Update phishing_email_detection_gpt2.py

Syntax error ...

Author: david-thrower

phishing_email_detection_gpt2.py

@@ -655,76 +655,14 @@ def reset_state(self):
 print("GENERATED TEXT SAMPLES")
 print("="*50)
 
-# Get pad token id
-pad_token_id = tokenizer.pad_token_id
-end_prompt_token_id = tokenizer.encode("</prompt>", add_special_tokens=False)[0]
-
-# # Generate text for first 5 test samples (Working)
-# generated_texts = []
-# for i in range(min(5, len(x_test_packaged[0]))):
-#     original_input = x_test_packaged[0][i].numpy()
-    
-#     # Find the end of the prompt
-#     try:
-#         end_prompt_index = list(original_input).index(end_prompt_token_id)
-#     except ValueError:
-#         end_prompt_index = 0
-    
-#     # Extract the prompt part
-#     prompt_tokens = original_input[:end_prompt_index+1].tolist()
-    
-#     # Generate tokens sequentially
-#     generated_tokens = []
-#     current_input = prompt_tokens.copy()
-    
-#     # Generate up to 100 tokens or until pad token
-#     for _ in range(100):
-#         # Pad or truncate to MAX_SEQ_LENGTH
-#         input_tensor = tf.constant([current_input + [pad_token_id] * (MAX_SEQ_LENGTH - len(current_input))], dtype=tf.int32)
-        
-#         # Get prediction
-#         prediction = reconstituted_model(input_tensor)
-#         next_token_id = int(tf.argmax(prediction[0], axis=-1).numpy())
-        
-#         # Stop if pad token generated
-#         if next_token_id == pad_token_id:
-#             break
-            
-#         generated_tokens.append(next_token_id)
-#         current_input.append(next_token_id)
-        
-#         # Stop if we exceed max length
-#         if len(current_input) >= MAX_SEQ_LENGTH:
-#             break
-    
-#     generated_texts.append((prompt_tokens, generated_tokens))
-
-# # Decode and print with proper formatting
-# for idx, (prompt_tokens, generated_tokens) in enumerate(generated_texts):
-#     # Decode prompt
-#     prompt_text = tokenizer.decode(prompt_tokens, skip_special_tokens=False)
-    
-#     # Extract original prompt content
-#     if '<prompt>' in prompt_text and '</prompt>' in prompt_text:
-#         original_prompt = prompt_text.split('<prompt>')[-1].split('</prompt>')[0]
-#     else:
-#         original_prompt = prompt_text[:50] + "..." if len(prompt_text) > 50 else prompt_text
-    
-#     # Decode generated text
-#     generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=False) if generated_tokens else ""
-    
-#     print(f"\nGenerated text from sample {idx+1}:")
-#     print(f"<prompt>{original_prompt}</prompt>{generated_text}")
-
-
 
 ## Proper model wrapper and generation method (under development):
 
 print("###### Output of the model wrapper (under development) ########### ")
 
 # Register the config and model wrapper as serializable
 @tf.keras.utils.register_keras_serializable()
-class CerebrosAutoregressiveTextGeneratorConfig:
+class CerebrosNotGPTConfig:
     def __init__(self, max_sequence_length=1536, padding_token=None):
         self.max_sequence_length = max_sequence_length
         self.padding_token = padding_token
@@ -740,7 +678,7 @@ def from_config(cls, config):
         return cls(**config)
 
 @tf.keras.utils.register_keras_serializable()
-class CerebrosAutoregressiveTextGenerator(tf.keras.Model):
+class CerebrosNotGPT(tf.keras.Model):
     def __init__(self, config, **kwargs):
         super().__init__(**kwargs)
         self.config = config
@@ -846,17 +784,17 @@ def call(self, inputs):
 print("="*50)
 
 # Create config and generator
-config = CerebrosAutoregressiveTextGeneratorConfig(
+config = CerebrosNotGPTConfig(
     max_sequence_length=MAX_SEQ_LENGTH,
     padding_token=tokenizer.pad_token_id
 )
-generator = CerebrosAutoregressiveTextGenerator(config)
+generator = CerebrosNotGPT(config)
 
 print("########### BEFORE SEARIALIZING THE GENERATIVE MODEL")
 
 def complete_text(text):
     input_ids = tokenizer(
-        half_sample,
+        text,
         add_special_tokens=False
     )['input_ids']