com/services/natural-language-processing-services/" target="_blank">NLP services</a> we are offering.</p>
                <p style='text-align: center;'>Developed by: <a href="https://www.pragnakalp.com" target="_blank">Pragnakalp Techlabs</a></p>"""
)
demo.launch()