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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() |