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# -*- coding: utf-8 -*-
"""Copy of russian model testing.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1c9k49wiWEvDa1zxIw65pUAsuzMlFn-tq
"""
'''test'''
#!pip install gradio
#!pip install translate
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import nltk
from nltk.translate.bleu_score import sentence_bleu
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('punkt')
url = 'https://raw.githubusercontent.com/Obai33/NLP_PoemGenerationDatasets/main/russianpoems.csv'
text_data = pd.read_csv(url)
# removing duplicates and missing values
text_data.drop_duplicates(inplace = True)
text_data.dropna(inplace = True)
text_data
text_data = text_data['text']
text_data = text_data[500:700]
# Tokenization and lowercasing
tokenizer = Tokenizer()
tokenizer.fit_on_texts(text_data)
total_words = len(tokenizer.word_index) + 1
input_sequences = []
for line in text_data:
token_list = tokenizer.texts_to_sequences([line])[0]
for i in range(1, len(token_list)):
n_gram_sequence = token_list[:i+1]
input_sequences.append(n_gram_sequence)
# pad sequences
max_sequence_len = 100
input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre'))
# create predictors and label
xs, labels = input_sequences[:,:-1],input_sequences[:,-1]
ys = tf.keras.utils.to_categorical(labels, num_classes=total_words)
import requests
# URL of the model
url = 'https://github.com/Obai33/NLP_PoemGenerationDatasets/raw/main/modelrus1.h5'
# Local file path to save the model
local_filename = 'modelrus1.h5'
# Download the model file
response = requests.get(url)
with open(local_filename, 'wb') as f:
f.write(response.content)
# Load the pre-trained model
model = tf.keras.models.load_model(local_filename)
# Import the necessary library for translation
import translate
# Function to translate text to English
def translate_to_english(text):
translator = translate.Translator(from_lang="ru", to_lang="en")
translated_text = translator.translate(text)
return translated_text
def generate_russian_text(seed_text, next_words=50):
generated_text = seed_text
for _ in range(next_words):
token_list = tokenizer.texts_to_sequences([generated_text])[0]
token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
predicted = np.argmax(model.predict(token_list), axis=-1)
output_word = ""
for word, index in tokenizer.word_index.items():
if index == predicted:
output_word = word
break
generated_text += " " + output_word
'''
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(generated_text)
return generated_text, t_text
import gradio as gr
# Update Gradio interface to include both Arabic and English outputs
iface = gr.Interface(
fn=generate_russian_text,
inputs="text",
outputs=["text", "text"],
title="Russian Poetry Generation",
description="Enter Russian text to generate a small poem.",
theme="compact"
)
# Run the interface
iface.launch()