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import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr
# Check if GPU is available, otherwise use CPU
# device = "cuda" if torch.cuda.is_available() else "cpu"
# 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=16, 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_text1 = gr.Textbox(label="Generated Text")
output_text2 = gr.Textbox(label="Generated Text with Nucleus Search")
# Set examples to None or empty list if not available
examples = [
["I am happy."],
["This is a good day."],
["It is raining outside."],
None # Example for output_text2
]
gr.Interface(generate_text, input_text, output_text1, output_text2,
title="Text Generation with GPT-2",
description="Generate text using the GPT-2 model.",
theme="default", # Change theme to default
allow_flagging="never",
examples=examples).launch(share=True)
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