Update app.py

app.py

@@ -1,68 +1,3 @@
-# import torch
-# from transformers import AutoTokenizer, AutoModelForCausalLM
-# import gradio as gr
-
-# # Load pre-trained GPT-2 model and tokenizer
-# model_name = "gpt2-large"
-# tokenizer = AutoTokenizer.from_pretrained(model_name)
-# model = AutoModelForCausalLM.from_pretrained(model_name)
-
-
-# def generate_text(input_text, max_length=32, num_beams=5, do_sample=False, no_repeat_ngram_size=2):
-#     """
-#     Generate text based on the given input text.
-#     Parameters:
-#     - input_text (str): The input text to start generation from.
-#     - max_length (int): Maximum length of the generated text.
-#     - num_beams (int): Number of beams for beam search.
-#     - do_sample (bool): Whether to use sampling or not.
-#     - no_repeat_ngram_size (int): Size of the n-gram to avoid repetition.
-#     Returns:
-#     - generated_text (str): The generated text.
-#     """
-#     # Encode the input text and move it to the appropriate device
-#     input_ids = tokenizer(input_text, return_tensors='pt')['input_ids']
-#     # Generate text using the model
-#     output = model.generate(input_ids, max_length=max_length, num_beams=num_beams,
-#                             do_sample=do_sample, no_repeat_ngram_size=no_repeat_ngram_size)
-#     # Decode the generated output
-#     generated_text = tokenizer.decode(output[0])
-#     return generated_text
-
-
-# # def generate_text_with_nucleus_search(input_text, max_length=16, do_sample=True, top_p=0.9):
-# #     """
-# #     Generate text with nucleus sampling based on the given input text.
-# #     Parameters:
-# #     - input_text (str): The input text to start generation from.
-# #     - max_length (int): Maximum length of the generated text.
-# #     - do_sample (bool): Whether to use sampling or not.
-# #     - top_p (float): Nucleus sampling parameter.
-# #     Returns:
-# #     - generated_text (str): The generated text.
-# #     """
-# #     # Encode the input text and move it to the appropriate device
-# #     input_ids = tokenizer(input_text, return_tensors='pt')['input_ids']
-# #     # Generate text using nucleus sampling
-# #     output = model.generate(input_ids, max_length=max_length, do_sample=do_sample, top_p=top_p)
-# #     # Decode the generated output
-# #     generated_text = tokenizer.decode(output[0])
-# #     return generated_text
-
-
-# # Create Gradio interface
-# input_text = gr.Textbox(lines=10, label="Input Text", placeholder="Enter text for text generation...")
-# output_text = gr.Textbox(label="Generated Text")
-
-
-
-# gr.Interface(generate_text, input_text, output_text,
-#              title="Text Generation with GPT-2",
-#              description="Generate text using the GPT-2 model.",
-#              theme="default", 
-#              allow_flagging="never").launch(share=True)
-
-
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import gradio as gr