Update app.py

app.py

@@ -6,6 +6,62 @@ import numpy as np
 import pandas as pd
 import tensorflow as tf
 
+def generateNames(type, amount, max_length=30, temperature=0.5, seed_text=""):
+    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
+        # 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 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
+        # 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 pd.DataFrame(names, columns=['Names'])
+
+demo = gr.Interface(
+    fn=generateNames,
+    inputs=[gr.Radio(choices=["Terraria", "Skyrim"], label="Choose a model for your request"), gr.Slider(1,25, 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, similar to 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()
+
 def custom_pad_sequences(sequences, maxlen, padding='pre', value=0):
     """
     Pads sequences to the same length.
@@ -89,60 +145,4 @@ def generate_random_name(interpreter, vocab_size, sp, max_length=10, temperature
     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=""):
-    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
-        # 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 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
-        # 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 pd.DataFrame(names, columns=['Names'])
-
-demo = gr.Interface(
-    fn=generateNames,
-    inputs=[gr.Radio(choices=["Terraria", "Skyrim"], label="Choose a model for your request"), gr.Slider(1,25, 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, similar to 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()
+    return generated_name.strip()