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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() |