app.py

# -*- coding: utf-8 -*-
"""ArabicPoetryGeneration.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1HDyT5F8qnrbR_PW_HYpiM3O-7i6htGG2
"""
'''
pip install transformers
pip install tashaphyne
pip install gradio
pip install translate
'''

import pandas as pd
import nltk
from nltk.tokenize import word_tokenize
from transformers import BertTokenizer
from transformers import AutoTokenizer
import random
from tashaphyne import normalize
import re
import numpy as np
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional, GRU
import tensorflow as tf
from transformers import AutoTokenizer

nltk.download('punkt')
nltk.download('wordnet')

aurl = 'https://raw.githubusercontent.com/Obai33/NLP_PoemGenerationDatasets/main/arabicpoems.csv'
adf = pd.read_csv(aurl)

# Function to normalize text
def normalize_text(text):
    normalize.strip_tashkeel(text)
    normalize.strip_tatweel(text)
    normalize.normalize_hamza(text)
    normalize.normalize_lamalef(text)
    return text

# Normalize the text
allah = normalize_text('الله')
adf = adf['poem_text']
i = random.randint(0, len(adf))
adf = adf.sample(n=100, random_state=i)
adf = adf.apply(lambda x: normalize_text(x))
adf = adf[~adf.str.contains(allah)]

# Function to clean text
def remove_non_arabic_symbols(text):
    arabic_pattern = r'[\u0600-\u06FF\s]+'
    arabic_text = re.findall(arabic_pattern, text)
    cleaned_text = ''.join(arabic_text)
    return cleaned_text

# Clean the text
adf = adf.apply(lambda x: remove_non_arabic_symbols(x))

# Tokenize the text
tokenizer = AutoTokenizer.from_pretrained("aubmindlab/bert-base-arabertv2")
tokens = tokenizer.tokenize(adf.tolist(), is_split_into_words=True)

input_sequences = []
for line in adf:
    token_list = tokenizer.encode(line, add_special_tokens=True)
    for i in range(1, len(token_list)):
        n_gram_sequence = token_list[:i+1]
        input_sequences.append(n_gram_sequence)

max_sequence_len = 100
input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre'))

total_words = tokenizer.vocab_size

xs, labels = input_sequences[:, :-1], input_sequences[:, -1]
ys = tf.keras.utils.to_categorical(labels, num_classes=total_words)

print('error not here')

##############

import requests

# URL of the model
url = 'https://github.com/Obai33/NLP_PoemGenerationDatasets/raw/main/modelarab1.h5'
# Local file path to save the model
local_filename = 'modelarab1.h5'

# Download the model file
response = requests.get(url)
with open(local_filename, 'wb') as f:
    f.write(response.content)

print('error not here')

# Load the pre-trained model
model = tf.keras.models.load_model(local_filename)
print('error not here')

##############

# Import the necessary library for translation
import translate

# Function to translate text to English
def translate_to_english(text):
    translator = translate.Translator(from_lang="ar", to_lang="en")
    translated_text = translator.translate(text)
    return translated_text

def generate_arabic_text(seed_text, next_words=50):
    generated_text = seed_text
    for _ in range(next_words):
        token_list = tokenizer.encode(generated_text, add_special_tokens=False)
        token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
        predicted = np.argmax(model.predict(token_list), axis=-1)
        output_word = tokenizer.decode(predicted[0])
        generated_text += " " + output_word
    reconnected_text = generated_text.replace(" ##", "")
    t_text = translate_to_english(reconnected_text)
    return reconnected_text, t_text

import gradio as gr
print('error not here')

# Update Gradio interface to include both Arabic and English outputs
iface = gr.Interface(
    fn=generate_arabic_text,
    inputs="text",
    outputs=["text", "text"],
    title="Arabic Poetry Generation",
    description="Enter Arabic text to generate a small poem.",
    theme="compact"
)
# Run the interface
iface.launch()