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| import sentencepiece as spm | |
| import numpy as np | |
| import tensorflow as tf | |
| from tensorflow.keras.preprocessing.sequence import pad_sequences | |
| from valx import detect_profanity, detect_hate_speech | |
| import gradio as gr | |
| sp = spm.SentencePieceProcessor() | |
| sp.Load("dungen_dev_preview.model") | |
| model = tf.keras.models.load_model("dungen_dev_preview_model.keras") | |
| max_seq_len = 25 | |
| def generate_text(seed_text, next_words=30, temperature=0.5): | |
| seed_text = seed_text.lower() + ' | ' | |
| hate_speech = detect_hate_speech(seed_text) | |
| profanity = detect_profanity([seed_text], language='All') | |
| if profanity: | |
| gr.Warning("Profanity detected in the prompt, using the default prompt.") | |
| seed_text = 'game name | ' | |
| elif hate_speech and hate_speech[0] in ['Hate Speech', 'Offensive Speech']: | |
| gr.Warning('Harmful speech detected in the seed text, using default prompt.') | |
| seed_text = 'game name | ' | |
| generated_text = seed_text | |
| for _ in range(next_words): | |
| token_list = sp.encode_as_ids(generated_text) | |
| token_list = pad_sequences([token_list], maxlen=max_seq_len - 1, padding='pre') | |
| predicted = model.predict(token_list, verbose=0)[0] | |
| predicted = np.asarray(predicted).astype("float64") | |
| predicted = np.log(predicted + 1e-8) / temperature | |
| exp_preds = np.exp(predicted) | |
| predicted = exp_preds / np.sum(exp_preds) | |
| next_index = np.random.choice(len(predicted), p=predicted) | |
| next_token = sp.id_to_piece(next_index) | |
| generated_text += next_token | |
| if next_token.endswith('</s>') or next_token.endswith('<unk>'): | |
| break | |
| decoded = sp.decode_pieces(sp.encode_as_pieces(generated_text)) | |
| decoded = decoded.replace("</s>", "").replace("<unk>", "").strip() | |
| # Remove the prompt from the generated text | |
| if '|' in decoded: | |
| decoded = decoded.split('|', 1)[1].strip() #Split at the first occurence of '|' and take the second part | |
| hate_speech2 = detect_hate_speech(decoded) | |
| profanity2 = detect_profanity([decoded], language='All') | |
| if profanity2 or (hate_speech2 and hate_speech2[0] in ['Hate Speech', 'Offensive Speech']): | |
| gr.Warning("Flagged potentially harmful output.") | |
| decoded = 'Flagged Output' | |
| return decoded | |
| demo = gr.Interface( | |
| fn=generate_text, | |
| inputs=[ | |
| gr.Textbox(label="Prompt", value="a female character name", max_lines=1), | |
| gr.Slider(1, 50, step=1, label='Next Words', value=30), | |
| gr.Slider(0.1, 1, value=0.5, label='Temperature', info='Controls randomness of generation, higher values = more creative, lower values = more probalistic') | |
| ], | |
| outputs=gr.Textbox(label="Generated Names"), | |
| title='Dungen Dev - Name Generator', | |
| description='A prompt-based name generator for game developers.', | |
| examples=[ | |
| ["a male elf name", 30, 0.5], | |
| ["a futuristic city name", 30, 0.5], | |
| ["a powerful magic item name", 30, 0.5], | |
| ["a dark and mysterious forest name", 30, 0.5], | |
| ["a female character name", 30, 0.5] | |
| ] | |
| ) | |
| demo.launch() |