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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 = "robzchhangte/20k-MizGPT2-data4-cleaned"
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()
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