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import gradio as gr
from transformers import AutoTokenizer
from transformers import GenerationConfig
from transformers import AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("t5-small")
headline = AutoModelForSeq2SeqLM.from_pretrained("wetey/content-summarizer")
generate_long = AutoModelForSeq2SeqLM.from_pretrained("wetey/content-generator")
def generate_headline(text):
inputs = tokenizer(text, return_tensors="pt").input_ids
generation_config = GenerationConfig(temperature = 1.2,
encoder_no_repeat_ngram_size = 4)
outputs = headline.generate(inputs,
do_sample = True,
generation_config = generation_config)
return tokenizer.decode(outputs[0], skip_special_tokens = True)
def generate_content(text):
inputs = tokenizer(text, return_tensors="pt").input_ids
generation_config = GenerationConfig(temperature = 1.2,
encoder_no_repeat_ngram_size = 2,
min_length = 50,
max_length = 512,
length_penalty = 1.5,
num_beams = 4,
repetition_penalty = 1.5,
no_repeat_ngram_size = 3)
outputs = generate_long.generate(inputs,
do_sample = True,
generation_config = generation_config)
return tokenizer.decode(outputs[0], skip_special_tokens = True)
textbox = gr.Textbox(label="Type your text here", lines=2)
demo = gr.Blocks()
with demo:
text_input = gr.Textbox()
text_output = gr.Textbox()
b1 = gr.Button("Generate headline")
b2 = gr.Button("Generate long content")
b1.click(generate_headline, inputs=text_input, outputs=text_output)
b2.click(generate_content, inputs=text_input, outputs=text_output)
demo.launch() |