Spaces:
Sleeping
Sleeping
#### Import Dependencies #### | |
import gradio as gr | |
import transformers | |
from transformers import pipeline | |
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer | |
import torch | |
#### Model 1 #### | |
#model_name = "snrspeaks/t5-one-line-summary" | |
#model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
#tokenizer = AutoTokenizer.from_pretrained(model_name) | |
#### Model 2 #### | |
summarizer = pipeline( | |
"summarization", | |
"pszemraj/long-t5-tglobal-base-16384-book-summary", | |
device=0 if torch.cuda.is_available() else -1, | |
) | |
params = { | |
"max_length": 256, | |
"min_length": 8, | |
"no_repeat_ngram_size": 3, | |
"early_stopping": True, | |
"repetition_penalty": 3.5, | |
"length_penalty": 0.3, | |
"encoder_no_repeat_ngram_size": 3, | |
"num_beams": 4, | |
} # parameters for text generation out of model | |
#### Run the model 1#### | |
def summarize(text): | |
#input_ids = tokenizer.encode("summarize: " + text, return_tensors="pt", add_special_tokens=True) | |
#generated_id = model.generate(input_ids=input_ids,num_beams=5,max_length=50,repetition_penalty=2.5,length_penalty=1,early_stopping=True,num_return_sequences=1) | |
#pred = tokenizer.decode(generated_id[0], skip_special_tokens=True, clean_up_tokenization_spaces=True) | |
result = summarizer(text, **params) | |
pred2 = result[0]['summary_text'] | |
#output = pred + "\n\n" + pred2 | |
return pred2 | |
#### Display summarized text #### | |
with gr.Blocks() as demo: | |
text = gr.Textbox(label="Text", lines=10, placeholder="Enter text here") | |
t1 = gr.Textbox(label="Output") | |
btn = gr.Button("Summarise") | |
btn.click(fn=summarize, inputs=text, outputs=t1) | |
demo.launch() |