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

cwd = os.getcwd()
model_path = os.path.join(cwd)
tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-emotion")
model_base = AutoModelWithLMHead.from_pretrained(model_path)
def get_emotion(text):

    # input_ids = tokenizer.encode(text + '</s>', return_tensors='pt')
    input_ids = tokenizer.encode(text, return_tensors='pt')
    output = model_base.generate(input_ids=input_ids,
                                 max_length=2)

    dec = [tokenizer.decode(ids) for ids in output]
    label = dec[0]
    gc.collect()
    return label

def generate_emotion(article):
    sen_list = article
    sen_list = sen_list.split('\r\n')
    sen_list_temp = sen_list[0:]
    results_dict = []
    results = []

    for sen in sen_list_temp:
        if(sen.strip()):
            log_sen_list.append(sen)
            cur_result = get_emotion(sen)

            results.append(cur_result)
            results_dict.append(
                {
                    'sentence': sen,
                    'emotion': cur_result
                }
            )

    result = {
        'result': results_dict,
    }
    gc.collect()
    print("LENGTH of results ====> ", results)
    
    return result

inputs=gr.Textbox(lines=10, label="Sentences",elem_id="inp_div")
outputs=gr.Textbox(lines=10, label="Here is the Result",elem_id="inp_div")

demo = gr.Interface(
    generate_emotion,
    inputs,
    outputs,
    title="Emotion Detection",
    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()