app.py

import os
import random
import gradio as gr
import sentencepiece as spm
import numpy as np
import tensorflow as tf

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=""):
    # Get input and output tensors
    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()

    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)
        # 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>", "")
    generated_name = decoded_name.rsplit(' ', 1)[0]
    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 generateTerrariaNames(amount, max_length=30, temperature=0.5, seed_text=""):
    sp = spm.SentencePieceProcessor()
    sp.load("models/terraria_names.model")
    amount = int(amount)

    names = []

    # Define necessary variables
    vocab_size = sp.GetPieceSize()
    max_seq_len = 12 # For skyrim = 13, for terraria = 12

    # Load TFLite model
    interpreter = tf.lite.Interpreter(model_path="models/dungen_terraria_model.tflite")
    interpreter.allocate_tensors()

    # Use the function to generate a name
    # Assuming `vocab_size` and `sp` (SentencePiece processor) are defined elsewhere
    for _ in range(amount):
        generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature)
        names.append(generated_name)
    return names

demo = gr.Interface(
    fn=generateTerrariaNames,
    inputs=[gr.Number(1,25), gr.Slider(10, 60), gr.Slider(0.01, 1), gr.Text(0,10)],
    outputs=["text"],
)

demo.launch()