app.py

import numpy as np
from flask import Flask, request, jsonify, render_template
import simpletransformers
import pandas as pd
import requests
from simpletransformers.ner import NERModel
import json 
import io
from flask_csv import send_csv
from flask import make_response
from flask import session, redirect

app = Flask(__name__)
@app.route('/')
def home():
    return render_template('index.html')

@app.route('/predict',methods=['POST',"GET"])
def predict():
    '''
    For rendering results on HTML GUI
    '''
    int_features = [x for x in request.form.values()]
    print(int_features)
    sentence = int_features[0]
    if int_features[1] == 'display':
        model1 = NERModel('bert', 'MODEL',
                  labels=[LIST OF UNIQUE LABELS],
                  args={"save_eval_checkpoints": False,
        "save_steps": -1,
        "output_dir": "MODEL",
        'overwrite_output_dir': True,
        "save_model_every_epoch": False,
        'reprocess_input_data': True, 
        "train_batch_size": 10,'num_train_epochs': 5,"max_seq_length": 256}, use_cuda=False)
        predictions, raw_outputs = model1.predict([sentence])     
        result = json.dumps(predictions[0])        
        return render_template('index.html', prediction_text=result)

    elif int_features[1] == 'getcsv':
        model1 = NERModel('bert', 'MODEL',
                  labels=[LIST OF LABELS],
                  args={"save_eval_checkpoints": False,
        "save_steps": -1,
        "output_dir": "MODEL",
        'overwrite_output_dir': True,
        "save_model_every_epoch": False,
        'reprocess_input_data': True, 
        "train_batch_size": 10,'num_train_epochs': 5,"max_seq_length": 256}, use_cuda=False)
        predictions, raw_outputs = model1.predict([sentence])
        l=[]
        for i in predictions[0]:
            dic={}
            for j in i.keys():
                dic['word']=j
                dic['tag']=i[j]
            l.append(dic)
        return send_csv(l,"tags.csv",["word","tag"])
      
if __name__ == "__main__":
    app.run(debug=True)

Thank you!