Update app.py

app.py

@@ -6,7 +6,31 @@ import numpy as np
 import pandas as pd
 import tensorflow as tf
 
-def generate_random_name(interpreter, vocab_size, sp, max_length=10, temperature=0.5, seed_text=""):
+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()
@@ -83,11 +107,11 @@ def generateNames(type, amount, max_length=30, temperature=0.5, seed_text=""):
         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)
+            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)
             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()
@@ -105,9 +129,8 @@ def generateNames(type, amount, max_length=30, temperature=0.5, seed_text=""):
         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)
+            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)
             names.append(generated_name)
         return pd.DataFrame(names, columns=['Names'])
 
@@ -119,30 +142,4 @@ demo = gr.Interface(
     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.
-
-    :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)
-    """
-    maxlen = max_seq_len
-    
-    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
+demo.launch()