# -*- 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()