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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.strip().lower() | |
| # if "|" in seed_text: | |
| # gr.Warning("The prompt should not contain the '|' character. Using default prompt.") | |
| # seed_text = 'game name | ' | |
| # elif detect_profanity([seed_text], language='All'): | |
| # gr.Warning("Profanity detected in the prompt, using the default prompt.") | |
| # seed_text = 'game name | ' | |
| # elif (hate_speech_result := detect_hate_speech(seed_text)) and hate_speech_result[0] in ['Hate Speech', 'Offensive Speech']: | |
| # gr.Warning('Harmful speech detected in the prompt, using default prompt.') | |
| # seed_text = 'game name | ' | |
| # else: | |
| # seed_text += ' | ' | |
| # generated_text = seed_text | |
| # if generated_text != 'game name | ': # only generate if not the default prompt | |
| # 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() | |
| # if '|' in decoded: | |
| # decoded = decoded.split('|', 1)[1].strip() | |
| # if any(detect_profanity([decoded], language='All')) or (hate_speech_result := detect_hate_speech(decoded)) and hate_speech_result[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, 100, 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. Dungen Dev is an experimental model, and may produce outputs that are inappropriate, biased, or potentially harmful and inaccurate. Caution is advised.', | |
| # examples=[ | |
| # ["a male character name", 30, 0.5], | |
| # ["a futuristic city name", 30, 0.5], | |
| # ["an item name", 30, 0.5], | |
| # ["a dark and mysterious forest name", 30, 0.5], | |
| # ["an evil character name", 30, 0.5] | |
| # ] | |
| # ) | |
| # demo.launch() | |
| 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 | |
| import csv | |
| from datetime import datetime | |
| 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.strip().lower() | |
| if "|" in seed_text: | |
| gr.Warning("The prompt should not contain the '|' character. Using default prompt.") | |
| seed_text = 'game name | ' | |
| elif detect_profanity([seed_text], language='All'): | |
| gr.Warning("Profanity detected in the prompt, using the default prompt.") | |
| seed_text = 'game name | ' | |
| elif (hate_speech_result := detect_hate_speech(seed_text)) and hate_speech_result[0] in ['Hate Speech', 'Offensive Speech']: | |
| gr.Warning('Harmful speech detected in the prompt, using default prompt.') | |
| seed_text = 'game name | ' | |
| else: | |
| seed_text += ' | ' | |
| generated_text = seed_text | |
| if generated_text != 'game name | ': # only generate if not the default prompt | |
| 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() | |
| if '|' in decoded: | |
| decoded = decoded.split('|', 1)[1].strip() | |
| if any(detect_profanity([decoded], language='All')) or (hate_speech_result := detect_hate_speech(decoded)) and hate_speech_result[0] in ['Hate Speech', 'Offensive Speech']: | |
| gr.Warning("Flagged potentially harmful output.") | |
| decoded = 'Flagged Output' | |
| return decoded | |
| flagged_outputs = [] | |
| def flag_output(prompt, generated_text, next_words, temperature): | |
| if not generated_text.strip(): | |
| return "Cannot flag an empty output." | |
| timestamp = datetime.now().isoformat() | |
| flagged_outputs.append({ | |
| "Prompt": prompt, | |
| "Generated Text": generated_text, | |
| "Next Words": next_words, | |
| "Temperature": temperature, | |
| "Timestamp": timestamp | |
| }) | |
| with open("flagged_outputs.csv", "a", newline="") as file: | |
| writer = csv.DictWriter(file, fieldnames=["Prompt", "Generated Text", "Next Words", "Temperature", "Timestamp"]) | |
| if file.tell() == 0: | |
| writer.writeheader() | |
| writer.writerow({ | |
| "Prompt": prompt, | |
| "Generated Text": generated_text, | |
| "Next Words": next_words, | |
| "Temperature": temperature, | |
| "Timestamp": timestamp | |
| }) | |
| return "Output flagged successfully." | |
| demo = gr.Interface( | |
| fn=generate_text, | |
| inputs=[ | |
| gr.Textbox(label="Prompt", value="a female character name", max_lines=1), | |
| gr.Slider(1, 100, 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 probabilistic') | |
| ], | |
| outputs=[ | |
| gr.Textbox(label="Generated Name"), | |
| gr.Button("Flag Output") | |
| ], | |
| title='Dungen Dev - Name Generator', | |
| description='A prompt-based name generator for game developers. Dungen Dev is an experimental model, and may produce outputs that are inappropriate, biased, or potentially harmful and inaccurate. Caution is advised.', | |
| examples=[ | |
| ["a male character name", 30, 0.5], | |
| ["a futuristic city name", 30, 0.5], | |
| ["an item name", 30, 0.5], | |
| ["a dark and mysterious forest name", 30, 0.5], | |
| ["an evil character name", 30, 0.5] | |
| ] | |
| ) | |
| demo.launch() |