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#### 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() |