Update README.md

README.md

@@ -15,7 +15,9 @@ language:
 library_name: transformers
 ---
 import torch
-from transformers import BartForConditionalGeneration, BartTokenizer
+from transformers import BartForConditionalGeneration, BartTokenizer, GPT2LMHeadModel, GPT2Tokenizer
+import argparse
+import sys
 
 class AdvancedSummarizer:
     def __init__(self, model_name="facebook/bart-large-cnn"):
@@ -38,30 +40,14 @@ class AdvancedSummarizer:
         summary = self.tokenizer.decode(summary_ids[0], skip_special_tokens=True)
         return summary
 
-def main():
+def main_summarizer():
     # Example usage
     summarizer = AdvancedSummarizer()
-
-    text = """
-    Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to natural intelligence displayed by animals including humans. AI research has been defined as the field of study of intelligent agents, which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals.
-
-    The term "artificial intelligence" had previously been used to describe machines that mimic and display "human" cognitive skills that are associated with the human mind, such as "learning" and "problem-solving". This definition has since been rejected by major AI researchers who now describe AI in terms of rationality and acting rationally, which does not limit how intelligence can be articulated.
-
-    AI applications include advanced web search engines, recommendation systems, understanding human speech, self-driving cars, automated decision-making and competing at the highest level in strategic game systems. As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology.
-    """
-
+    text = """..."""  # Your text here
     summary = summarizer.summarize(text)
     print("Summary:")
     print(summary)
 
-if __name__ == "__main__":
-    main()
-    ---
-    import torch
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-import argparse
-import sys
-
 class AdvancedTextGenerator:
     def __init__(self, model_name="gpt2-medium"):
         try:
@@ -78,7 +64,6 @@ class AdvancedTextGenerator:
         try:
             input_ids = self.tokenizer.encode(prompt, return_tensors="pt").to(self.device)
 
-            # Configure output parameters
             output_sequences = self.model.generate(
                 input_ids=input_ids,
                 max_length=max_length + len(input_ids[0]),
@@ -91,8 +76,7 @@ class AdvancedTextGenerator:
             )
 
             generated_sequences = []
-            for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
-                generated_sequence = generated_sequence.tolist()
+            for generated_sequence in output_sequences:
                 text = self.tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
                 total_sequence = text[len(self.tokenizer.decode(input_ids[0], clean_up_tokenization_spaces=True)):]
                 generated_sequences.append(total_sequence)
@@ -101,7 +85,7 @@ class AdvancedTextGenerator:
         except Exception as e:
             return [f"Error during text generation: {e}"]
 
-def main():
+def main_generator():
     parser = argparse.ArgumentParser(description="Advanced Text Generator")
     parser.add_argument("--prompt", type=str, help="Starting prompt for text generation")
     parser.add_argument("--max_length", type=int, default=100, help="Maximum length of generated text")
@@ -137,6 +121,5 @@ def main():
         print(text)
 
 if __name__ == "__main__":
-    main()
-    ---
-    
+    main_summarizer()  # Call the summarizer main function
+    main_generator()   # Call the text generator main function