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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() |