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from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import gradio as gr
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
def text_generation(input_text, seed):
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
torch.manual_seed(seed) # Max value: 18446744073709551615
outputs = model.generate(input_ids, do_sample=True, min_length=50, max_length=200)
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
return generated_text
title = "Text Generator Demo GPT-Neo"
description = "Text Generator Application by ecarbo"
gr.Interface(
text_generation,
[gr.inputs.Textbox(lines=2, label="Enter input text"), gr.inputs.Number(default=10, label="Enter seed number")],
[gr.outputs.Textbox(type="auto", label="Text Generated")],
title=title,
description=description,
theme="huggingface"
).launch()