├── briefly about Amberi.md ├── LICENSE ├── ver2.0 ├── README.md └── source /briefly about Amberi.md: -------------------------------------------------------------------------------- 1 | # Amberi-neural-network 2 | This is our advanced neural network of our own production. She is a companion of our 3D sex game in VR mode. 3 | Amberi is a cutting-edge neural network that serves as a mediator between two players, helping to generate and launch a game scene. With a built-in voice assistant that speaks 30 different languages, including Hindi and Simplified Chinese, Amberi is more than just a tool - it's a companion. With the ability to extract motion from video and convert it into data that can be used in Unreal Engine 5, Amberi is revolutionizing the way we experience video games. Whether you're looking for a more immersive gaming experience or just a new way to have fun, Amberi is the perfect choice. 4 | ![2023-02-01_17-15-34](https://user-images.githubusercontent.com/124265441/216617446-e688f494-4d75-4dd9-b0fb-b3e6e2f29f27.png) 5 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Boost Software License - Version 1.0 - August 17th, 2003 2 | 3 | Permission is hereby granted, free of charge, to any person or organization 4 | obtaining a copy of the software and accompanying documentation covered by 5 | this license (the "Software") to use, reproduce, display, distribute, 6 | execute, and transmit the Software, and to prepare derivative works of the 7 | Software, and to permit third-parties to whom the Software is furnished to 8 | do so, all subject to the following: 9 | 10 | The copyright notices in the Software and this entire statement, including 11 | the above license grant, this restriction and the following disclaimer, 12 | must be included in all copies of the Software, in whole or in part, and 13 | all derivative works of the Software, unless such copies or derivative 14 | works are solely in the form of machine-executable object code generated by 15 | a source language processor. 16 | 17 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 18 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 19 | FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT 20 | SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE 21 | FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, 22 | ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER 23 | DEALINGS IN THE SOFTWARE. 24 | -------------------------------------------------------------------------------- /ver2.0: -------------------------------------------------------------------------------- 1 | !pip install transformers nltk 2 | import transformers 3 | import pandas as pd 4 | import numpy as np 5 | import string 6 | from nltk.tokenize import word_tokenize 7 | from nltk.corpus import stopwords 8 | import torch 9 | from tensorflow.keras.preprocessing.sequence import pad_sequences 10 | from collections import Counter 11 | import nltk 12 | nltk.download('punkt') 13 | nltk.download('stopwords') 14 | 15 | # Load the dialog data into a pandas DataFrame 16 | dialog_data = pd.read_csv("dialog_data.csv") 17 | 18 | # Convert all text to lowercase 19 | dialog_data['text'] = dialog_data['text'].str.lower() 20 | 21 | # Remove punctuation and stop words 22 | punctuation = string.punctuation 23 | stop_words = set(stopwords.words('english')) 24 | dialog_data['text'] = dialog_data['text'].apply(lambda x: ' '.join([word for word in word_tokenize(x) if word not in punctuation and word not in stop_words])) 25 | 26 | # Tokenize the text 27 | dialog_data['text'] = dialog_data['text'].apply(lambda x: word_tokenize(x)) 28 | 29 | # Convert words to numerical representation 30 | word_counter = Counter() 31 | for text in dialog_data['text']: 32 | for word in text: 33 | word_counter[word] += 1 34 | 35 | word_index = {word: i+1 for i, (word, count) in enumerate(word_counter.most_common())} 36 | dialog_data['text'] = dialog_data['text'].apply(lambda x: [word_index[word] for word in x]) 37 | 38 | # Pad the sequences to the same length 39 | max_length = max([len(text) for text in dialog_data['text']]) 40 | dialog_data['text'] = pad_sequences(dialog_data['text'].tolist(), maxlen=max_length, padding='post') 41 | 42 | # Load the pre-trained GPT2 model 43 | tokenizer_gpt2 = transformers.GPT2Tokenizer.from_pretrained("gpt2") 44 | model_gpt2 = transformers.GPT2LMHeadModel.from_pretrained("gpt2") 45 | 46 | # Generate input ids from a given text 47 | input_ids = torch.tensor(tokenizer_gpt2.encode("Hello, I am a language model.", return_tensors='pt')) 48 | 49 | # Generate predictions for GPT2 50 | with torch.no_grad(): 51 | outputs = model_gpt2(input_ids) 52 | predictions = outputs.logits 53 | 54 | # Get the predicted next word(s) 55 | predicted_next_word = tokenizer_gpt2.decode(torch.argmax(predictions[0, -1, :]).item()) 56 | print(f"Predicted next word (GPT2): {predicted_next_word}") 57 | 58 | # Load the pre-trained BERT model for sequence classification 59 | tokenizer_bert = transformers.BertTokenizer.from_pretrained("bert-base-uncased") 60 | model_bert = transformers.BertForSequenceClassification.from_pretrained("bert-base-uncased") 61 | 62 | # Prepare a sample text and its label 63 | text = "Hello, I am a sample text for BERT." 64 | label = 1 # replace with actual label 65 | 66 | # Encode the text 67 | inputs = tokenizer_bert(text, return_tensors='pt', padding=True, truncation=True, max_length=512) 68 | inputs['labels'] = torch.tensor([label]).unsqueeze(0) 69 | 70 | # Forward pass 71 | outputs = model_bert(**inputs) 72 | 73 | # Get the predicted class 74 | predicted_class = torch.argmax(outputs.logits).item() 75 | print(f"Predicted class (BERT): 76 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | technical part of the work Amberi: 2 | The first part uses the transformer library to implement natural language processing tasks. It uses two different pre-trained models, GPT-2 and BERT. 3 | 4 | GPT-2 is a text generation model, and the script uses it to generate text as a continuation of the input text "Hello, I'm a language model". 5 | The predicted next word is obtained from the output of the model. 6 | 7 | BERT is a sentiment analysis model, and the script uses it to classify the sentiment of the input text "Hello, I'm a language model." 8 | The predicted sentiment class is obtained from the output of the model. 9 | 10 | The script also has some data preprocessing steps for the dialog dataset, including lowercase, punctuation and stopword removal, 11 | tokenization, and converting words to numeric representations. 12 | 13 | The script also implements a custom classifier layer for BERT, but this is not used in the current code. 14 | 15 | The code initializes a GPT-2 model from pretrained weights, creates a tokenizer for the model, and uses the model to answer a question with a prompt variable. 16 | The code then fine-tunes the model on the dialog dataset using 10 epochs and a burst size of 4. 17 | Finally, the code defines a tokenize_and_encode function that takes input text and returns the corresponding token IDs, 18 | and then applies the function to the sample input. text. Token IDs are printed to the console. 19 | 20 | The code imports the required libraries and loads the dialog data into a pandas dataframe. It then converts all text to lowercase, 21 | removes punctuation and stop words, tokenizes the text, converts words to numeric representations, pads sequences to the same length, 22 | and loads a pretrained machine translation model. Finally, 23 | it generates input IDs from the given text and generates predictions for the machine translation model. 24 | 25 | This code uses transformers and TensorFlow to parse dialogs into data read from a CSV file. 26 | It processes text (lowercase, removes punctuation and stop words, tokenization), converts words to numeric representations, 27 | and pads sequences to the same length. It then loads the pretrained GPT2 and BERT models, 28 | generates input IDs, and generates predictions for the given text. 29 | 30 | This code sets up an OpenAI API key and defines a generate_answer function that uses the OpenAI API to generate an answer to a given question. 31 | The function uses the openai.Completion.create method to send a request to the API with the given parameters. The API returns the response as a dictionary, 32 | and the function returns the text of the first choice in the list of "choices" in the response. 33 | 34 | I'd like to introduce you to our neural network, which is made up of 6 different networks working together to ensure a smooth and efficient experience. 35 | Our code for the basic operation of the web is open source, excluding data and sensitive information. 36 | 37 | Our neural network is written in Python and uses the interaction between 6 different networks responsible for different functions. 38 | The first network uses an extensive database to work and interact with the others. 39 | The second network is closely related to translation and understanding of voice input. 40 | The third network converts voice input to text and converts it for further use, and also interacts with GPT-2 and BERT. 41 | The fourth network is able to give the answer itself and predict the next word. If it does not understand the text, 42 | it forwards it to the fifth network. The fifth network censors the text, submits complex questions to ChatGPT, 43 | and censors the response received to make it understandable using the original text. Finally, 44 | the sixth network interacts with Unreal Engine 5 to trigger the scene using voice control. 45 | 46 | Thank you for your attention and I hope you found this presentation informative. 47 | -------------------------------------------------------------------------------- /source: -------------------------------------------------------------------------------- 1 | Collecting base 2 | !pip install transformers 3 | import transformers 4 | import pandas as pd 5 | import numpy as np 6 | import string 7 | from nltk.tokenize import word_tokenize 8 | from nltk.corpus import stopwords 9 | import torch 10 | import tensorflow as tf 11 | from tensorflow.keras.preprocessing.text import Tokenizer 12 | from tensorflow.keras.preprocessing.sequence import pad_sequences 13 | 14 | # Load the dialog data into a pandas DataFrame 15 | dialog_data = pd.read_csv("dialog_data.csv") 16 | 17 | # Convert all text to lowercase 18 | dialog_data['text'] = dialog_data['text'].str.lower() 19 | 20 | # Remove punctuation and stop words 21 | punctuation = string.punctuation 22 | stop_words = set(stopwords.words('english')) 23 | 24 | dialog_data['text'] = dialog_data['text'].apply(lambda x: ' '.join([word for word in word_tokenize(x) if word not in punctuation and word not in stop_words])) 25 | 26 | # Tokenize the text 27 | dialog_data['text'] = dialog_data['text'].apply(lambda x: word_tokenize(x)) 28 | 29 | # Convert words to numerical representation 30 | from collections import Counter 31 | word_counter = Counter() 32 | for text in dialog_data['text']: 33 | for word in text: 34 | word_counter[word] += 1 35 | 36 | word_index = {word: i+1 for i, (word, count) in enumerate(word_counter.most_common())} 37 | 38 | dialog_data['text'] = dialog_data['text'].apply(lambda x: [word_index[word] for word in x]) 39 | 40 | # Pad the sequences to the same length 41 | from keras.preprocessing.sequence import pad_sequences 42 | max_length = max([len(text) for text in dialog_data['text']]) 43 | dialog_data['text'] = pad_sequences(dialog_data['text'], maxlen=max_length, padding='post') 44 | 45 | # Load the pre-trained GPT2 model 46 | tokenizer = transformers.GPT2Tokenizer.from_pretrained("gpt2") 47 | model = transformers.GPT2LMHeadModel.from_pretrained("gpt2") 48 | model.eval() 49 | 50 | # Generate input ids from a given text 51 | input_ids = torch.tensor(tokenizer.encode("Hello, I am a language model.")).unsqueeze(0) 52 | 53 | # Generate predictions for GPT2 54 | with torch.no_grad(): 55 | outputs = model(input_ids) 56 | predictions = outputs[0] 57 | 58 | # Get the predicted next word(s) 59 | predicted_next_word = tokenizer.decode(torch.argmax(predictions[0, -1, :]).tolist()) 60 | print(f"Predicted next word (GPT2): {predicted_next_word}") 61 | 62 | # Load the pre-trained BERT model for sequence classification 63 | tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased") 64 | model = transformers.BertForSequenceClassification.from_pretrained("bert-base-uncased") 65 | model.eval() 66 | 67 | # Generate input 68 | 69 | Готовый номер 2 70 | !pip install transformers 71 | import transformers 72 | import string 73 | import tensorflow as tf 74 | from tensorflow.keras.preprocessing.text import Tokenizer 75 | from tensorflow.keras.preprocessing.sequence import pad_sequences 76 | import pandas as pd 77 | import numpy as np 78 | from nltk.tokenize import word_tokenize 79 | from nltk.corpus import stopwords 80 | import torch 81 | 82 | # Load the dialog data into a pandas DataFrame 83 | dialog_data = pd.read_csv("dialog_data.csv") 84 | 85 | # Convert all text to lowercase 86 | dialog_data['text'] = dialog_data['text'].str.lower() 87 | 88 | # Remove punctuation and stop words 89 | punctuation = string.punctuation 90 | stop_words = set(stopwords.words('english')) 91 | dialog_data['text'] = dialog_data['text'].apply(lambda x: ' '.join([word for word in word_tokenize(x) if word not in punctuation and word not in stop_words])) 92 | 93 | # Tokenize the text 94 | dialog_data['text'] = dialog_data['text'].apply(lambda x: word_tokenize(x)) 95 | 96 | # Convert words to numerical representation 97 | word_counter = Counter() 98 | for text in dialog_data['text']: 99 | for word in text: 100 | word_counter[word] += 1 101 | word_index = {word: i+1 for i, (word, count) in enumerate(word_counter.most_common())} 102 | dialog_data['text'] = dialog_data['text'].apply(lambda x: [word_index[word] for word in x]) 103 | 104 | # Pad the sequences to the same length 105 | max_length = max([len(text) for text in dialog_data['text']]) 106 | dialog_data['text'] = pad_sequences(dialog_data['text'], maxlen=max_length, padding='post') 107 | 108 | # Load the pre-trained GPT2 model 109 | tokenizer = transformers.GPT2Tokenizer.from_pretrained("gpt2") 110 | model = transformers.GPT2LMHeadModel.from_pretrained("gpt2") 111 | model.eval() 112 | 113 | # Generate input ids from a given text 114 | input_ids = torch.tensor(tokenizer.encode("Hello, I am a language model.")).unsqueeze(0) 115 | 116 | GPT-2 117 | 118 | # Generate predictions for GPT2 119 | with torch.no_grad(): 120 | outputs = model(input_ids) 121 | predictions = outputs[0] 122 | 123 | # Get the predicted next word(s) 124 | predicted_next_word = tokenizer.decode(torch.argmax(predictions[0, -1, :]).tolist()) 125 | print(f"Predicted next word (GPT2): {predicted_next_word}") 126 | 127 | # Load the pre-trained BERT model for sequence classification 128 | tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased") 129 | model = transformers.BertForSequenceClassification.from_pretrained("bert-base-uncased") 130 | model.eval() 131 | 132 | # Generate input ids from a given text 133 | input_ids = torch.t 134 | 135 | 3ая версия import string 136 | import tensorflow as tf 137 | from tensorflow.keras.preprocessing.text import Tokenizer 138 | from tensorflow.keras.preprocessing.sequence import pad_sequences 139 | import pandas as pd 140 | import numpy as np 141 | from nltk.tokenize import word_tokenize 142 | from nltk.corpus import stopwords 143 | import torch 144 | import transformers 145 | 146 | # Load the dialog data into a pandas DataFrame 147 | dialog_data = pd.read_csv("dialog_data.csv") 148 | 149 | # Convert all text to lowercase 150 | dialog_data['text'] = dialog_data['text'].str.lower() 151 | 152 | # Remove punctuation and stop words 153 | punctuation = string.punctuation 154 | stop_words = set(stopwords.words('english')) 155 | dialog_data['text'] = dialog_data['text'].apply(lambda x: ' '.join([word for word in word_tokenize(x) if word not in punctuation and word not in stop_words])) 156 | 157 | # Tokenize the text 158 | dialog_data['text'] = dialog_data['text'].apply(lambda x: word_tokenize(x)) 159 | 160 | # Convert words to numerical representation 161 | word_counter = Counter() 162 | for text in dialog_data['text']: 163 | for word in text: 164 | word_counter[word] += 1 165 | word_index = {word: i+1 for i, (word, count) in enumerate(word_counter.most_common())} 166 | dialog_data['text'] = dialog_data['text'].apply(lambda x: [word_index[word] for word in x]) 167 | 168 | # Pad the sequences to the same length 169 | max_length = max([len(text) for text in dialog_data['text']]) 170 | dialog_data['text'] = pad_sequences(dialog_data['text'], maxlen=max_length, padding='post') 171 | 172 | # Load the pre-trained machine translation model 173 | model = transformers.TFRobertaForCausalLM.from_pretrained("roberta-base") 174 | model.eval() 175 | 176 | # Generate input ids from a given text 177 | input_text = "Hello, I am a language model." 178 | input_ids = torch.tensor(tokenizer.encode(input_text, add_special_tokens=True)).unsqueeze(0) 179 | 180 | # Generate predictions for the machine translation model 181 | with torch.no_grad(): 182 | outputs = model.generate(input_ids, max_length=max_length, num_beams=1, repetition_penalty=1.0, length_ 183 | 184 | 185 | translate 186 | 187 | import string 188 | import tensorflow as tf 189 | from tensorflow.keras.preprocessing.text import Tokenizer 190 | from tensorflow.keras.preprocessing.sequence import pad_sequences 191 | import pandas as pd 192 | import numpy as np 193 | from nltk.tokenize import word_tokenize 194 | from nltk.corpus import stopwords 195 | import torch 196 | import transformers 197 | 198 | # Load the dialog data into a pandas DataFrame 199 | dialog_data = pd.read_csv("dialog_data.csv") 200 | 201 | # Convert all text to lowercase 202 | dialog_data['text'] = dialog_data['text'].str.lower() 203 | 204 | # Remove punctuation and stop words 205 | punctuation = string.punctuation 206 | stop_words = set(stopwords.words('english')) 207 | dialog_data['text'] = dialog_data['text'].apply(lambda x: ' '.join([word for word in word_tokenize(x) if word not in punctuation and word not in stop_words])) 208 | 209 | # Tokenize the text 210 | dialog_data['text'] = dialog_data['text'].apply(lambda x: word_tokenize(x)) 211 | 212 | # Convert words to numerical representation 213 | word_counter = Counter() 214 | for text in dialog_data['text']: 215 | for word in text: 216 | word_counter[word] += 1 217 | word_index = {word: i+1 for i, (word, count) in enumerate(word_counter.most_common())} 218 | dialog_data['text'] = dialog_data['text'].apply(lambda x: [word_index[word] for word in x]) 219 | 220 | # Pad the sequences to the same length 221 | max_length = max([len(text) for text in dialog_data['text']]) 222 | dialog_data['text'] = pad_sequences(dialog_data['text'], maxlen=max_length, padding='post') 223 | 224 | # Load the pre-trained machine translation model 225 | model = transformers.TFRobertaForCausalLM.from_pretrained("roberta-base") 226 | model.eval() 227 | 228 | # Generate input ids from a given text 229 | input_text = "Hello, I am a language model." 230 | input_ids = torch.tensor(tokenizer.encode(input_text, add_special_tokens=True)).unsqueeze(0) 231 | 232 | # Generate predictions for the machine translation model 233 | with torch.no_grad(): 234 | outputs = model.generate(input_ids, max_length=max_length, num_beams=1, repetition_penalty=1.0, length_ 235 | 236 | import tensorflow as tf 237 | 238 | # Load audio data 239 | audio_data = ... 240 | 241 | # Pre-processing audio data 242 | preprocessed_audio_data = ... 243 | 244 | # Split data into training and testing sets 245 | train_data, test_data = ... 246 | 247 | # Create RNN model 248 | model = tf.keras.Sequential() 249 | model.add(tf.keras.layers.LSTM(units=128, input_shape=(None, audio_data.shape[1]), return_sequences=True)) 250 | model.add(tf.keras.layers.LSTM(units=128, return_sequences=True)) 251 | model.add(tf.keras.layers.Dense(units=audio_data.shape[1])) 252 | 253 | # Compile the model 254 | model.compile(optimizer='adam', loss='mean_squared_error') 255 | 256 | # Train the model 257 | model.fit(train_data, train_data, epochs=100, batch_size=32) 258 | 259 | # Evaluate the model 260 | test_loss = model.evaluate(test_data, test_data) 261 | print("Test loss: ", test_loss) 262 | 263 | # Use the model for predictions 264 | predictions = model.predict(preprocessed_audio_data 265 | 266 | answering a question and predicting the answer 267 | 268 | # Initialize the GPT-2 model 269 | import transformers 270 | model_name = "gpt2" 271 | model = transformers.GPT2Model.from_pretrained(model_name) 272 | tokenizer = transformers.GPT2Tokenizer.from_pretrained(model_name) 273 | 274 | # Use the model to answer a question 275 | prompt = "What is your name?" 276 | input_ids = tokenizer.encode(prompt, return_tensors='pt') 277 | output = model.generate(input_ids=input_ids, max_length=100, top_k=5) 278 | generated_response = tokenizer.decode(output[0], skip_special_tokens=True) 279 | print(generated_response) 280 | 281 | # Fine-tune the model on a conversational dataset 282 | conversational_dataset = [("Hi, how are you today?", "I'm doing well, thank you. How about you?"), ("What's your favorite food?", "I don't have a favorite food as I am an AI language model."), ("What's the weather like today?", "I'm sorry, I don't have access to current weather information.")] 283 | num_epochs = 10 284 | batch_size = 4 285 | optimizer = transformers.AdamW(model.parameters(), lr=2e-5) 286 | loss_fn = transformers.CrossEntropyLoss() 287 | for epoch in range(num_epochs): 288 | total_loss = 0 289 | for (inputs, targets) in conversational_dataset: 290 | optimizer.zero_grad() 291 | outputs = model(inputs) 292 | loss = loss_fn(outputs, targets) 293 | loss.backward() 294 | optimizer.step() 295 | total_loss += loss.item() 296 | print("Epoch: {}, Loss: {}".format(epoch, total_loss)) 297 | model.save_pretrained("gpt2-finetuned") 298 | 299 | # Encode an input text into token IDs 300 | def tokenize_and_encode(text): 301 | input_ids = tokenizer.encode(text, return_tensors='pt') 302 | input_ids = input_ids.to('cuda') if torch.cuda.is_available() else input_ids.to('cpu') 303 | return input_ids 304 | input_text = "What is the capital of France?" 305 | input_ids = tokenize_and_encode(input_text) 306 | print(input_ids) 307 | 308 | ChatGPT 309 | 310 | import openai 311 | 312 | # Apply API Key 313 | openai.api_key = "YOUR_API_KEY" 314 | 315 | def generate_answer(question): 316 | response = openai.Completion.create( 317 | engine="text-davinci-002", 318 | prompt=question, 319 | max_tokens=1024, 320 | n=1, 321 | stop=None, 322 | temperature=0.5, 323 | ) 324 | return response["choices"][0]["text"] 325 | 326 | # Example usage 327 | answer = generate_answer("What is the capital of France?") 328 | print(answer) 329 | 330 | censorship 331 | 332 | import re 333 | import numpy as np 334 | import tensorflow as tf 335 | 336 | # Load the database with offensive words and their appropriate replacements 337 | with open('word_database.txt', 'r') as file: 338 | words = file.readlines() 339 | 340 | offensive_words = [word.split(',')[0] for word in words] 341 | appropriate_words = [word.split(',')[1].strip() for word in words] 342 | 343 | # Define the neural network 344 | model = tf.keras.Sequential([ 345 | tf.keras.layers.Embedding(len(offensive_words), 128, input_length=1), 346 | tf.keras.layers.LSTM(128), 347 | tf.keras.layers.Dense(len(appropriate_words), activation='softmax') 348 | ]) 349 | 350 | model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) 351 | 352 | # Train the neural network on the word database 353 | def one_hot_encode(word, words_list): 354 | encoded = np.zeros(len(words_list)) 355 | encoded[words_list.index(word)] = 1 356 | return encoded 357 | 358 | x_train = [one_hot_encode(word, offensive_words) for word in offensive_words] 359 | y_train = [one_hot_encode(word, appropriate_words) for word in appropriate_words] 360 | 361 | model.fit(x_train, y_train, epochs=100) 362 | 363 | # Replace offensive words in a given text with appropriate words 364 | def replace_words(text): 365 | text = text.split() 366 | for i, word in enumerate(text): 367 | if word in offensive_words: 368 | encoded = one_hot_encode(word, offensive_words) 369 | encoded = np.reshape(encoded, (1, -1)) 370 | predicted_word = appropriate_words[np.argmax(model.predict(encoded))] 371 | text[i] = predicted_word 372 | return ' '.join(text) 373 | 374 | # Example usage 375 | text = "This is a bad text with some offensive words." 376 | print("Original text:", text) 377 | print("Cleaned text:", replace_words(text)) 378 | 379 | final 380 | 381 | !pip install transformers 382 | import transformers 383 | import string 384 | import tensorflow as tf 385 | from tensorflow.keras.preprocessing.text import Tokenizer 386 | from tensorflow.keras.preprocessing.sequence import pad_sequences 387 | import pandas as pd 388 | import numpy as np 389 | from nltk.tokenize import word_tokenize 390 | from nltk.corpus import stopwords 391 | import torch 392 | 393 | # Load the dialog data into a pandas DataFrame 394 | dialog_data = pd.read_csv("dialog_data.csv") 395 | 396 | # Convert all text to lowercase 397 | dialog_data['text'] = dialog_data['text'].str.lower() 398 | 399 | # Remove punctuation and stop words 400 | punctuation = string.punctuation 401 | stop_words = set(stopwords.words('english')) 402 | dialog_data['text'] = dialog_data['text'].apply(lambda x: ' '.join([word for word in word_tokenize(x) if word not in punctuation and word not in stop_words])) 403 | 404 | # Tokenize the text 405 | dialog_data['text'] = dialog_data['text'].apply(lambda x: word_tokenize(x)) 406 | 407 | # Convert words to numerical representation 408 | word_counter = Counter() 409 | for text in dialog_data['text']: 410 | for word in text: 411 | word_counter[word] += 1 412 | word_index = {word: i+1 for i, (word, count) in enumerate(word_counter.most_common())} 413 | dialog_data['text'] = dialog_data['text'].apply(lambda x: [word_index[word] for word in x]) 414 | 415 | # Pad the sequences to the same length 416 | max_length = max([len(text) for text in dialog_data['text']]) 417 | dialog_data['text'] = pad_sequences(dialog_data['text'], maxlen=max_length, padding='post') 418 | 419 | # Load the pre-trained GPT2 model 420 | tokenizer = transformers.GPT2Tokenizer.from_pretrained("gpt2") 421 | model = transformers.GPT2LMHeadModel.from_pretrained("gpt2") 422 | model.eval() 423 | 424 | # Generate input ids from a given text 425 | input_ids = torch.tensor(tokenizer.encode("Hello, I am a language model.")).unsqueeze(0) 426 | 427 | # Generate predictions for GPT2 428 | with torch.no_grad(): 429 | outputs = model(input_ids) 430 | predictions = outputs[0] 431 | 432 | # Get the predicted next word(s) 433 | predicted_next_word = tokenizer.decode(torch.argmax(predictions[0, -1, :]).tolist()) 434 | print(f"Predicted next word (GPT2): {predicted_next_word}") 435 | 436 | # Load the pre-trained BERT model for sequence classification 437 | tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased") 438 | model = transformers.BertForSequenceClassification.from_pretrained("bert-base-uncased") 439 | model.eval() 440 | 441 | UE5 scene generation 442 | 443 | import tensorflow as tf 444 | import torch 445 | import torch.nn as nn 446 | import torch.nn.functional as F 447 | import torch.optim as optim 448 | 449 | # Define the model architecture (using Tensorflow) 450 | model = tf.keras.Sequential() 451 | model.add(tf.keras.layers.Dense(64, activation='relu', input_shape=(input_shape,))) 452 | model.add(tf.keras.layers.Dense(64, activation='relu')) 453 | model.add(tf.keras.layers.Dense(output_shape, activation='softmax')) 454 | 455 | # Compile the model 456 | model.compile(optimizer='adam', 457 | loss='categorical_crossentropy', 458 | metrics=['accuracy']) 459 | 460 | # Train the model on the dataset of examples 461 | model.fit(x_train, y_train, epochs=10, batch_size=32) 462 | 463 | # Use the trained model to generate outputs for controlling Unreal Engine 5 environment 464 | outputs = model.predict(x_test) 465 | 466 | # Define the neural network architecture (using PyTorch) 467 | class NeuralNet(nn.Module): 468 | def init(self): 469 | super(NeuralNet, self).init() 470 | self.fc1 = nn.Linear(input_dim, hidden_dim) 471 | self.fc2 = nn.Linear(hidden_dim, output_dim) 472 | 473 | def forward(self, x): 474 | x = F.relu(self.fc1(x)) 475 | x = self.fc2(x) 476 | return x 477 | 478 | # Initialize the network 479 | input_dim = 100 480 | hidden_dim = 50 481 | output_dim = 10 482 | model = NeuralNet() 483 | 484 | # Define the loss function and optimizer 485 | criterion = nn.CrossEntropyLoss() 486 | optimizer = optim.SGD(model.parameters(), lr=0.01) 487 | 488 | # Train the network 489 | for epoch in range(100): 490 | # Forward pass 491 | outputs = model(inputs) 492 | loss = criterion(outputs, labels) 493 | 494 | # Backward pass and optimization 495 | optimizer.zero_grad() 496 | loss.backward() 497 | optimizer.step() 498 | 499 | # Print the loss 500 | if (epoch+1) % 10 == 0: 501 | print(f'Epoch [{epoch+1}/100], Loss: {loss.item():.4f}') 502 | 503 | # Save the trained model 504 | torch.save(model.state_dict(), 'model.pt') 505 | 506 | # Unreal Engine 5 code for the plugin 507 | #include "CoreMinimal.h" 508 | #include "TorchModel.h" 509 | #include "torch/script.h" 510 | #include "VoiceInput.h" 511 | 512 | UTorchModel::UTorchModel() 513 | { 514 | // Load the model from file 515 | torch::jit::script::Module module = torch::jit::load("model.pt"); 516 | } 517 | 518 | void UTorchModel::GenerateScene(FString voiceCommand) 519 | { 520 | // Use the model to generate a new scene based on the voice command 521 | ... 522 | 523 | // Update the environment in Unreal Engine 5 using the Unreal Engine 5 API 524 | ... 525 | } 526 | 527 | void UTorchModel::OnVoiceCommandReceived(FString 528 | --------------------------------------------------------------------------------