app.py

import torch
import transformers
from transformers import AutoTokenizer, AutoModel , AutoModelForCausalLM
from transformers import AutoModelForSeq2SeqLM, GenerationConfig, AutoConfig ,BartForConditionalGeneration
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
import streamlit as st
nltk.download('punkt')



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/Wajeez_model")
tokenizer = AutoTokenizer.from_pretrained('bushra1dajam/Wajeez_model',use_fast=False)

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"
}
st.markdown("""
    <style>
        body {
            background-color: #f0f4f8;
            direction: rtl;
            font-family: 'Arial', sans-serif;
        }

        .logo-container {
            display: flex;
            justify-content: center;
            align-items: center;
            margin-bottom: 20px;
        }

        .stTextArea textarea, .stText {
            text-align: right;
        }

        .stButton>button {
            background-color: #3498db;
            color: white;
            font-family: 'Arial', sans-serif;
        }

        .stButton>button:hover {
            background-color: #2980b9;
        }

        h1, h2, h3, h4, h5, h6, .stSubheader {
            text-align: right;
        }

        .home-title {
            text-align: center;
            font-size: 40px;
            color: #3498db;
        }

        .home-description {
            text-align: center;
            font-size: 20px;
            color: #2c3e50;
        }

        .larger-text {
            font-size: 24px;
            color: #2c3e50;
        }
    </style>
""", unsafe_allow_html=True)


# Function for the Home Page
def home_page():
    st.markdown('<h1 class="home-title">مرحبا بك في تطبيق وجيز</h1>', unsafe_allow_html=True)
    st.markdown('<p class="home-description">تطبيق وجيز يقدم لك خدمة التصنيف والملخص للنصوص القانونية. يمكنك إدخال النصوص هنا للحصول على تصنيف دقيق وملخص شامل.</p>', unsafe_allow_html=True)


def main_page():
    st.title("صنف ولخص")

    # Input text area
    input_text = st.text_area("ادخل النص", "")

    if st.button('صنف ولخص'):
        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)

            st.markdown('<p class="larger-text">تصنيف القضية :</p>', unsafe_allow_html=True)
            st.write(classifiy_class)

            st.markdown('<p class="larger-text">ملخص للقضية :</p>', unsafe_allow_html=True)
            st.write(summarized_text)

def app():
    # Sidebar navigation with logo inside the sidebar
    with st.sidebar:
        st.markdown('<div class="logo-container">', unsafe_allow_html=True)
        st.image("logo.png", width=200)  # Make sure you have the logo file in your app folder
        st.markdown('</div>', unsafe_allow_html=True)
        
        st.header("تطييق وجيز")
        page_selection = st.selectbox("اختر صفحة", ["الرئيسية", " صنف ولخص !"])

    if page_selection == "الرئيسية":
        home_page()
    elif page_selection == " صنف ولخص !":
        main_page()

if __name__ == "__main__":
    app()