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 gc
import os
import numpy as np
import json
from tqdm import trange
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

HF_TOKEN = os.environ.get("HF_TOKEN")
DATASET_NAME = "bert_base_ner"
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)

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():
    result = {}
    if os.name == "nt":
        result = "Running on Windows"
        hostname = socket.gethostname()
        ip_address = socket.gethostbyname(hostname)
        result['ip_addr'] = ip_address
        result['host'] = hostname
        print(result)
        return result
    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))
        ipaddr = s.getsockname()[0]
        gateway = gw[2]
        host = socket.gethostname()
        result['ip_addr'] = ipaddr
        result['host'] = host
        print(result)
        return result
    else:
        result['id'] = os.name + " not supported yet."
        print(result)
        return result


def generate_emotion(article):
    text = "Input sentence: "
    text += article
    
    model_ner = Ner(bert_ner_model)
    
    output = model_ner.predict(text)
    print(output)
    k = 0
    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(Inputdata,output):
    try:
        hostname = {}
        hostname = get_device_ip_address()
        # 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) 
        
        ip = hostname.get("ip_addr","")
        add_csv = [Inputdata,Generate_text,IP]
        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)
        return "Successfully save data"
    
    except Exception as e:
        return "Error while sending mail" + str(e)
        

inputs=gr.Textbox(lines=10, label="Sentences",elem_id="inp_div")
outputs = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(2, "fixed"), label="Here is the Result", headers=["Entities Found","Entity Types"])]

demo = gr.Interface(
    generate_emotion,
    inputs,
    outputs,
    title="Entity Recognition For Input Text",
    description="Feel free to give your feedback", 
    css=".gradio-container {background-color: lightgray} #inp_div {background-color: [#7](https://www1.example.com/issues/7)FB3D5;"
)
demo.launch()