from flask import Flask, render_template, request, jsonify
import torch
import transformers
from transformers import AutoTokenizer, AutoModel , AutoModelForCausalLM
from transformers import AutoModelForSeq2SeqLM
import pickle
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import nltk
from nltk.tokenize import word_tokenize
import re
import string
from nltk.corpus import stopwords
from tashaphyne.stemming import ArabicLightStemmer
import pyarabic.araby as araby
from sklearn.feature_extraction.text import TfidfVectorizer
from flask import Flask, render_template, request, redirect, url_for
import os
nltk.download('punkt')


app = Flask(__name__)


with open('tfidf_vectorizer.pkl', 'rb') as f:
    vectorizer = pickle.load(f)

with open('svm_model.pkl', 'rb') as f:
    model_classify = pickle.load(f)


model = AutoModelForSeq2SeqLM.from_pretrained("bushra1dajam/AraBART")
tokenizer = AutoTokenizer.from_pretrained('bushra1dajam/AraBART')


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

def summarize_text(text):
    inputs = tokenizer("summarize: " + text, return_tensors="pt", max_length=512, truncation=True)
    inputs = {k: v.to(device) for k, v in inputs.items()}

    summary_ids = model.generate(
    inputs["input_ids"],
    max_length=512,
    num_beams=8,
    #no_repeat_ngram_size=4,  # Prevents larger n-gram repetitions
    early_stopping=True)
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary

def remove_numbers(text):
    cleaned_text = re.sub(r'\d+', '', text)
    return cleaned_text

def Removing_non_arabic(text):
    text =re.sub(r'[^0-9\u0600-\u06ff\u0750-\u077f\ufb50-\ufbc1\ufbd3-\ufd3f\ufd50-\ufd8f\ufd50-\ufd8f\ufe70-\ufefc\uFDF0-\uFDFD.0-9٠-٩]+', ' ',text)
    return text

nltk.download('stopwords')
ara_punctuations = '''`÷×؛<>_()*&^%][ـ،/:"؟.,'{}~¦+|!”…“–ـ''' + string.punctuation
stop_words = stopwords.words()

def remove_punctuations(text):
    translator = str.maketrans('', '', ara_punctuations)
    text = text.translate(translator)

    return text


def remove_tashkeel(text):
    text = text.strip()
    text = re.sub("[إأٱآا]", "ا", text)
    text = re.sub("ى", "ي", text)
    text = re.sub("ؤ", "ء", text)
    text = re.sub("ئ", "ء", text)
    text = re.sub("ة", "ه", text)
    noise = re.compile(""" ّ    | # Tashdid
                             َ    | # Fatha
                             ً    | # Tanwin Fath
                             ُ    | # Damma
                             ٌ    | # Tanwin Damm
                             ِ    | # Kasra
                             ٍ    | # Tanwin Kasr
                             ْ    | # Sukun
                             ـ     # Tatwil/Kashida
                         """, re.VERBOSE)
    text = re.sub(noise, '', text)
    text = re.sub(r'(.)\1+', r"\1\1", text)
    return araby.strip_tashkeel(text)

arabic_stopwords = stopwords.words("arabic")
def remove_stop_words(text):
    Text=[i for i in str(text).split() if i not in arabic_stopwords]
    return " ".join(Text)

def tokenize_text(text):
    tokens = word_tokenize(text)
    return tokens

def Arabic_Light_Stemmer(text):

    Arabic_Stemmer = ArabicLightStemmer()
    text=[Arabic_Stemmer.light_stem(y) for y in text]

    return " " .join(text)

def preprocess_text(text):
    text = remove_numbers(text)
    text = Removing_non_arabic(text)
    text = remove_punctuations(text)
    text = remove_stop_words(text)
    text = remove_tashkeel(text)
    text = tokenize_text(text)
    text = Arabic_Light_Stemmer(text)
    return text

class_mapping = {
    0: "جنائية",
    1: "احوال شخصية",
    2: "عامة"
}


@app.route('/')
def index():
    return render_template('index.html')

@app.route('/result', methods=['GET', 'POST'])
def result():
    if request.method == 'POST':
        input_text = request.form['text']
        if input_text:
            prepro = preprocess_text(input_text)
            features = vectorizer.transform([prepro])
            prediction = model_classify.predict(features)
            classifiy = prediction[0]
            classifiy_class = class_mapping.get(classifiy, "لم يتم التعرف")
            summarized_text = summarize_text(input_text)
        return render_template('result.html', classification=classifiy_class, summary=summarized_text, input_text=input_text)

    return render_template('result.html')



@app.route('/profile')
def profile():
    return render_template('profile.html')

@app.route('/login', methods=['GET', 'POST'])
def login():
    return render_template('login.html')

@app.route('/create_account', methods=['GET', 'POST'])
def create_account():
    return render_template('create_account.html')


if __name__ == '__main__':
    app.run(debug=True)