Update app.py

app.py

@@ -24,11 +24,10 @@ def infer(input_ids, max_length, temperature, top_k, top_p):
     )
 
     return output_sequences
-default_value = "See how a modern neural network auto-completes your text 🤗 This site, built by the Hugging Face team, lets you write a whole document directly from your browser, and you can trigger the Transformer anywhere using the Tab key. Its like having a smart machine that completes your thoughts 😀 Get started by typing a custom snippet, check out the repository, or try one of the examples. Have fun!"
+default_value = "Once upon a time, in a galaxy far, far away...."
 
 #prompts
-st.title("Write with Transformers 🦄")
-st.write("The almighty king of text generation, GPT-2 comes in four available sizes, only three of which have been publicly made available. Feared for its fake news generation capabilities, it currently stands as the most syntactically coherent model. A direct successor to the original GPT, it reinforces the already established pre-training/fine-tuning killer duo. From the paper: Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.")
+st.title("Text completion with GPT-2")
 
 sent = st.text_area("Text", default_value, height = 275)
 max_length = st.sidebar.slider("Max Length", min_value = 10, max_value=30)
@@ -42,29 +41,18 @@ if encoded_prompt.size()[-1] == 0:
 else:
     input_ids = encoded_prompt
 
-
 output_sequences = infer(input_ids, max_length, temperature, top_k, top_p)
 
-
-
 for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
     print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===")
     generated_sequences = generated_sequence.tolist()
 
-    # Decode text
     text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
 
-    # Remove all text after the stop token
-    #text = text[: text.find(args.stop_token) if args.stop_token else None]
-
-    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
     total_sequence = (
         sent + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]
     )
 
     generated_sequences.append(total_sequence)
-    print(total_sequence)
-
-
-st.write(generated_sequences[-1])
 
+st.write(generated_sequences[-1])