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import gradio as gr | |
import torch | |
from transformers import AutoTokenizer, AutoModelForCausalLM | |
tokenizer = AutoTokenizer.from_pretrained("Armandoliv/gpt2-tweetml-generator") | |
model = AutoModelForCausalLM.from_pretrained("Armandoliv/gpt2-tweetml-generator") | |
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
model = model.to(device) | |
def main_generator(text): | |
preprocess_text = text.strip().replace("\n"," ").strip() | |
prompt = f"<|startoftext|> {preprocess_text}" | |
generated = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0) | |
generated = generated.to(device) | |
sample_outputs = model.generate( | |
generated, | |
do_sample=True, | |
top_k=20, | |
max_length = 70, | |
top_p=0.98, | |
num_return_sequences=10, | |
temperature=0.95 | |
) | |
output = "" | |
for i, sample_output in enumerate(sample_outputs): | |
output += "{}: {}\n\n".format(i+1, tokenizer.decode(sample_output, skip_special_tokens=True)) | |
return output | |
inputs = [gr.Textbox(lines=1, placeholder="Text Here...", label="Input")] | |
outputs = gr.Text( label="10 Tweets Generated") | |
title="Tweets generation app" | |
description = "This demo uses AI Models to create tweets.\nIt focus on Data Science and Machine Learning tweets creation." | |
examples = ['I wonder'] | |
io = gr.Interface(fn=main_generator, inputs=inputs, outputs=outputs, title=title, description = description, examples = examples, | |
css= """.gr-button-primary { background: -webkit-linear-gradient( | |
90deg, #355764 0%, #55a8a1 100% ) !important; background: #355764; | |
background: linear-gradient( | |
90deg, #355764 0%, #55a8a1 100% ) !important; | |
background: -moz-linear-gradient( 90deg, #355764 0%, #55a8a1 100% ) !important; | |
background: -webkit-linear-gradient( | |
90deg, #355764 0%, #55a8a1 100% ) !important; | |
color:white !important}""" | |
) | |
io.launch() |