Update app.py

app.py

@@ -6,6 +6,65 @@ 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=""):
+    # 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 generateNames(type, amount, max_length=30, temperature=0.5, seed_text=""):
     if type == "Terraria":
         max_seq_len = 12 # For skyrim = 13, for terraria = 12
@@ -86,63 +145,4 @@ def custom_pad_sequences(sequences, maxlen, padding='pre', value=0):
                 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()
+    return padded_sequences