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Text-Generation-GPT-2-Model / app.py

ajeetkumar01

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	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)
	# Set pad token to eos token
	tokenizer.pad_token = tokenizer.eos_token
	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', padding=True)['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], skip_special_tokens=True)
	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()