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import os
import random
import gradio as gr
import sentencepiece as spm
import numpy as np
import pandas as pd
import tensorflow as tf
from valx import detect_profanity, detect_hate_speech
def custom_pad_sequences(sequences, maxlen, padding='pre', value=0):
"""
Pads sequences to the same length.
:param sequences: List of lists, where each element is a sequence.
:param maxlen: Maximum length of all sequences.
:param padding: 'pre' or 'post', pad either before or after each sequence.
:param value: Float, padding value.
:return: Numpy array with dimensions (number_of_sequences, maxlen)
"""
padded_sequences = np.full((len(sequences), maxlen), value)
for i, seq in enumerate(sequences):
if padding == 'pre':
if len(seq) <= maxlen:
padded_sequences[i, -len(seq):] = seq
else:
padded_sequences[i, :] = seq[-maxlen:]
elif padding == 'post':
if len(seq) <= maxlen:
padded_sequences[i, :len(seq)] = seq
else:
padded_sequences[i, :] = seq[:maxlen]
return padded_sequences
def generate_random_name(interpreter, vocab_size, sp, max_length=10, temperature=0.5, seed_text="", max_seq_len=12):
# Get input and output tensors
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
decoded_name = ''
if seed_text:
generated_name = seed_text
else:
random_index = np.random.randint(1, vocab_size)
random_token = sp.id_to_piece(random_index)
generated_name = random_token
for _ in range(max_length - 1):
token_list = sp.encode_as_ids(generated_name)
# Handle empty token list case
if len(token_list) == 0:
continue # Skip the current iteration if the token list is empty
# Pad to the correct length expected by the model
token_list = custom_pad_sequences([token_list], maxlen=max_seq_len, padding='pre')
# Convert token_list to FLOAT32 before setting the tensor
token_list = token_list.astype(np.float32)
# Set the input tensor
interpreter.set_tensor(input_details[0]['index'], token_list)
# Run inference
interpreter.invoke()
# Get the output tensor
predicted = interpreter.get_tensor(output_details[0]['index'])[0]
# Apply temperature to predictions
predicted = np.log(predicted + 1e-8) / temperature
predicted = np.exp(predicted) / np.sum(np.exp(predicted))
# Sample from the distribution
next_index = np.random.choice(range(vocab_size), p=predicted)
next_index = int(next_index)
next_token = sp.id_to_piece(next_index)
generated_name += next_token
# Decode the generated subword tokens into a string
decoded_name = sp.decode_pieces(generated_name.split())
# Stop if end token is predicted (optional)
if next_token == '' or len(decoded_name) > max_length:
break
decoded_name = decoded_name.replace("▁", " ")
decoded_name = decoded_name.replace("</s>", "")
decoded_name = decoded_name.replace("<unk>", "")
decoded_name = decoded_name.replace("<s>", "")
generated_name = decoded_name.rsplit(' ', 1)[0]
if generated_name:
generated_name = generated_name[0].upper() + generated_name[1:]
# Split the name and check the last part
parts = generated_name.split()
if parts and len(parts[-1]) < 3:
generated_name = " ".join(parts[:-1])
return generated_name.strip()
def generateNames(type, amount, max_length=30, temperature=0.5, seed_text=""):
hate_speech = detect_hate_speech(seed_text)
profanity = detect_profanity([seed_text], language='All')
output = ''
if len(profanity) > 0:
gr.Warning("Profanity detected in the seed text, using an empty seed text.")
seed_text = ''
else:
if hate_speech == ['Hate Speech']:
gr.Warning('Hate speech detected in the seed text, using an empty seed text.')
seed_text = ''
elif hate_speech == ['Offensive Speech']:
gr.Warning('Offensive speech detected in the seed text, using an empty seed text.')
seed_text = ''
# elif hate_speech == ['No Hate and Offensive Speech']:
if type == "Terraria":
max_seq_len = 12 # For skyrim = 13, for terraria = 12
sp = spm.SentencePieceProcessor()
sp.load("models/terraria_names.model")
amount = int(amount)
max_length = int(max_length)
names = []
# Define necessary variables
vocab_size = sp.GetPieceSize()
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="models/dungen_terraria_model.tflite")
interpreter.allocate_tensors()
# Use the function to generate a name
for _ in range(amount):
generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
stripped = generated_name.strip()
hate_speech = detect_hate_speech(stripped)
profanity = detect_profanity([stripped], language='All')
name = ''
if len(profanity) > 0:
name = "Profanity Detected"
else:
if hate_speech == ['Hate Speech']:
name = 'Hate Speech Detected'
elif hate_speech == ['Offensive Speech']:
name = 'Offensive Speech Detected'
elif hate_speech == ['No Hate and Offensive Speech']:
name = stripped
names.append(name)
return pd.DataFrame(names, columns=['Names'])
elif type == "Skyrim":
max_seq_len = 13 # For skyrim = 13, for terraria = 12
sp = spm.SentencePieceProcessor()
sp.load("models/skyrim_names.model")
amount = int(amount)
max_length = int(max_length)
names = []
# Define necessary variables
vocab_size = sp.GetPieceSize()
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="models/dungen_skyrim_model.tflite")
interpreter.allocate_tensors()
# Use the function to generate a name
for _ in range(amount):
generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
stripped = generated_name.strip()
hate_speech = detect_hate_speech(stripped)
profanity = detect_profanity([stripped], language='All')
name = ''
if len(profanity) > 0:
name = "Profanity Detected"
else:
if hate_speech == ['Hate Speech']:
name = 'Hate Speech Detected'
elif hate_speech == ['Offensive Speech']:
name = 'Offensive Speech Detected'
elif hate_speech == ['No Hate and Offensive Speech']:
name = stripped
names.append(name)
return pd.DataFrame(names, columns=['Names'])
elif type == "Witcher":
max_seq_len = 20 # For skyrim = 13, for terraria = 12
sp = spm.SentencePieceProcessor()
sp.load("models/witcher_names.model")
amount = int(amount)
max_length = int(max_length)
names = []
# Define necessary variables
vocab_size = sp.GetPieceSize()
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="models/dungen_witcher_model.tflite")
interpreter.allocate_tensors()
# Use the function to generate a name
for _ in range(amount):
generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
stripped = generated_name.strip()
hate_speech = detect_hate_speech(stripped)
profanity = detect_profanity([stripped], language='All')
name = ''
if len(profanity) > 0:
name = "Profanity Detected"
else:
if hate_speech == ['Hate Speech']:
name = 'Hate Speech Detected'
elif hate_speech == ['Offensive Speech']:
name = 'Offensive Speech Detected'
elif hate_speech == ['No Hate and Offensive Speech']:
name = stripped
names.append(name)
return pd.DataFrame(names, columns=['Names'])
elif type == "WOW":
max_seq_len = 16 # For skyrim = 13, for terraria = 12
sp = spm.SentencePieceProcessor()
sp.load("models/wow_names.model")
amount = int(amount)
max_length = int(max_length)
names = []
# Define necessary variables
vocab_size = sp.GetPieceSize()
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="models/dungen_wow_model.tflite")
interpreter.allocate_tensors()
# Use the function to generate a name
for _ in range(amount):
generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
stripped = generated_name.strip()
hate_speech = detect_hate_speech(stripped)
profanity = detect_profanity([stripped], language='All')
name = ''
if len(profanity) > 0:
name = "Profanity Detected"
else:
if hate_speech == ['Hate Speech']:
name = 'Hate Speech Detected'
elif hate_speech == ['Offensive Speech']:
name = 'Offensive Speech Detected'
elif hate_speech == ['No Hate and Offensive Speech']:
name = stripped
names.append(name)
return pd.DataFrame(names, columns=['Names'])
elif type == "Minecraft":
max_seq_len = 17 # For skyrim = 13, for terraria = 12
sp = spm.SentencePieceProcessor()
sp.load("models/minecraft_names.model")
amount = int(amount)
max_length = int(max_length)
names = []
# Define necessary variables
vocab_size = sp.GetPieceSize()
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="models/dungen_minecraft_model.tflite")
interpreter.allocate_tensors()
# Use the function to generate a name
for _ in range(amount):
generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
stripped = generated_name.strip()
hate_speech = detect_hate_speech(stripped)
profanity = detect_profanity([stripped], language='All')
name = ''
if len(profanity) > 0:
name = "Profanity Detected"
else:
if hate_speech == ['Hate Speech']:
name = 'Hate Speech Detected'
elif hate_speech == ['Offensive Speech']:
name = 'Offensive Speech Detected'
elif hate_speech == ['No Hate and Offensive Speech']:
name = stripped
names.append(name)
return pd.DataFrame(names, columns=['Names'])
elif type == "Dark Souls":
max_seq_len = 13 # For skyrim = 13, for terraria = 12
sp = spm.SentencePieceProcessor()
sp.load("models/dark_souls_names.model")
amount = int(amount)
max_length = int(max_length)
names = []
# Define necessary variables
vocab_size = sp.GetPieceSize()
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="models/dungen_dark_souls_model.tflite")
interpreter.allocate_tensors()
# Use the function to generate a name
for _ in range(amount):
generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
stripped = generated_name.strip()
hate_speech = detect_hate_speech(stripped)
profanity = detect_profanity([stripped], language='All')
name = ''
if len(profanity) > 0:
name = "Profanity Detected"
else:
if hate_speech == ['Hate Speech']:
name = 'Hate Speech Detected'
elif hate_speech == ['Offensive Speech']:
name = 'Offensive Speech Detected'
elif hate_speech == ['No Hate and Offensive Speech']:
name = stripped
names.append(name)
return pd.DataFrame(names, columns=['Names'])
elif type == "Final Fantasy":
max_seq_len = 14
sp = spm.SentencePieceProcessor()
sp.load("models/final_fantasy_names.model")
amount = int(amount)
max_length = int(max_length)
names = []
# Define necessary variables
vocab_size = sp.GetPieceSize()
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="models/dungen_final_fantasy_model.tflite")
interpreter.allocate_tensors()
# Use the function to generate a name
for _ in range(amount):
generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
stripped = generated_name.strip()
hate_speech = detect_hate_speech(stripped)
profanity = detect_profanity([stripped], language='All')
name = ''
if len(profanity) > 0:
name = "Profanity Detected"
else:
if hate_speech == ['Hate Speech']:
name = 'Hate Speech Detected'
elif hate_speech == ['Offensive Speech']:
name = 'Offensive Speech Detected'
elif hate_speech == ['No Hate and Offensive Speech']:
name = stripped
names.append(name)
return pd.DataFrame(names, columns=['Names'])
elif type == "Elden Ring":
max_seq_len = 18
sp = spm.SentencePieceProcessor()
sp.load("models/elden_ring_names.model")
amount = int(amount)
max_length = int(max_length)
names = []
# Define necessary variables
vocab_size = sp.GetPieceSize()
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="models/dungen_elden_ring_model.tflite")
interpreter.allocate_tensors()
# Use the function to generate a name
for _ in range(amount):
generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
stripped = generated_name.strip()
hate_speech = detect_hate_speech(stripped)
profanity = detect_profanity([stripped], language='All')
name = ''
if len(profanity) > 0:
name = "Profanity Detected"
else:
if hate_speech == ['Hate Speech']:
name = 'Hate Speech Detected'
elif hate_speech == ['Offensive Speech']:
name = 'Offensive Speech Detected'
elif hate_speech == ['No Hate and Offensive Speech']:
name = stripped
names.append(name)
return pd.DataFrame(names, columns=['Names'])
elif type == "Zelda":
max_seq_len = 15
sp = spm.SentencePieceProcessor()
sp.load("models/zelda_names.model")
amount = int(amount)
max_length = int(max_length)
names = []
# Define necessary variables
vocab_size = sp.GetPieceSize()
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="models/dungen_zelda_model.tflite")
interpreter.allocate_tensors()
# Use the function to generate a name
for _ in range(amount):
generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
stripped = generated_name.strip()
hate_speech = detect_hate_speech(stripped)
profanity = detect_profanity([stripped], language='All')
name = ''
if len(profanity) > 0:
name = "Profanity Detected"
else:
if hate_speech == ['Hate Speech']:
name = 'Hate Speech Detected'
elif hate_speech == ['Offensive Speech']:
name = 'Offensive Speech Detected'
elif hate_speech == ['No Hate and Offensive Speech']:
name = stripped
names.append(name)
return pd.DataFrame(names, columns=['Names'])
elif type == "Dragon Age":
max_seq_len = 16 # For skyrim = 13, for terraria = 12
sp = spm.SentencePieceProcessor()
sp.load("models/dragon_age_names.model")
amount = int(amount)
max_length = int(max_length)
names = []
# Define necessary variables
vocab_size = sp.GetPieceSize()
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="models/dungen_dragon_age_model.tflite")
interpreter.allocate_tensors()
# Use the function to generate a name
for _ in range(amount):
generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
stripped = generated_name.strip()
hate_speech = detect_hate_speech(stripped)
profanity = detect_profanity([stripped], language='All')
name = ''
if len(profanity) > 0:
name = "Profanity Detected"
else:
if hate_speech == ['Hate Speech']:
name = 'Hate Speech Detected'
elif hate_speech == ['Offensive Speech']:
name = 'Offensive Speech Detected'
elif hate_speech == ['No Hate and Offensive Speech']:
name = stripped
names.append(name)
return pd.DataFrame(names, columns=['Names'])
elif type == "Fallout":
max_seq_len = 13
sp = spm.SentencePieceProcessor()
sp.load("models/fallout_names.model")
amount = int(amount)
max_length = int(max_length)
names = []
# Define necessary variables
vocab_size = sp.GetPieceSize()
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="models/dungen_fallout_model.tflite")
interpreter.allocate_tensors()
# Use the function to generate a name
for _ in range(amount):
generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
stripped = generated_name.strip()
hate_speech = detect_hate_speech(stripped)
profanity = detect_profanity([stripped], language='All')
name = ''
if len(profanity) > 0:
name = "Profanity Detected"
else:
if hate_speech == ['Hate Speech']:
name = 'Hate Speech Detected'
elif hate_speech == ['Offensive Speech']:
name = 'Offensive Speech Detected'
elif hate_speech == ['No Hate and Offensive Speech']:
name = stripped
names.append(name)
return pd.DataFrame(names, columns=['Names'])
elif type == "Darkest Dungeon":
max_seq_len = 14
sp = spm.SentencePieceProcessor()
sp.load("models/darkest_dungeon_names.model")
amount = int(amount)
max_length = int(max_length)
names = []
# Define necessary variables
vocab_size = sp.GetPieceSize()
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="models/dungen_darkest_dungeon_model.tflite")
interpreter.allocate_tensors()
# Use the function to generate a name
for _ in range(amount):
generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
stripped = generated_name.strip()
hate_speech = detect_hate_speech(stripped)
profanity = detect_profanity([stripped], language='All')
name = ''
if len(profanity) > 0:
name = "Profanity Detected"
else:
if hate_speech == ['Hate Speech']:
name = 'Hate Speech Detected'
elif hate_speech == ['Offensive Speech']:
name = 'Offensive Speech Detected'
elif hate_speech == ['No Hate and Offensive Speech']:
name = stripped
names.append(name)
return pd.DataFrame(names, columns=['Names'])
elif type == "Monster Hunter":
max_seq_len = 15
sp = spm.SentencePieceProcessor()
sp.load("models/monster_hunter_names.model")
amount = int(amount)
max_length = int(max_length)
names = []
# Define necessary variables
vocab_size = sp.GetPieceSize()
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="models/dungen_monster_hunter_model.tflite")
interpreter.allocate_tensors()
# Use the function to generate a name
for _ in range(amount):
generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
stripped = generated_name.strip()
hate_speech = detect_hate_speech(stripped)
profanity = detect_profanity([stripped], language='All')
name = ''
if len(profanity) > 0:
name = "Profanity Detected"
else:
if hate_speech == ['Hate Speech']:
name = 'Hate Speech Detected'
elif hate_speech == ['Offensive Speech']:
name = 'Offensive Speech Detected'
elif hate_speech == ['No Hate and Offensive Speech']:
name = stripped
names.append(name)
return pd.DataFrame(names, columns=['Names'])
elif type == "Bloodborne":
max_seq_len = 12
sp = spm.SentencePieceProcessor()
sp.load("models/bloodborne_names.model")
amount = int(amount)
max_length = int(max_length)
names = []
# Define necessary variables
vocab_size = sp.GetPieceSize()
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="models/dungen_bloodborne_model.tflite")
interpreter.allocate_tensors()
# Use the function to generate a name
for _ in range(amount):
generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
stripped = generated_name.strip()
hate_speech = detect_hate_speech(stripped)
profanity = detect_profanity([stripped], language='All')
name = ''
if len(profanity) > 0:
name = "Profanity Detected"
else:
if hate_speech == ['Hate Speech']:
name = 'Hate Speech Detected'
elif hate_speech == ['Offensive Speech']:
name = 'Offensive Speech Detected'
elif hate_speech == ['No Hate and Offensive Speech']:
name = stripped
names.append(name)
return pd.DataFrame(names, columns=['Names'])
elif type == "Hollow Knight":
max_seq_len = 15
sp = spm.SentencePieceProcessor()
sp.load("models/hollow_knight_names.model")
amount = int(amount)
max_length = int(max_length)
names = []
# Define necessary variables
vocab_size = sp.GetPieceSize()
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="models/dungen_hollow_knight_model.tflite")
interpreter.allocate_tensors()
# Use the function to generate a name
for _ in range(amount):
generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
stripped = generated_name.strip()
hate_speech = detect_hate_speech(stripped)
profanity = detect_profanity([stripped], language='All')
name = ''
if len(profanity) > 0:
name = "Profanity Detected"
else:
if hate_speech == ['Hate Speech']:
name = 'Hate Speech Detected'
elif hate_speech == ['Offensive Speech']:
name = 'Offensive Speech Detected'
elif hate_speech == ['No Hate and Offensive Speech']:
name = stripped
names.append(name)
return pd.DataFrame(names, columns=['Names'])
elif type == "Assassin's Creed":
max_seq_len = 15
sp = spm.SentencePieceProcessor()
sp.load("models/assassins_creed_names.model")
amount = int(amount)
max_length = int(max_length)
names = []
# Define necessary variables
vocab_size = sp.GetPieceSize()
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="models/dungen_assassins_creed_model.tflite")
interpreter.allocate_tensors()
# Use the function to generate a name
for _ in range(amount):
generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
stripped = generated_name.strip()
hate_speech = detect_hate_speech(stripped)
profanity = detect_profanity([stripped], language='All')
name = ''
if len(profanity) > 0:
name = "Profanity Detected"
else:
if hate_speech == ['Hate Speech']:
name = 'Hate Speech Detected'
elif hate_speech == ['Offensive Speech']:
name = 'Offensive Speech Detected'
elif hate_speech == ['No Hate and Offensive Speech']:
name = stripped
names.append(name)
return pd.DataFrame(names, columns=['Names'])
elif type == "Baldur's Gate":
max_seq_len = 14
sp = spm.SentencePieceProcessor()
sp.load("models/baldurs_gate_names.model")
amount = int(amount)
max_length = int(max_length)
names = []
# Define necessary variables
vocab_size = sp.GetPieceSize()
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="models/dungen_baldurs_gate_model.tflite")
interpreter.allocate_tensors()
# Use the function to generate a name
for _ in range(amount):
generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
stripped = generated_name.strip()
hate_speech = detect_hate_speech(stripped)
profanity = detect_profanity([stripped], language='All')
name = ''
if len(profanity) > 0:
name = "Profanity Detected"
else:
if hate_speech == ['Hate Speech']:
name = 'Hate Speech Detected'
elif hate_speech == ['Offensive Speech']:
name = 'Offensive Speech Detected'
elif hate_speech == ['No Hate and Offensive Speech']:
name = stripped
names.append(name)
return pd.DataFrame(names, columns=['Names'])
elif type == "Fantasy":
max_seq_len = 16 # For fantasy, 16
sp = spm.SentencePieceProcessor()
sp.load("models/fantasy_names.model")
amount = int(amount)
max_length = int(max_length)
names = []
# Define necessary variables
vocab_size = sp.GetPieceSize()
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="models/dungen_fantasy_model.tflite")
interpreter.allocate_tensors()
# Use the function to generate a name
for _ in range(amount):
generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
stripped = generated_name.strip()
hate_speech = detect_hate_speech(stripped)
profanity = detect_profanity([stripped], language='All')
name = ''
if len(profanity) > 0:
name = "Profanity Detected"
else:
if hate_speech == ['Hate Speech']:
name = 'Hate Speech Detected'
elif hate_speech == ['Offensive Speech']:
name = 'Offensive Speech Detected'
elif hate_speech == ['No Hate and Offensive Speech']:
name = stripped
names.append(name)
return pd.DataFrame(names, columns=['Names'])
demo = gr.Interface(
fn=generateNames,
inputs=[gr.Radio(choices=["Terraria", "Skyrim", "Witcher", "WOW", "Minecraft", "Dark Souls", "Final Fantasy", "Elden Ring", "Zelda", "Dragon Age", "Fallout", "Darkest Dungeon", "Monster Hunter", "Bloodborne", "Hollow Knight", "Assassin's Creed", "Baldur's Gate", "Fantasy"], label="Choose a model for your request", value="Terraria"), gr.Slider(1,100, step=1, label='Amount of Names', info='How many names to generate, must be greater than 0'), gr.Slider(10, 60, value=30, step=1, label='Max Length', info='Max length of the generated word'), gr.Slider(0.1, 1, value=0.5, label='Temperature', info='Controls randomness of generation, higher values = more creative, lower values = more probalistic'), gr.Textbox('', label='Seed text (optional)', info='The starting text to begin with', max_lines=1, )],
outputs=[gr.Dataframe(row_count = (2, "dynamic"), col_count=(1, "fixed"), label="Generated Names", headers=["Names"])],
title='Dungen - Name Generator',
description='A fun game-inspired name generator. For an example of how to create, and train your model, like this one, head over to: https://github.com/Infinitode/OPEN-ARC/tree/main/Project-5-TWNG. There you will find our base model, the dataset we used, and implementation code in the form of a Jupyter Notebook (exported from Kaggle).'
)
demo.launch() |