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README.md

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----
-title: Dungen
-emoji: 🚀
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 4.41.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Dungen
+emoji: 🚀
+colorFrom: indigo
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.41.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# Dungen
+
+Name generation models, specifically trained to generate names based on games. The 2 models available can generate names based on names of characters (NPCs), places, and items in the official games of:
+- Terraria
+- The Elder Scrolls V: Skyrim
+
+## Training your own models
+
+You can train your own version of these models, using the project code from our GitHub repo link below:
+
+https://github.com/Infinitode/OPEN-ARC/tree/main/Project-5-TWNG

app.py

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+import os
+import random
+import gradio as gr
+import sentencepiece as spm
+import numpy as np
+import tflite_runtime.interpreter as tflite
+
+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 = tflite.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
+
+def gradio_interface(amount, max_length, temperature, seed_text):
+    names = generateTerrariaNames(amount, max_length, temperature, seed_text)
+    return names
+
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=[
+        gr.inputs.Number(default=1, maximum=25, label="Amount"),
+        gr.inputs.Slider(minimum=1, maximum=100, default=30, label="Max Length"),
+        gr.inputs.Slider(minimum=0.1, maximum=1.0, default=0.5, label="Temperature"),
+        gr.inputs.Textbox(default="", label="Seed Text (optional)")
+    ],
+    outputs="text",
+    title="Terraria Name Generator",
+    description="Generate random Terraria names using a TFLite model."
+)
+
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+gradio
+tflite_runtime
+numpy
+sentencepiece