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import gradio as gr
from transformers import pipeline
title = "Silly Ted-Talk snippet generator"
description = "Tap on the \"Submit\" button to generate a random text snippet."
article = "<p>Fine tuned <a href=\"https://huggingface.co/EleutherAI/gpt-neo-125M\">EleutherAI/gpt-neo-125M</a> upon a formatted <a href=\"https://www.kaggle.com/datasets/miguelcorraljr/ted-ultimate-dataset\"> TED – Ultimate Dataset</a> (English)</p>"
model_id = "./model"
text_generator = pipeline('text-generation', model=model_id, tokenizer=model_id)
max_length = 128
top_k = 40
top_p = 0.92
temperature = 1.0
def text_generation(input_text = None):
if input_text == None or len(input_text) == 0:
input_text = "\t\""
else:
input_text.replace("\"", "")
if input_text.startswith("<|startoftext|>") == False:
input_text ="\t\"" + input_text
generated_text = text_generator(input_text,
max_length=max_length,
top_k=top_k,
top_p=top_p,
temperature=temperature,
do_sample=True,
repetition_penalty=2.0,
num_return_sequences=1)
parsed_text = generated_text[0]["generated_text"].replace("<|startoftext|>", "").replace("\r","").replace("\n\n", "\n").replace("\t", " ").replace("<|pad|>", " * ").replace("\"\"", "\"")
return parsed_text
gr.Interface(
text_generation,
[gr.inputs.Textbox(lines=1, label="Enter input text or leave blank")],
outputs=[gr.outputs.Textbox(type="text", label="Generated Ted-Talk snippet")],
title=title,
description=description,
article=article,
theme="default",
allow_flagging=False,
).launch() |