main.py

import tensorflow as tf
import numpy as np
import pandas as pd
import swifter
import json
import re
import requests
import time
from keras.preprocessing.text import Tokenizer
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
# from keras.optimizers.optimizer_v2.rmsprop import RMSProp
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense, Conv1D, Embedding, MaxPooling1D, GlobalMaxPooling1D, GlobalAveragePooling1D, SpatialDropout1D, LSTM, Dropout, SimpleRNN, Bidirectional, Attention, Activation, GRU, TextVectorization, Input
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.preprocessing.sequence import pad_sequences
import arabicstopwords.arabicstopwords as stp
from nltk.stem.isri import ISRIStemmer
from pyarabic.araby import strip_tashkeel, strip_tatweel
from huggingface_hub import from_pretrained_keras
from collections import Counter
from fastapi import FastAPI, Request, HTTPException
import firebase_admin
from firebase_admin import credentials
from firebase_admin import firestore
import threading
# Import the Firebase Admin SDK
import firebase_admin
from firebase_admin import credentials
from firebase_admin import firestore
from transformers import BertTokenizer, AutoModelForSeq2SeqLM, pipeline
from arabert.preprocess import ArabertPreprocessor
from transformers import AutoTokenizer, AutoModelForCausalLM
import re
import smtplib
from email.mime.text import MIMEText
import os
import math
import random
# Firebase ininlaziton
cred = credentials.Certificate(
    "text-to-emotions-firebase-adminsdk-8isbn-dffbdf01e8.json")
firebase_admin.initialize_app(cred)

# Model summury
model_name="abdalrahmanshahrour/auto-arabic-summarization"
preprocessor = ArabertPreprocessor(model_name="")

tokenizer = AutoTokenizer.from_pretrained(model_name)
modelsummary =AutoModelForSeq2SeqLM.from_pretrained(model_name)
pipeline1 = pipeline("text2text-generation",model=modelsummary,tokenizer=tokenizer)

# Model inilization
isristemmer = ISRIStemmer()
model = from_pretrained_keras('MahmoudNasser/GRU-MODEL-EMOTION-AR-TEXT-76jP')
# dictinarties for email OTP
emailOTP={}


def stemming(txt):
    return isristemmer.stem(txt)


def remove_singleCharacter(text):
    text_tokenized = ar.tokenize(text)
    clean_txt = ''
    for word in text_tokenized:
        if len(word) != 1:
            clean_txt = clean_txt + word + ' '

    return clean_txt[:-1]

# remove_punctuations


def remove_punctuations(text):
    punc = '''()-[]{};:'"\,<>./@#$%^&*،؛_~'''
    arabic_punctuations = '''`÷×؛_ـ،/:".,'~¦+|”…“–ـ=﴾﴿ ﹱ ﹹ ⸀˓• ב'''
    punctuations_list = punc + arabic_punctuations
    for x in punctuations_list:
        text = text.replace(x, ' ')
    return text


def normalize_text(txt):
    txt = strip_tashkeel(txt)
    txt = strip_tatweel(txt)
    txt = ''.join(txt[i] for i in range(len(txt)) if i ==
                  0 or txt[i-1] != txt[i])  # remove repeated characters
    return txt


def remove_stopwords(txt, path="stopword.txt"):
    text_tokenized = txt.split(' ')
    clean_txt = ''
#   useful_words=[]
#   filtered_sentence=" "
    arabic_stop_words_file = open(path, 'r', encoding='utf-8')
    arabic_stop_words = arabic_stop_words_file.read().split('\n')
    for word in text_tokenized:
        if word not in arabic_stop_words:
            clean_txt = clean_txt + word + ' '

    return clean_txt[:-1]


def Remove_unwanted(text):
    # removing the extra spacing and links

    text = re.sub(r'^https?:\/\/.*[\r\n]*', ' ', text, flags=re.MULTILINE)
    text = re.sub(r'^http?:\/\/.*[\r\n]*', ' ', text, flags=re.MULTILINE)
    text = re.sub(r"http\S+", " ", text)
    text = re.sub(r"https\S+", " ", text)
    text = re.sub(r'\s+', ' ', text)
    text = re.sub(r'[a-zA-Z]+', ' ', text)
    text = re.sub(r"^\s+|\s+$", "", text)
    text = re.sub(r"(\s\d+)", " ", text)
    text = re.sub(r"$\d+\W+|\b\d+\b|\W+\d+$", " ", text)
    text = re.sub(r"\d+", " ", text)
    text = re.sub(r'[إأٱآا]', 'ا', text)
    text = re.sub(r'ى', '[ي]', text)
    text = re.sub(r'ء', '[ؤئ]', text)
    text = re.sub(r' +', ' ', text)
    return text


def txt_preprocess(text):
    text = normalize_text(text)
    text = stemming(text)
    text = remove_stopwords(text)
    text = remove_punctuations(text)
    text = Remove_unwanted(text)
    return text


def see_if_thereupdates():
    f = open("updates.txt", "r")
    return f.readline()


def getmodel():
    m = from_pretrained_keras('MahmoudNasser/GRU-MODEL-EMOTION-AR-TEXT-72P')
    return m


def original_values(num):
    if num == 0:
        return 'anger'
    elif num == 1:
        return 'sadness'
    elif num == 2:
        return 'joy'
    elif num == 3:
        return 'surprise'
    elif num == 4:
        return 'love'
    elif num == 5:
        return 'sympathy'
    elif num == 6:
        return 'fear'
def modelsummary(data):
    result = pipeline1(data,
    pad_token_id= tokenizer.eos_token_id,
    num_beams=4,
    repetition_penalty=3.0,
    max_length=600,
    length_penalty=.50,
    no_repeat_ngram_size = 3)[0]['generated_text']
    result = remove_punctuations(result)
    return { 'summary':result}

def modelpredict(data):
    data = txt_preprocess(data)
    pred = model.predict(pd.Series([data]))
    return {'anger': float(pred[0][0]), 'sadness': float(pred[0][1]), 'joy': float(pred[0][2]), 'surprise': float(pred[0][3]),
            'love': float(pred[0][4]), 'sympathy': float(pred[0][5]), 'fear': float(pred[0][6])}
    # return {"anger": .90, "happy": .02, "emotionlabel": "anger"}
    
#OTP code 
def genereteotp (email):    
  digits = "0123456789"
  OTP = ""
  for i in range(6):
      OTP += digits[math.floor(random.random()*10)]
  emailverification[email]=OTP
  otp = "your otp is "+OTP
  s = smtplib.SMTP('smtp.gmail.com', 587)
        # start TLS for security
  s.starttls()
  # Authentication
  s.login("youssefmk1214@gmail.com", "lipnacjbsxmjpjxm")
  # message to be sent
  message = otp
  # instance of MIMEText
  msg = MIMEText(message)
  # sender's email address
  msg['From'] = "youssefmk1214@gmail.com"
  # recipient's email address
  msg['To'] = email
  # subject of the email
  msg['Subject'] = " Shakwa Textual OTP"
  # send the message via SMTP server
  s.sendmail(msg['From'], msg['To'], msg.as_string())
  # terminate the SMTP session
  s.quit()    

# Main Server inilization
app = FastAPI()

@app.get("/")
def index():
    return "Hello World"
@app.post("/summary")
async def read_root(request:Request):
    json_data = await request.json()
    if 'text'in json_data:
        return modelsummary(json_data['text'])
    else:
        raise HTTPException(status_code=400, detail="Missing text value")
@app.post("/getOTPCode")
async def read_root(request:Request):
    json_data = await request.json()
    if 'email' in json_data:
        genereteotp(json_data["email"])
        return "message was sent succesufully to "+json_data['email']
    else:
       raise HTTPException(status_code=400, detail="Missing email value")
@app.post("/verifyOTP")
async def read_root(request:Request):
       if 'email' in json_data and 'otpcode' in json_data:
            if json_data['otpcode'] ==emailOTP[json_data['email']]  :
                return "OTP verified succesufully "
            else:
                return "OTP Code is wrong "
       else:
           raise HTTPException(status_code=400, detail="Missing email value")
    
@app.post("/predict")
async def read_root(request: Request):
    json_data = await request.json()
    if "mathod" in json_data and json_data["mathod"] == "emotion_predict" and 'text' in json_data:
        return modelpredict(json_data["text"])
    else:
        raise HTTPException(status_code=400, detail="Missing mathod value")
@app.get("/commonwords")
def getcommonwords():
    return {'التسجيل': 23, 'مش': 19, 'تطبيق': 18, 'التطبيق': 18, 'التفعيل': 17, 'كود': 13, 'ارسال': 12, 'تسجيل': 12, 'يتم': 12, 'الرقم': 12}